Home > Resources > Articles > Here’s How Analytics are Transforming the Marketing Industry

Here’s How Analytics are Transforming the Marketing Industry

When it comes to marketing today, big data analytics has become a powerful being. The raw material marketers need to make sense of the information they are presented with so they can do their jobs with accuracy and excellence. Big data is what empowers marketers to understand their customers based on any online action they take.

Thanks to the boom of big data, marketers have learned more about new marketing trends and preferences, and behaviors of the consumer. For example, marketers know what their customers are streaming to what groceries they are ordering, thanks to big data.

Data is readily available in abundance due to digital technology. Data is created through mobile phones, social media, digital ads, weblogs, electronic devices, and sensors attached through the internet of things (IoT).

Data analytics helps organizations discover newer markets, learn how new customers interact with online ads, and draw conclusions and effects of new strategies. Newer sophisticated marketing analytics software and analytics tools are now being used to determine consumers’ buying patterns and key influencers in decision-making and validate data marketing approaches that yield the best results.

With the integration of product management with data science, real-time data capture, and analytics, big data analytics is helping companies increase sales and improve the customer experience.

In this article, we will examine how big data analytics are transforming the marketing industry.

Personalized Marketing

Personalized Marketing has taken an essential place in direct marketing to the consumers. Greeting consumers with their first name whenever they visit the website, sending them promotional emails of their favorite products, or notifying them with personalized recipes based on their grocery shopping are some of the examples of data-driven marketing.

When marketers collect critical data marketing pieces about customers at different marketing touchpoints such as their interests, their name, what they like to listen to, what they order most, what they’d like to hear about, and who they want to hear from, this enables marketers to plan their campaigns strategically.

Marketers aim for churn prevention and onboarding new customers. With customer’s marketing touchpoints, these insights can be used to improve acquisition rates, drive brand loyalty, increase revenue per customer, and improve the effectiveness of products and services.

With these data marketing touchpoints, marketers can build an ideal customer profile. Furthermore, these customer profiles can help them strategize and execute personalized campaigns accordingly.

Predictive Analytics

Customer behavior can be traced by historical data, which is the best way to predict how customers would behave in the future. It allows companies to correctly predict which customers are interested in their products at the right time and place. Predictive analytics applies data mining, statistical techniques, machine learning, and artificial intelligence for data analysis and predict the customer’s future behavior and activities.

Take an example of an online grocery store. If a customer tends to buy healthy and sugar-free snacks from the store now, they will keep buying it in the future too.

This predictable behavior from the customer makes it easy for brands to capitalize on that and has been made easy by analytics tools. They can automate their sales and target the said customer. What they would be doing gives the customer chances to make “repeat purchases” based on their predictive behavior. Marketers can also suggest customers purchase products related to those repeat purchases to get them on board with new products.

Customer Segmentation

Customer segmentation means dividing your customers into strata to identify a specific pattern. For example, customers from a particular city may buy your products more than others, or customers from a certain age demographic prefer some products more than other age demographics.

Specific marketing analytics software can help you segment your audience. For example, you can gather data like specific interests, how many times they have visited a place, unique preferences, and demographics such as age, gender, work, and home location.

These insights are a golden opportunity for marketers to create bold campaigns optimizing their return on investment. They can cluster customers into specific groups and target these segments with highly relevant data marketing campaigns.

The main goal of customer segmentation is to identify any interesting information that can help them increase revenue and meet their goals. Effective customer segmentation can help marketers with:

• Identifying most profitable and least profitable customers
• Building loyal relationships
• Predicting customer patterns
• Pricing products accordingly
• Developing products based on their interests

Businesses continue to invest in collecting high-quality data for perfect customer segmentation, which results in successful efforts.

Optimized Ad Campaigns

Customers’ social media data like Facebook, LinkedIn, and Twitter makes it easier for marketers to create customized ad campaigns on a larger scale. This means that they can create specific ad campaigns for particular groups and successfully execute an ad campaign.

Big data also makes it easier for marketers to run ‘remarketing’ campaigns. Remarketing campaigns ads follow your customers online, wherever they browse, once they have visited your website.

Execution of an online ad campaign makes all the difference in its success. Chasing customers with paid ads can work as an effective strategy if executed well. According to the rule 7, prospective customers need to be exposed to an ad minimum of seven times before they make any move on it.

When creating online ad campaigns, do keep one thing in mind. Your customers should not feel as if they are being stalked when you make any remarketing campaigns. Space out your ads and their exposure, so they appear naturally rather than coming on as pushy.

Consumer Impact

Advancements in data science have vastly impacted consumers. Every move they make online is saved and measured. In addition, websites now use cookies to store consumer data, so whenever these consumers visit these websites, product lists based on their shopping habits pop up on the site.

Search engines and social media data enhance this. This data can be used to analyze their behavior patterns and market to them accordingly.

The information gained from search engines and social media can be used to influence consumers into staying loyal and help their businesses benefit from the same.

These implications can be frightening, like seeing personalized ads crop up on their Facebook page or search engine. However, when consumer data is so openly available to marketers, they need to use it wisely and safeguard it from falling into the wrong hands.

Fortunately, businesses are taking note and making sure that this information remains secure.

Conclusion

The future of marketing because of big data and analytics seems bright and optimistic. Businesses are collecting high-quality data in real-time and analyzing it with the help of machine learning and AI; the marketing world seems to be up for massive changes. Analytics are transforming marketing industry to a different level. And with sophisticated marketers behind the wheel, the sky is the only limit.

Frequently Asked Questions

Why is marketing analytics so important these days?

Marketing analytics helps us see how everything plays off each other, and decide how we might want to invest moving forward. Re-prioritizing how you spend your time, how you build out your team, and the resources you invest in channels and efforts are critical steps to achieving marketing team success.

What is the use of marketing analytics?

Marketing analytics is used to measure how well your marketing efforts are performing and to determine what can be done differently to get better results across marketing channels.

Which companies use marketing analytics?

Marketing analytics enables you to improve your overall marketing program performance by identifying channel deficiencies, adjusting strategies and tactics as needed, optimizing processes, etc. Companies like Netflix, Sephora, EasyJet, and Spotify use marketing analytics to improve their markeitng performance as well.

Spotlight

Zementis

Zementis, Inc. is a leading software company focused on the operational deployment of predictive analytics and data mining solutions. Zementis was recognized by CIO Review as one of the "Top 20 most promising Big Data companies in 2013" and named "Cool Vendor in Data Science" by Gartner in 2014. Its ADAPA® and Universal PMML Plug-in scoring engines are designed from the ground up to benefit from open standards and to significantly shorten the time-to-market for predictive analytics in any industry…

OTHER ARTICLES
Performance Management

Rethinking and Recontextualizing Context(s) in Natural Language Processing

Article | May 30, 2023

We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum. Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake. Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions. Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation. Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization. A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction. Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors. Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt. The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation. Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.

Read More
Business Intelligence, Big Data Management, Big Data

Implementing Big Data and AI: Best Practices and Strategies for 2023

Article | July 18, 2023

Discover the latest strategies and best practices for implementing big data and AI into your organization for 2023. Gain insights on leading Big Data and AI solution providers to drive business growth. Contents 1 Establishing a Relationship between Big Data and AI 2 Importance of Big Data and AI in 2023 3 Key Challenges in Implementing Big Data and AI 4 Best Practices and Strategies for Big Data and AI Implementation 4.1 Building a Data Strategy 4.2 Implementing a Data Governance Framework 4.3 Leveraging Cloud Computing 4.4 Developing a Data Science and AI Roadmap 4.5 Leveraging Established Agile Methodologies 4.6 Prototyping Through Sandboxing 5 Top AI and Big Data Companies to Look For in 2023 6 Conclusion 1. Establishing a Relationship between Big Data and AI The relationship between AI and big data is mutually beneficial, as AI requires vast amounts of data to enhance its decision-making abilities, while big data analytics benefits from AI for superior analysis. This union enables the implementation of advanced analytics, such as predictive analysis, resulting in the optimization of business efficiency by anticipating emerging trends, scrutinizing consumer behavior, automating customer segmentation, customizing digital campaigns, and utilizing decision support systems propelled by big data, AI, and predictive analytics. This integration empowers organizations to become data-driven, resulting in significant improvements in business performance. 2. Importance of Big Data and AI in 2023 In the year 2023, it is anticipated that the utilization of big data analytics and artificial intelligence (AI) will profoundly impact diverse industries. The investment in big data analytics will be primarily driven by the need for data compliance, security, and mobilization, ultimately aiming to achieve real-time analysis. Therefore, businesses seeking to excel in this area must be prepared to adopt cloud technology and make significant advancements in computing power and data processing methods. Recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks. (Source: Forbes) The banking, retail, manufacturing, finance, healthcare, and government sectors have already made substantial investments in big data analytics, which have resulted in the forecasting of trends, enhancing business recommendations, and increasing profits. In addition, AI technology will make significant advancements in 2023, including democratization, making it accessible to a broader user population. This shift will enable customers to wield authority, and businesses will be able to use AI to better meet their specific and individualized business requirements. Finally, a significant shift likely to be witnessed in the AI field in 2023 is the move to a more industrialized, embedded type of architecture, where actual business users may begin utilizing algorithms. According to a recent study, 61% of respondents believe that AI will have a significant impact on their industry within the next three to five years. (Source: Deloitte Insights Report) 3. Key Challenges in Implementing Big Data and AI 97.2% of business executives say their organizations are investing in big data and AI projects. These executives cite their desire to become “nimble, data-driven businesses” as the reason for these investments, as 54.4% say that their companies’ inability to do this was the biggest threat they faced. In addition, 79.4% say they’re afraid that other, more data-driven companies will disrupt and outperform them. (Source: Zippia) Implementing big data analytics and artificial intelligence (AI) presents various challenges that businesses must tackle to realize their full potential. One such obstacle is the intricate nature of the data, which could be either structured or unstructured and necessitate specialized tools and techniques for processing and analysis. Moreover, companies must ensure data quality, completeness, and integrity to facilitate accurate analysis and decision-making. Another substantial challenge in implementing big data and AI is the requirement for skilled personnel with expertise in data science, machine learning, and related technologies. To stay up-to-date on the latest tools and techniques, companies must invest in ongoing training and development programs for their employees. Ethical and legal concerns surrounding data privacy, security, and transparency must also be addressed, especially after recent data breaches and privacy scandals. Integrating big data and AI into existing IT systems can be a challenging and time-consuming process that necessitates careful planning and coordination to ensure smooth integration and minimize disruption. Lastly, the high cost of implementing these technologies can be a significant barrier, especially for smaller businesses or those with limited IT budgets. To overcome these challenges, companies must be strategic, prioritize use cases, and develop a clear implementation roadmap while leveraging third-party tools and services to minimize costs and maximize ROI. 4. Best Practices and Strategies for Big Data and AI Implementation 24% of companies use big data analytics. While 97.2% of companies say they’re investing in big data and AI projects, just 24% describe their organizations as data-driven. (Source: Zippia) 4.1 Building a Data Strategy One of the biggest challenges in building a data strategy is identifying the most relevant data sources and data types for the organization’s specific business objectives. The sheer volume and diversity of data available can further complicate this. The key to addressing this challenge is thoroughly assessing the organization’s data assets and prioritizing them based on their business value. This involves: Identifying the key business objectives and Determining which data sources and data types are most relevant to achieving those objectives 4.2 Implementing a Data Governance Framework Establishing a data governance framework involving all stakeholders is crucial for ensuring agreement on data quality, privacy, and security standards. However, implementing such a framework can be daunting due to the divergent priorities and perspectives of stakeholders on good data governance. So, to overcome this challenge, clear guidelines and processes must be established: Creating a data governance council Defining roles and responsibilities Involving all stakeholders in the development and implementation of guidelines Data quality management, privacy, and security processes should be established to maintain high data governance standards Organizations can improve the effectiveness of their data governance initiatives by aligning all stakeholders and ensuring their commitment to maintaining optimal data governance standards. 4.3 Leveraging Cloud Computing It is essential to carefully select a cloud provider that aligns with the organization's security and compliance requirements. In addition, robust data security and compliance controls should be implemented: Establishing data encryption and access controls Implementing data backup and recovery procedures Regularly conducting security and compliance audits By following these practices, organizations can ensure their big data and AI projects are secure and compliant. 4.4 Developing a Data Science and AI Roadmap The obstacles to developing a data science and AI roadmap lie in identifying the most pertinent use cases that cater to the specific business objectives of an organization. This difficulty is further compounded by the potential divergence of priorities and perspectives among various stakeholders concerning the definition of a successful use case. Hence, it is imperative to establish unambiguous guidelines for identifying and prioritizing use cases that align with their respective business values. This entails: Identifying the key business objectives Carefully ascertaining which use cases are most pertinent to realizing those objectives Meticulously delineating the success criteria for each use case 4.5 Leveraging Established Agile Methodologies Leveraging well-established agile methodologies is critical in successfully implementing large-scale big data and AI projects. By defining a precise project scope and goals, prioritizing tasks, and fostering consistent communication and collaboration, enterprises can effectively execute AI and big data analytics initiatives leveraging agile methodologies. Such an approach provides teams with a clear understanding of their responsibilities, facilitates seamless communication, and promotes continuous improvement throughout the project lifecycle, resulting in a more efficient and effective implementation. 4.6 Prototyping Through Sandboxing Establishing clear guidelines and processes is crucial to overcome the challenge of creating prototypes through sandboxing that are representative of the production environment and can meet the organization's requirements. It includes: Defining the scope and objectives of the prototype, Meticulously selecting the appropriate tools and technologies Guaranteeing that the prototype is an authentic reflection of the production environment Additionally, conducting thorough testing and evaluation is necessary to ensure that the prototype can be scaled effectively to meet the organization's needs. 5. Top AI and Big Data Companies to Look For in 2023 H2O.ai H2O.ai is a leading provider of artificial intelligence (AI) and machine learning (ML) software. It provides a platform for businesses to use artificial intelligence and data-driven insights to drive innovation and growth. The software offers a suite of tools and algorithms to help users build predictive models, analyze data, and gain insights that inform business decisions. With a user-friendly interface and a robust set of features, H2O.ai is a valuable tool for businesses looking to leverage the power of machine learning to stay ahead of the competition. ThoughtSpot ThoughtSpot is a leading search and AI-driven analytics platform that enables businesses to quickly and easily analyze complex data sets. The platform offers a range of features, including advanced analytics, customizable visualizations, and collaborative capabilities. It is designed to make data analytics accessible to anyone within an organization, regardless of technical expertise. The platform is also highly customizable, allowing businesses to tailor it to meet their specific needs and integrate it with their existing data infrastructure. Treasure Data Treasure Data is a cloud-based enterprise data management platform that helps businesses collect, store, and analyze their data to gain valuable insights. Its platform includes a suite of powerful tools for data collection, storage, processing, and analysis, including a flexible data pipeline, a powerful data management console, and a range of analytics tools. The platform is also highly scalable, capable of handling massive amounts of data and processing millions of events per second, making it suitable for businesses of all sizes and industries. Denodo Denodo is a leading data virtualization software company that provides a unified platform for integrating and delivering data across multiple sources and formats in real time. The platform offers unmatched performance and unified access to a broad range of enterprise, big data, cloud, and unstructured sources. It also provides agile data service provisioning and governance at less than half the cost of traditional data integration. In addition, its data virtualization technology simplifies the complexity of data sources and creates a virtual layer of data services accessible to any application or user, regardless of the data’s location or format. Pendo.io Pendo.io is a leading cloud-based platform that provides product analytics, user feedback, and guidance for digital products. It allows businesses to make data-driven decisions about their products and optimize their customer journey. The platform empowers companies to transform product intelligence into actionable insights rapidly and at scale, enabling a new generation of businesses that prioritize product development. TigerGraph TigerGraph is a graph database and analytics platform that allows businesses to gain deeper insights and make better decisions by analyzing connected data. It is designed to handle complex data sets and perform advanced graph analytics at scale. The platform offers a range of graph analytics algorithms that can be applied to a variety of use cases, including fraud detection, recommendation engines, supply chain optimization, and social network analysis. Solix Technologies, Inc. Solix Technologies, Inc. is a leading big data management and analysis software solution provider that empowers data-driven enterprises to achieve their Information Lifecycle Management (ILM) goals. Its flagship product, Solix Big Data Suite, provides an ILM framework for Enterprise Archiving and Enterprise Data Lake applications utilizing Apache Hadoop as an enterprise data repository. In addition, the Solix Enterprise Data Management Suite (Solix EDMS) helps organizations implement database archiving, test data management, data masking and application retirement across all enterprise data. Reltio Reltio is a leading provider of cloud-based master data management (MDM) solutions that enable organizations to create a unified view of their data across all sources and formats. The platform combines MDM with big data analytics and machine learning to provide a single source of truth for data-driven decision-making. The solution offers a range of features, including data modeling, data quality management, data governance, and data analytics. dbt Labs dbt Labs is a cloud-based data transformation software platform that helps analysts and engineers manage the entire analytics engineering workflow, from data ingestion to analysis. The platform enables users to transform and model raw data into analysis-ready data sets using a SQL-based language. With its modular and scalable approach, dbt Labs makes it easier for data teams to collaborate and manage their data pipelines. Rockset Rockset is a real-time indexing database platform that allows businesses to run fast queries on data from multiple sources without needing to manage the underlying infrastructure. It supports various data types, including structured, semi-structured, and nested data, making it flexible and versatile. In addition, the serverless platform is built on a cloud-native architecture, making it easy to scale up or down as needed. With Rockset, users can build real-time applications and dashboards, perform ad hoc analysis, and create data-driven workflows. 6. Conclusion The relationship between big data and AI is mutually beneficial, given the fact that AI requires copious amounts of data to refine its decision-making capabilities, while big data analytics derives immense value from AI for advanced analysis. As a result, the integration of big data analytics and AI is projected to profoundly impact diverse industries in 2023. Nevertheless, adopting these technologies poses multifarious challenges, necessitating businesses to adopt a strategic approach and develop a comprehensive implementation roadmap to optimize ROI and minimize expenses. Ultimately, the successful implementation of big data and AI strategies can enable organizations to become data-driven, culminating in substantial improvements in business performance.

Read More
Business Intelligence, Big Data Management, Big Data

A Complete Guide to Creating a Successful Business Intelligence (BI) Strategy

Article | May 15, 2023

In today's environment, running a business can be quite challenging. These ever-changing and dynamic obstacles can make you feel overwhelmed. Maintenance of operations is a time-consuming process that leaves little time for working on the insights needed to gain a competitive advantage. However, organizations of all sizes, particularly SMEs, require accurate and actionable data perspectives. The role of a business intelligence (BI) strategy is to make this data available, which necessitates a deliberate plan. The central goal of a business intelligence strategy is to use software and services to transform important data into actionable knowledge. This is very important as business intelligence revenue in software was projected to reach $23,258.94 million in 2021. BI tools give users access to analytical data, which includes reports, dashboards, maps, charts, and various other visual representations. Users can get detailed information regarding the state of the company. “BI is about providing the right data at the right time to the right people so that they can make the right decisions.” Nic Smith, Microsoft BI Solutions Marketing Business intelligence strategy includes: Performance management Predictive modeling Analytics Data mining Why Should Businesses Implement BI? A business intelligence strategy will allow you to address your data problems, such as clarity, scarcity, insights out of data, and requirements, create a unified system, and sustain it. You should consider implementing a BI strategy if your business faces the following issues: You generate a lot of data but don't know what to do with it Overstocking or understocking Wasted resources and time Loss of customers Underperforming employees Data-driven decisions can benefit your business by: Discovering problems and their solutions Analyzing competitors’ data Analyzing customer behavior Planning approach to increase profit Foreseeing trends Optimizing operations Tracking performance Tips to Create a Successful Business Intelligence Strategy Business intelligence tools and capabilities are designed to create quick and easy-to-understand portrayals of an organization's current state. Developing a strategy to deal with all of these tools and skills is an essential part of reaping the benefits of business intelligence. If you want to learn how to build a strong business intelligence strategy, keep reading. Understand and Assess the Present Status The first step in implementing a business intelligence strategy is to put together a team that is capable of analyzing and presenting the current state of the company's data. With a dedicated team in place, evaluating an organization's current situation entails thinking about the data collected and the technology used to manage it. Understanding the organization's structures and processes for mining and interpreting data is also critical. At this level, a BI team will seek to assess which data is the most valuable and which is irrelevant to the current operations. Have a Vision with a Purpose and Direction A vision is a combination of direction and purpose. Without a vision, there is no strategy. Instead, it presents itself in various critical decisions, such as where we collect our data and who will access the insights. The following should be explained in the vision statement: Who will be in charge of the business intelligence processes? What is the state of your BI strategy concerning the business and IT strategies? How will it provide help and solutions? What solutions do you want to deploy, and where do you propose them? What kind of infrastructure do you want to provide? Prioritize Initiative by Developing a BI Road Map The BI roadmap should provide deliverables at various execution levels and a timetable. On the roadmap, you should have all of the data you wish to organize and arrange, as well as the dates and deliverables for each activity. Define the Way How the Data Is Going to Be Shared Another thing to do before establishing a business intelligence strategy is to define the terms and meaning of BI with all of your stakeholders. Because many employees are involved in data processing, make sure that everyone is on the same page and understands the business intelligence development strategy. At this stage, you should answer all the possible questions from your stakeholders, and the way and process data will be shared with all of them. Must-have BI Strategy Documentation A BI strategy document's logic is that it will serve as a point of reference for the entire organization and will be used to communicate the strategy. The following sections should be in the document: Executive summary BI strategy alignment with corporate strategy Project scope and requirements BI governance team Alternatives Assessment Appendices Make Regular Reviews to Assess the Progress A review process is necessary for any effective business intelligence strategy. These review methods should evaluate lessons gained while also documenting and determining the value of the data to the company. A review process may consider the user's experience and the possibility of changing the business's KPIs year after year. In addition, it helps to understand the progress of the strategy and the benefits it has brought to the company. Summing Up Any business's growth requires a BI strategy as it gives you a competitive advantage. You need a solid strategy, planning, and analysis to enjoy the rewards. You can drown yourself in useless analytics if you don't have a structured roadmap in place. Therefore, staying on track and assessing your methods regularly are critical to reaping the benefits of a BI strategy. The abovementioned steps serve you as stepping stones in developing a successful BI strategy. Frequently Asked Questions What is Business intelligence? Business intelligence is how businesses use methods and technology to analyze both current and historical data. This is done to improve strategic decision-making and gain a competitive advantage. Which are some of the BI tools? Data mining, predictive modeling, and contextual dashboards or KPIs are the most popular and widely used BI tools. Which are some of the major benefits of business intelligence? The benefits of BI are speedy analysis, intuitive dashboards, data-driven business decisions, improved employee satisfaction, increased organizational efficiency, and many more.

Read More
Big Data Management

How Companies Are Using Big Data and Analytics

Article | April 1, 2022

“Data are becoming the new raw material of business.” Craig Mundie, Senior Advisor to CEO, Microsoft Currently, the most valuable asset that a company has is data. By analyzing a large quantity of data and drawing valuable insights, companies can use raw materials (data) to work more effectively. In addition, many big data analytics case studies show that data gives businesses a big advantage over their less tech-savvy competitors. Let’s explore more about big data and analytics in this article. Why Do the C-Level Executives Need Big Data? Every C-level executive is on the lookout for new insights that help them keep their company viable. In recent years, the use of data analytics has become crucial for business leaders to make important decisions. According to McKinsey & Company, companies using big data analytics extensively across all business segments see a 126% profit improvement over companies that don’t. With the use of big data analytics, these companies see 6.5 times more customer retention, 7.4 times more outperformance than competitors, and almost 19 times more profitability. Here are some top reasons why the C-suite needs big data. Take Calculated Actions Harvard Business Review estimated that 70% of companies don’t feel that they understand the needs of their customers well enough to recognize what initiatives will drive growth. In such cases, you already know what you need to do, i.e., leverage big data and analytics. Big data analytics for businesses can help in recognizing customer preferences and customer segments on the basis of those preferences. C-suites in any industry can align their structure and product offerings to create value and take calculated actions. Recognize the Data According to Statista, data creation will increase to more than 180 zettabytes by 2025, which is a huge number. So, you can’t keep an approach of ‘gather now and sort it out later.’ With this approach to big data, you will be buried under tons of non-structured data. Start tracking the data early and capture the ones that are customer-generated and provide value to your company. Segment Your Customer’s Experience Analyze your present data and utilize your analytics to evaluate which characteristics a group of customers have in common and which aspects they don’t share. Segment and organize customers according to their preferences to build a clear lifecycle structure for every segment. Biggest Concerns About Big Data Analytics According to Concepta, 80% of C-suites think that data analytics will be a transformative force for businesses, but only 1 in 10 deliberately use it. 48% describe analytics as critical to decision-making, but only 7.4% say they use analytics to guide corporate strategy. So, what are the issues or concerns that tech-savvy C-level executives face when it comes to big data and analytics? Integrating Data with Current Technology "Tech inertia" usually disrupts certain businesses from evolving. Sometimes, the analytics framework businesses have in place is outdated to accommodate new techniques. According to Concepta, more than half the C-suite feel their analytics infrastructure is too rigid, and 75% say that due to inflexibility, they could not fulfill their business needs. Changing or upgrading the current technology would result in a loss of productivity. Companies must get the appropriate tools like Oracle Data Integrator 12c, SAP Data Services, MuleSoft, etc. to handle their data integration challenges. Another option is to seek professional assistance. You may either engage seasoned specialists who are far more knowledgeable about these instruments. Another option is to hire big data consultants. Big Data Silos There is a lot of unstructured data that is collected by different departments within a company, which leads to big data silos. The C-suite plays a critical role in developing a strategy, ensuring all departments communicate and integrate data from various sources to get a holistic picture of their business operations. Integrating your software that collects and stores data correctly is one of the most effective ways to avoid data silos Make a decision to use an all-in-one tool to unify and speed up your data management Spare some time to filter your outdated data Big Data Security Big data security is one of the most difficult tasks. Businesses are often so preoccupied with understanding, storing, and analyzing data that they overlook data security. Unsecured data repositories may become fertile grounds for malicious hackers. A data breach may cost a company up to $3.7 million. Businesses are hiring more cybersecurity experts to protect their data. Other measures taken to secure big data include: encryption of data, data segregation, identity and access management endpoint security implementation, real-time security monitoring, and use of big data security technologies such as the IBM Guardian. Key to Big Success from Big Data To get the most out of your big data and overcome the associated challenges, we have listed some key pointers that make a business successful and show how companies using big data are standing out. Have a Calculated Approach While laying the foundation of big data and business analytics, it is important to have a calculated approach as it reduces the risk in the early stages of setting up big data analytics. So, rather than attempting to implement it all at once, businesses should focus on resources that drive value from big data. Programmatic Integration In an action-driven system, success demands synchronizing big data, relevant analytics, and decision-making platforms at the appropriate time. The most successful companies using big data get insights directly from the data analytics tools used by executives who can act immediately according to the insights from the data. Focus on Building Skills Businesses must expand the big data capabilities of current workers through training and development since data analytics talent still remains one of the major challenges. 54% of the CEOs say that their companies have already set up in-house technical training programs for their employees. State-of-the-Art Technology To create strong big data and analytics capabilities, you need the right tools and technologies. Unfortunately, those who don’t have access to efficient big data analytics tools like Hadoop find themselves falling behind. Conclusion There's no going back when it comes to technology. Business decisions and activities are now made based on the use of data, so businesses that don't learn how to use their data will soon be out of date because data is now at the heart of everything. Businesses can align their data structures according to the requirements of their product offerings to generate value by utilizing big data and analytics. It helps to determine consumer preferences and segment consumers based on insights. FAQ How much data does it take to be called “Big Data”? There is no definitive answer to this question. Based on the current market infrastructure, the minimum threshold is somewhere around 1 to 3 terabytes (TB). However, big data technologies are also suitable for smaller databases. Do I Need to Hire a Data Scientist? The decision to hire a data scientist for your company is often a difficult one, and it depends entirely on your business's position. While there has been a huge demand for data scientists over the last few years, they are not easily available. Many businesses just use the support of a data architect or analyst. How are big data and Hadoop related to each other? Hadoop and big data are almost synonymous. Hadoop is a framework that specializes in big data processing that has grown in popularity with the advent of big data. Professionals may use the framework to analyze large amounts of data and assist companies with better decision-making.

Read More
Performance Management

The Aftermath of the Great Resignation

Article | May 30, 2023

We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum. Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake. Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions. Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation. Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization. A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction. Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors. Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt. The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation. Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.

Read More
Business Intelligence, Big Data Management, Big Data

Leveraging Big Data for Competitive Advantage: Benefits

Article | July 18, 2023

Discover the latest strategies and best practices for implementing big data and AI into your organization for 2023. Gain insights on leading Big Data and AI solution providers to drive business growth. Contents 1 Establishing a Relationship between Big Data and AI 2 Importance of Big Data and AI in 2023 3 Key Challenges in Implementing Big Data and AI 4 Best Practices and Strategies for Big Data and AI Implementation 4.1 Building a Data Strategy 4.2 Implementing a Data Governance Framework 4.3 Leveraging Cloud Computing 4.4 Developing a Data Science and AI Roadmap 4.5 Leveraging Established Agile Methodologies 4.6 Prototyping Through Sandboxing 5 Top AI and Big Data Companies to Look For in 2023 6 Conclusion 1. Establishing a Relationship between Big Data and AI The relationship between AI and big data is mutually beneficial, as AI requires vast amounts of data to enhance its decision-making abilities, while big data analytics benefits from AI for superior analysis. This union enables the implementation of advanced analytics, such as predictive analysis, resulting in the optimization of business efficiency by anticipating emerging trends, scrutinizing consumer behavior, automating customer segmentation, customizing digital campaigns, and utilizing decision support systems propelled by big data, AI, and predictive analytics. This integration empowers organizations to become data-driven, resulting in significant improvements in business performance. 2. Importance of Big Data and AI in 2023 In the year 2023, it is anticipated that the utilization of big data analytics and artificial intelligence (AI) will profoundly impact diverse industries. The investment in big data analytics will be primarily driven by the need for data compliance, security, and mobilization, ultimately aiming to achieve real-time analysis. Therefore, businesses seeking to excel in this area must be prepared to adopt cloud technology and make significant advancements in computing power and data processing methods. Recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks. (Source: Forbes) The banking, retail, manufacturing, finance, healthcare, and government sectors have already made substantial investments in big data analytics, which have resulted in the forecasting of trends, enhancing business recommendations, and increasing profits. In addition, AI technology will make significant advancements in 2023, including democratization, making it accessible to a broader user population. This shift will enable customers to wield authority, and businesses will be able to use AI to better meet their specific and individualized business requirements. Finally, a significant shift likely to be witnessed in the AI field in 2023 is the move to a more industrialized, embedded type of architecture, where actual business users may begin utilizing algorithms. According to a recent study, 61% of respondents believe that AI will have a significant impact on their industry within the next three to five years. (Source: Deloitte Insights Report) 3. Key Challenges in Implementing Big Data and AI 97.2% of business executives say their organizations are investing in big data and AI projects. These executives cite their desire to become “nimble, data-driven businesses” as the reason for these investments, as 54.4% say that their companies’ inability to do this was the biggest threat they faced. In addition, 79.4% say they’re afraid that other, more data-driven companies will disrupt and outperform them. (Source: Zippia) Implementing big data analytics and artificial intelligence (AI) presents various challenges that businesses must tackle to realize their full potential. One such obstacle is the intricate nature of the data, which could be either structured or unstructured and necessitate specialized tools and techniques for processing and analysis. Moreover, companies must ensure data quality, completeness, and integrity to facilitate accurate analysis and decision-making. Another substantial challenge in implementing big data and AI is the requirement for skilled personnel with expertise in data science, machine learning, and related technologies. To stay up-to-date on the latest tools and techniques, companies must invest in ongoing training and development programs for their employees. Ethical and legal concerns surrounding data privacy, security, and transparency must also be addressed, especially after recent data breaches and privacy scandals. Integrating big data and AI into existing IT systems can be a challenging and time-consuming process that necessitates careful planning and coordination to ensure smooth integration and minimize disruption. Lastly, the high cost of implementing these technologies can be a significant barrier, especially for smaller businesses or those with limited IT budgets. To overcome these challenges, companies must be strategic, prioritize use cases, and develop a clear implementation roadmap while leveraging third-party tools and services to minimize costs and maximize ROI. 4. Best Practices and Strategies for Big Data and AI Implementation 24% of companies use big data analytics. While 97.2% of companies say they’re investing in big data and AI projects, just 24% describe their organizations as data-driven. (Source: Zippia) 4.1 Building a Data Strategy One of the biggest challenges in building a data strategy is identifying the most relevant data sources and data types for the organization’s specific business objectives. The sheer volume and diversity of data available can further complicate this. The key to addressing this challenge is thoroughly assessing the organization’s data assets and prioritizing them based on their business value. This involves: Identifying the key business objectives and Determining which data sources and data types are most relevant to achieving those objectives 4.2 Implementing a Data Governance Framework Establishing a data governance framework involving all stakeholders is crucial for ensuring agreement on data quality, privacy, and security standards. However, implementing such a framework can be daunting due to the divergent priorities and perspectives of stakeholders on good data governance. So, to overcome this challenge, clear guidelines and processes must be established: Creating a data governance council Defining roles and responsibilities Involving all stakeholders in the development and implementation of guidelines Data quality management, privacy, and security processes should be established to maintain high data governance standards Organizations can improve the effectiveness of their data governance initiatives by aligning all stakeholders and ensuring their commitment to maintaining optimal data governance standards. 4.3 Leveraging Cloud Computing It is essential to carefully select a cloud provider that aligns with the organization's security and compliance requirements. In addition, robust data security and compliance controls should be implemented: Establishing data encryption and access controls Implementing data backup and recovery procedures Regularly conducting security and compliance audits By following these practices, organizations can ensure their big data and AI projects are secure and compliant. 4.4 Developing a Data Science and AI Roadmap The obstacles to developing a data science and AI roadmap lie in identifying the most pertinent use cases that cater to the specific business objectives of an organization. This difficulty is further compounded by the potential divergence of priorities and perspectives among various stakeholders concerning the definition of a successful use case. Hence, it is imperative to establish unambiguous guidelines for identifying and prioritizing use cases that align with their respective business values. This entails: Identifying the key business objectives Carefully ascertaining which use cases are most pertinent to realizing those objectives Meticulously delineating the success criteria for each use case 4.5 Leveraging Established Agile Methodologies Leveraging well-established agile methodologies is critical in successfully implementing large-scale big data and AI projects. By defining a precise project scope and goals, prioritizing tasks, and fostering consistent communication and collaboration, enterprises can effectively execute AI and big data analytics initiatives leveraging agile methodologies. Such an approach provides teams with a clear understanding of their responsibilities, facilitates seamless communication, and promotes continuous improvement throughout the project lifecycle, resulting in a more efficient and effective implementation. 4.6 Prototyping Through Sandboxing Establishing clear guidelines and processes is crucial to overcome the challenge of creating prototypes through sandboxing that are representative of the production environment and can meet the organization's requirements. It includes: Defining the scope and objectives of the prototype, Meticulously selecting the appropriate tools and technologies Guaranteeing that the prototype is an authentic reflection of the production environment Additionally, conducting thorough testing and evaluation is necessary to ensure that the prototype can be scaled effectively to meet the organization's needs. 5. Top AI and Big Data Companies to Look For in 2023 H2O.ai H2O.ai is a leading provider of artificial intelligence (AI) and machine learning (ML) software. It provides a platform for businesses to use artificial intelligence and data-driven insights to drive innovation and growth. The software offers a suite of tools and algorithms to help users build predictive models, analyze data, and gain insights that inform business decisions. With a user-friendly interface and a robust set of features, H2O.ai is a valuable tool for businesses looking to leverage the power of machine learning to stay ahead of the competition. ThoughtSpot ThoughtSpot is a leading search and AI-driven analytics platform that enables businesses to quickly and easily analyze complex data sets. The platform offers a range of features, including advanced analytics, customizable visualizations, and collaborative capabilities. It is designed to make data analytics accessible to anyone within an organization, regardless of technical expertise. The platform is also highly customizable, allowing businesses to tailor it to meet their specific needs and integrate it with their existing data infrastructure. Treasure Data Treasure Data is a cloud-based enterprise data management platform that helps businesses collect, store, and analyze their data to gain valuable insights. Its platform includes a suite of powerful tools for data collection, storage, processing, and analysis, including a flexible data pipeline, a powerful data management console, and a range of analytics tools. The platform is also highly scalable, capable of handling massive amounts of data and processing millions of events per second, making it suitable for businesses of all sizes and industries. Denodo Denodo is a leading data virtualization software company that provides a unified platform for integrating and delivering data across multiple sources and formats in real time. The platform offers unmatched performance and unified access to a broad range of enterprise, big data, cloud, and unstructured sources. It also provides agile data service provisioning and governance at less than half the cost of traditional data integration. In addition, its data virtualization technology simplifies the complexity of data sources and creates a virtual layer of data services accessible to any application or user, regardless of the data’s location or format. Pendo.io Pendo.io is a leading cloud-based platform that provides product analytics, user feedback, and guidance for digital products. It allows businesses to make data-driven decisions about their products and optimize their customer journey. The platform empowers companies to transform product intelligence into actionable insights rapidly and at scale, enabling a new generation of businesses that prioritize product development. TigerGraph TigerGraph is a graph database and analytics platform that allows businesses to gain deeper insights and make better decisions by analyzing connected data. It is designed to handle complex data sets and perform advanced graph analytics at scale. The platform offers a range of graph analytics algorithms that can be applied to a variety of use cases, including fraud detection, recommendation engines, supply chain optimization, and social network analysis. Solix Technologies, Inc. Solix Technologies, Inc. is a leading big data management and analysis software solution provider that empowers data-driven enterprises to achieve their Information Lifecycle Management (ILM) goals. Its flagship product, Solix Big Data Suite, provides an ILM framework for Enterprise Archiving and Enterprise Data Lake applications utilizing Apache Hadoop as an enterprise data repository. In addition, the Solix Enterprise Data Management Suite (Solix EDMS) helps organizations implement database archiving, test data management, data masking and application retirement across all enterprise data. Reltio Reltio is a leading provider of cloud-based master data management (MDM) solutions that enable organizations to create a unified view of their data across all sources and formats. The platform combines MDM with big data analytics and machine learning to provide a single source of truth for data-driven decision-making. The solution offers a range of features, including data modeling, data quality management, data governance, and data analytics. dbt Labs dbt Labs is a cloud-based data transformation software platform that helps analysts and engineers manage the entire analytics engineering workflow, from data ingestion to analysis. The platform enables users to transform and model raw data into analysis-ready data sets using a SQL-based language. With its modular and scalable approach, dbt Labs makes it easier for data teams to collaborate and manage their data pipelines. Rockset Rockset is a real-time indexing database platform that allows businesses to run fast queries on data from multiple sources without needing to manage the underlying infrastructure. It supports various data types, including structured, semi-structured, and nested data, making it flexible and versatile. In addition, the serverless platform is built on a cloud-native architecture, making it easy to scale up or down as needed. With Rockset, users can build real-time applications and dashboards, perform ad hoc analysis, and create data-driven workflows. 6. Conclusion The relationship between big data and AI is mutually beneficial, given the fact that AI requires copious amounts of data to refine its decision-making capabilities, while big data analytics derives immense value from AI for advanced analysis. As a result, the integration of big data analytics and AI is projected to profoundly impact diverse industries in 2023. Nevertheless, adopting these technologies poses multifarious challenges, necessitating businesses to adopt a strategic approach and develop a comprehensive implementation roadmap to optimize ROI and minimize expenses. Ultimately, the successful implementation of big data and AI strategies can enable organizations to become data-driven, culminating in substantial improvements in business performance.

Read More
Business Intelligence, Big Data Management, Big Data

Harnessing the Power of Big Data: Events to attend in 2023

Article | May 15, 2023

In today's environment, running a business can be quite challenging. These ever-changing and dynamic obstacles can make you feel overwhelmed. Maintenance of operations is a time-consuming process that leaves little time for working on the insights needed to gain a competitive advantage. However, organizations of all sizes, particularly SMEs, require accurate and actionable data perspectives. The role of a business intelligence (BI) strategy is to make this data available, which necessitates a deliberate plan. The central goal of a business intelligence strategy is to use software and services to transform important data into actionable knowledge. This is very important as business intelligence revenue in software was projected to reach $23,258.94 million in 2021. BI tools give users access to analytical data, which includes reports, dashboards, maps, charts, and various other visual representations. Users can get detailed information regarding the state of the company. “BI is about providing the right data at the right time to the right people so that they can make the right decisions.” Nic Smith, Microsoft BI Solutions Marketing Business intelligence strategy includes: Performance management Predictive modeling Analytics Data mining Why Should Businesses Implement BI? A business intelligence strategy will allow you to address your data problems, such as clarity, scarcity, insights out of data, and requirements, create a unified system, and sustain it. You should consider implementing a BI strategy if your business faces the following issues: You generate a lot of data but don't know what to do with it Overstocking or understocking Wasted resources and time Loss of customers Underperforming employees Data-driven decisions can benefit your business by: Discovering problems and their solutions Analyzing competitors’ data Analyzing customer behavior Planning approach to increase profit Foreseeing trends Optimizing operations Tracking performance Tips to Create a Successful Business Intelligence Strategy Business intelligence tools and capabilities are designed to create quick and easy-to-understand portrayals of an organization's current state. Developing a strategy to deal with all of these tools and skills is an essential part of reaping the benefits of business intelligence. If you want to learn how to build a strong business intelligence strategy, keep reading. Understand and Assess the Present Status The first step in implementing a business intelligence strategy is to put together a team that is capable of analyzing and presenting the current state of the company's data. With a dedicated team in place, evaluating an organization's current situation entails thinking about the data collected and the technology used to manage it. Understanding the organization's structures and processes for mining and interpreting data is also critical. At this level, a BI team will seek to assess which data is the most valuable and which is irrelevant to the current operations. Have a Vision with a Purpose and Direction A vision is a combination of direction and purpose. Without a vision, there is no strategy. Instead, it presents itself in various critical decisions, such as where we collect our data and who will access the insights. The following should be explained in the vision statement: Who will be in charge of the business intelligence processes? What is the state of your BI strategy concerning the business and IT strategies? How will it provide help and solutions? What solutions do you want to deploy, and where do you propose them? What kind of infrastructure do you want to provide? Prioritize Initiative by Developing a BI Road Map The BI roadmap should provide deliverables at various execution levels and a timetable. On the roadmap, you should have all of the data you wish to organize and arrange, as well as the dates and deliverables for each activity. Define the Way How the Data Is Going to Be Shared Another thing to do before establishing a business intelligence strategy is to define the terms and meaning of BI with all of your stakeholders. Because many employees are involved in data processing, make sure that everyone is on the same page and understands the business intelligence development strategy. At this stage, you should answer all the possible questions from your stakeholders, and the way and process data will be shared with all of them. Must-have BI Strategy Documentation A BI strategy document's logic is that it will serve as a point of reference for the entire organization and will be used to communicate the strategy. The following sections should be in the document: Executive summary BI strategy alignment with corporate strategy Project scope and requirements BI governance team Alternatives Assessment Appendices Make Regular Reviews to Assess the Progress A review process is necessary for any effective business intelligence strategy. These review methods should evaluate lessons gained while also documenting and determining the value of the data to the company. A review process may consider the user's experience and the possibility of changing the business's KPIs year after year. In addition, it helps to understand the progress of the strategy and the benefits it has brought to the company. Summing Up Any business's growth requires a BI strategy as it gives you a competitive advantage. You need a solid strategy, planning, and analysis to enjoy the rewards. You can drown yourself in useless analytics if you don't have a structured roadmap in place. Therefore, staying on track and assessing your methods regularly are critical to reaping the benefits of a BI strategy. The abovementioned steps serve you as stepping stones in developing a successful BI strategy. Frequently Asked Questions What is Business intelligence? Business intelligence is how businesses use methods and technology to analyze both current and historical data. This is done to improve strategic decision-making and gain a competitive advantage. Which are some of the BI tools? Data mining, predictive modeling, and contextual dashboards or KPIs are the most popular and widely used BI tools. Which are some of the major benefits of business intelligence? The benefits of BI are speedy analysis, intuitive dashboards, data-driven business decisions, improved employee satisfaction, increased organizational efficiency, and many more.

Read More
Big Data Management

How Companies Are Using Big Data and Analytics

Article | April 1, 2022

“Data are becoming the new raw material of business.” Craig Mundie, Senior Advisor to CEO, Microsoft Currently, the most valuable asset that a company has is data. By analyzing a large quantity of data and drawing valuable insights, companies can use raw materials (data) to work more effectively. In addition, many big data analytics case studies show that data gives businesses a big advantage over their less tech-savvy competitors. Let’s explore more about big data and analytics in this article. Why Do the C-Level Executives Need Big Data? Every C-level executive is on the lookout for new insights that help them keep their company viable. In recent years, the use of data analytics has become crucial for business leaders to make important decisions. According to McKinsey & Company, companies using big data analytics extensively across all business segments see a 126% profit improvement over companies that don’t. With the use of big data analytics, these companies see 6.5 times more customer retention, 7.4 times more outperformance than competitors, and almost 19 times more profitability. Here are some top reasons why the C-suite needs big data. Take Calculated Actions Harvard Business Review estimated that 70% of companies don’t feel that they understand the needs of their customers well enough to recognize what initiatives will drive growth. In such cases, you already know what you need to do, i.e., leverage big data and analytics. Big data analytics for businesses can help in recognizing customer preferences and customer segments on the basis of those preferences. C-suites in any industry can align their structure and product offerings to create value and take calculated actions. Recognize the Data According to Statista, data creation will increase to more than 180 zettabytes by 2025, which is a huge number. So, you can’t keep an approach of ‘gather now and sort it out later.’ With this approach to big data, you will be buried under tons of non-structured data. Start tracking the data early and capture the ones that are customer-generated and provide value to your company. Segment Your Customer’s Experience Analyze your present data and utilize your analytics to evaluate which characteristics a group of customers have in common and which aspects they don’t share. Segment and organize customers according to their preferences to build a clear lifecycle structure for every segment. Biggest Concerns About Big Data Analytics According to Concepta, 80% of C-suites think that data analytics will be a transformative force for businesses, but only 1 in 10 deliberately use it. 48% describe analytics as critical to decision-making, but only 7.4% say they use analytics to guide corporate strategy. So, what are the issues or concerns that tech-savvy C-level executives face when it comes to big data and analytics? Integrating Data with Current Technology "Tech inertia" usually disrupts certain businesses from evolving. Sometimes, the analytics framework businesses have in place is outdated to accommodate new techniques. According to Concepta, more than half the C-suite feel their analytics infrastructure is too rigid, and 75% say that due to inflexibility, they could not fulfill their business needs. Changing or upgrading the current technology would result in a loss of productivity. Companies must get the appropriate tools like Oracle Data Integrator 12c, SAP Data Services, MuleSoft, etc. to handle their data integration challenges. Another option is to seek professional assistance. You may either engage seasoned specialists who are far more knowledgeable about these instruments. Another option is to hire big data consultants. Big Data Silos There is a lot of unstructured data that is collected by different departments within a company, which leads to big data silos. The C-suite plays a critical role in developing a strategy, ensuring all departments communicate and integrate data from various sources to get a holistic picture of their business operations. Integrating your software that collects and stores data correctly is one of the most effective ways to avoid data silos Make a decision to use an all-in-one tool to unify and speed up your data management Spare some time to filter your outdated data Big Data Security Big data security is one of the most difficult tasks. Businesses are often so preoccupied with understanding, storing, and analyzing data that they overlook data security. Unsecured data repositories may become fertile grounds for malicious hackers. A data breach may cost a company up to $3.7 million. Businesses are hiring more cybersecurity experts to protect their data. Other measures taken to secure big data include: encryption of data, data segregation, identity and access management endpoint security implementation, real-time security monitoring, and use of big data security technologies such as the IBM Guardian. Key to Big Success from Big Data To get the most out of your big data and overcome the associated challenges, we have listed some key pointers that make a business successful and show how companies using big data are standing out. Have a Calculated Approach While laying the foundation of big data and business analytics, it is important to have a calculated approach as it reduces the risk in the early stages of setting up big data analytics. So, rather than attempting to implement it all at once, businesses should focus on resources that drive value from big data. Programmatic Integration In an action-driven system, success demands synchronizing big data, relevant analytics, and decision-making platforms at the appropriate time. The most successful companies using big data get insights directly from the data analytics tools used by executives who can act immediately according to the insights from the data. Focus on Building Skills Businesses must expand the big data capabilities of current workers through training and development since data analytics talent still remains one of the major challenges. 54% of the CEOs say that their companies have already set up in-house technical training programs for their employees. State-of-the-Art Technology To create strong big data and analytics capabilities, you need the right tools and technologies. Unfortunately, those who don’t have access to efficient big data analytics tools like Hadoop find themselves falling behind. Conclusion There's no going back when it comes to technology. Business decisions and activities are now made based on the use of data, so businesses that don't learn how to use their data will soon be out of date because data is now at the heart of everything. Businesses can align their data structures according to the requirements of their product offerings to generate value by utilizing big data and analytics. It helps to determine consumer preferences and segment consumers based on insights. FAQ How much data does it take to be called “Big Data”? There is no definitive answer to this question. Based on the current market infrastructure, the minimum threshold is somewhere around 1 to 3 terabytes (TB). However, big data technologies are also suitable for smaller databases. Do I Need to Hire a Data Scientist? The decision to hire a data scientist for your company is often a difficult one, and it depends entirely on your business's position. While there has been a huge demand for data scientists over the last few years, they are not easily available. Many businesses just use the support of a data architect or analyst. How are big data and Hadoop related to each other? Hadoop and big data are almost synonymous. Hadoop is a framework that specializes in big data processing that has grown in popularity with the advent of big data. Professionals may use the framework to analyze large amounts of data and assist companies with better decision-making.

Read More
MORE ARTICLES

Spotlight

Zementis

Zementis, Inc. is a leading software company focused on the operational deployment of predictive analytics and data mining solutions. Zementis was recognized by CIO Review as one of the "Top 20 most promising Big Data companies in 2013" and named "Cool Vendor in Data Science" by Gartner in 2014. Its ADAPA® and Universal PMML Plug-in scoring engines are designed from the ground up to benefit from open standards and to significantly shorten the time-to-market for predictive analytics in any industry…

Related News

Data Architecture

IQVIA Earns Healthcare Leader Recognition in Data Stack Awards

IQVIA | September 18, 2023

Snowflake recognizes IQVIA as a Global Healthcare Leader in the Measurement and Attribution category as part of its annual Modern Marketing Data Stack awards. The Modern Marketing Data Stack report comprehensively analyzes data tools, applications, technologies, and processes in marketing data stacks. Orchestrated Analytics GM Tanveer Nasir expressed his gratitude for the recognition and emphasized the company's commitment to improving brand performance and patient lives through data-driven insights and solutions. Snowflake, a leading data cloud platform, has recognized IQVIA as a Global Healthcare Leader in the prestigious Measurement and Attribution category. This recognition comes as part of Snowflake's annual Modern Marketing Data Stack awards. The Modern Marketing Data Stack report is the outcome of a comprehensive year-long analysis focusing on data tools, applications, technologies, and processes employed by organizations in their marketing data stacks. This exhaustive assessment, encompassing approximately 8,100 Snowflake customers, employs a weighted scoring algorithm to discern "marketplace leaders" across diverse data-driven business functions and technology categories. The report underscores IQVIA's proficiency in aiding healthcare and life sciences organizations in the compliant utilization of extensive data resources. This enables swift and precise measurement and reporting, ultimately leading to actionable insights that facilitate informed decision-making and the formulation of effective sales and marketing strategies. In recent years, life sciences firms have significantly increased their investments in business intelligence (BI) solutions to enhance their competitiveness and performance. However, this growth has also brought forth challenges, such as analytics failing to address essential business questions, the absence of a "single source of truth" for dependable insights, and the inability to prioritize personalized prescriptive insights. IQVIA's Orchestrated Analytics platform has emerged as a preeminent solution in the industry due to its comprehensive consulting and change management approach. This approach guarantees that solutions align with specific business requirements, irrespective of the market while minimizing initial investment risks. Furthermore, the platform offers an array of self-service applications empowering business stakeholders to customize insights and extract reliable and actionable intelligence. An exceptional feature of IQVIA's Orchestrated Analytics is its extensive library of algorithms, featuring over 200 algorithms and a multitude of Key Performance Indicators (KPIs) exceeding 400, all aimed at elevating commercial impact through personalized insights for each user. The platform's user-friendly interface is complemented by embedded smart assistants, ensuring effortless access to personalized intelligence across a spectrum of business intelligence tools. IQVIA's global presence is another hallmark, with a team of over 86,000 experts operating in more than 100 countries. This expansive network accelerates the commercial impact of life sciences companies by furnishing market-relevant insights. In addition, Orchestrated Analytics is entrusted by seven out of the top ten pharmaceutical companies worldwide as they expand their brand portfolios. Tanveer Nasir, General Manager, Orchestrated Analytics, commented We are honored to be selected by Snowflake as the global leader in Measurement and Attribution in their Modern Marketing Data Stack report. [Source: IQVIA] He further explains that their insight recommendations and user adoption framework worked together effectively to enhance the sales force's efficiency and increase productivity. They had exhibited a significant ROI just by demonstrating Rx uplift for a top 10 pharmaceutical brand. Nasir conveyed that their commitment to enhancing brand performance and improving patients' lives worldwide by identifying the right customer at the right time through the correct channel and messaging continues to drive their passion.

Read More

Business Intelligence, Big Data Management, Big Data

Acoustic Builds Acoustic Connect℠ on the Snowflake Data Cloud

PR Newswire | August 21, 2023

Acoustic, a global marketing and customer engagement provider for B2C brands, today announced its partnership with Snowflake, the Data Cloud company, to optimize data sharing within the Acoustic Connect℠ platform. Acoustic Connect, Powered by Snowflake, enables marketers to seamlessly integrate multiple data sources into marketing activities, giving them a complete view of the customer journey and empowering them to create data-driven, personalization strategies based on behavior signals gathered throughout the digital customer experience. With Snowflake's Data Cloud, Acoustic Connect provides marketers with the ability to act on intent signals and engage customers across the buying journey as the first-of-its-kind platform to link campaign activities, customer data, and behavioral experience in real time. "By partnering with Snowflake, we have embraced a streaming-based model that enables marketers to understand and respond to customer behaviors in real time," said John Riewerts, SVP of Engineering at Acoustic. "With all data centralized in one place, our customers can now personalize their marketing efforts based on a wider selection of attributes. This partnership has allowed us to introduce Acoustic Connect, a game-changing solution that seamlessly ties together journey orchestration, multichannel marketing, and behavioral experience insights in an all-in-one platform." Building Acoustic Connect on Snowflake has allowed Acoustic to leverage Snowflake's Snowpipe Streaming capabilities to deliver enhanced data sharing and management as well as broader customer insights. Marketers using Acoustic Connect can integrate multiple data sources, make sense of this information quickly, and leverage it to launch personalized campaigns that capture consumer attention in the moment. Additionally, the time from implementation to execution is decreased due to Snowflake's ability to stream data, which helps marketers improve time to value and prove marketing ROI quickly. "We're excited to support Acoustic on its journey to transform how marketers orchestrate digital customer experiences by providing real-time data streaming capabilities that empower marketers to react in the moment," said Bill Stratton, Global Industry GTM Lead, Media, Entertainment and Advertising at Snowflake. "By building on the Snowflake Data Cloud, Acoustic is enabling marketers to leverage behavioral insights and create meaningful customer engagements." Joint customers will be able to gain transformative insights in real time with Acoustic Connect, powered by Snowflake. The combination of Acoustic's marketing platform with Snowflake's cutting-edge technology equips brands with the crucial data they need to create personalized, engaging experiences that drive customer satisfaction and loyalty.  Industry leading applications are Powered by Snowflake. By building on Snowflake, product and engineering teams are able to develop, scale, and operate their applications without operational burden, delivering differentiated products to their customers. With the Powered by Snowflake program, builders get access to resources to help them design, market, and operate their applications in the Data Cloud. To learn more about the Powered by Snowflake program and how organizations are building on Snowflake, click here. About Acoustic, L.P. Acoustic, L.P. helps brands build closer, lasting, more rewarding customer connections through data-driven visibility and personal, relevant, and frictionless engagement. Our diverse range of solutions includes unified marketing and digital customer experience insights, campaign execution, behavioral experience analytics, and optimized pricing, promotion, markdown, and collaboration technology. With the help of our award-winning technology and unbeatable client success teams, businesses across industries can drive customer lifetime value. Learn more about the Acoustic portfolio at www.acoustic.com.

Read More

Big Data Management

9Rooftops Launches Trajectory, New Data Driven Marketing Division

9Rooftops | October 08, 2021

Today, 9Rooftops, a leading integrated marketing agency, announced the launch of their new data-driven marketing division, Trajectory, which uses data science and machine learning to bring the most advanced analytical methods to common marketing issues. As marketers continue to shift strategies in response to privacy protections, business intelligence is transforming, and they need to understand why and what levers can be pulled in response to these changes. Trajectory helps clients unlock the true potential of their data, delivering unexpected insights that connect brands to their consumers. Co-led by young industry titans Amy Nixon, SVP, Director of Strategy, and Josh MacCarty, Vice President, Director of Data Science and Analytics, the new division is excited to help brands get the most from their data, using a flexible, scalable model. Nixon and MacCarty will continue to provide data marketing services to existing 9Rooftops clients, who are already leveraging Trajectory’s marketing strategies. “There’s an art and science to what we do – by harnessing the power of data and adding context and strategy, we deliver a data-backed strategic roadmap,” said Amy Nixon, SVP, Director of Strategy. “Trajectory was born from the idea that marketers have fundamental questions they ought to be able to answer but increasingly, can’t. ‘What was the effect of this program on sales?’ ‘How can I generate more traffic?’ Whether it’s identifying marketing attribution or forecasting with confidence, Trajectory provides answers to make marketers’ jobs easier.” “Brands with access to transactional data are sitting on a literal treasure trove of strategic assets and have only partially transformed this information – sometimes, not at all. We’ll help companies gain a significant advantage by leveraging data in highly competitive categories – this is where Trajectory will play an instrumental role in refining strategic analysis.” Kevin Meany, CEO at 9Rooftops Nixon hails from 9Rooftops and prior, FCB Chicago – she has more than 15 years of experience partnering with marketers to best reach consumers and has worked across CPG, retail, healthcare, wine/spirits and more, on brands including SC Johnson, Kraft, Bayer and International Truck. MacCarty is an economist and data science leader and has been bridging the art and science of data analysis for more than 12 years. Uniquely balanced between business acumen and economics, Josh has been recognized as DMN’s “National 40 Under 40 Marketers” for his use of data science to solve business problems and is a three times best-seller on learning platform Udemy for his course, “Foundations of Machine Learning.” MacCarty stated, “Whether you know exactly what you need, or just know you want to get more from your data, we work with clients of all sizes and levels of data sophistication – the breadth of our capabilities is sweeping. We have a top-tier data science team of machine learning experts, econometricians, statisticians, analysts and more who help clients navigate strategic data opportunities to elevate marketing and improve ROI, far surpassing data alone.” 9Rooftops, best known for their work with established brands like Qdoba Mexican Eats, New Amsterdam Vodka, Jelmar/CLR, Belle Tire, The Coca-Cola Company and Barilla, is headquartered in Chicago. About Trajectory Headquartered in Chicago, IL, Trajectory is a data-driven marketing consultancy of 9Rooftops, aiming to uncover valuable insights, fuel smarter marketing strategies and spark growth. Trajectory applies data science and machine learning to bring the most advanced analytical methods to common issues in marketing and helps clients unlock the true potential of their data, delivering unexpected insights that connect brands to their consumers. For more information about Trajectory, visit trajectorydata.com and LinkedIn. About 9Rooftops Headquartered in Chicago, IL, 9Rooftops is an integrated marketing agency, known for breakthrough creative that gets results. Enhanced with an in-house, multimedia content studio, data-driven health division and full-service branded media practice, 9Rooftops has offices in nine cities and more than 250 employees.

Read More

Data Architecture

IQVIA Earns Healthcare Leader Recognition in Data Stack Awards

IQVIA | September 18, 2023

Snowflake recognizes IQVIA as a Global Healthcare Leader in the Measurement and Attribution category as part of its annual Modern Marketing Data Stack awards. The Modern Marketing Data Stack report comprehensively analyzes data tools, applications, technologies, and processes in marketing data stacks. Orchestrated Analytics GM Tanveer Nasir expressed his gratitude for the recognition and emphasized the company's commitment to improving brand performance and patient lives through data-driven insights and solutions. Snowflake, a leading data cloud platform, has recognized IQVIA as a Global Healthcare Leader in the prestigious Measurement and Attribution category. This recognition comes as part of Snowflake's annual Modern Marketing Data Stack awards. The Modern Marketing Data Stack report is the outcome of a comprehensive year-long analysis focusing on data tools, applications, technologies, and processes employed by organizations in their marketing data stacks. This exhaustive assessment, encompassing approximately 8,100 Snowflake customers, employs a weighted scoring algorithm to discern "marketplace leaders" across diverse data-driven business functions and technology categories. The report underscores IQVIA's proficiency in aiding healthcare and life sciences organizations in the compliant utilization of extensive data resources. This enables swift and precise measurement and reporting, ultimately leading to actionable insights that facilitate informed decision-making and the formulation of effective sales and marketing strategies. In recent years, life sciences firms have significantly increased their investments in business intelligence (BI) solutions to enhance their competitiveness and performance. However, this growth has also brought forth challenges, such as analytics failing to address essential business questions, the absence of a "single source of truth" for dependable insights, and the inability to prioritize personalized prescriptive insights. IQVIA's Orchestrated Analytics platform has emerged as a preeminent solution in the industry due to its comprehensive consulting and change management approach. This approach guarantees that solutions align with specific business requirements, irrespective of the market while minimizing initial investment risks. Furthermore, the platform offers an array of self-service applications empowering business stakeholders to customize insights and extract reliable and actionable intelligence. An exceptional feature of IQVIA's Orchestrated Analytics is its extensive library of algorithms, featuring over 200 algorithms and a multitude of Key Performance Indicators (KPIs) exceeding 400, all aimed at elevating commercial impact through personalized insights for each user. The platform's user-friendly interface is complemented by embedded smart assistants, ensuring effortless access to personalized intelligence across a spectrum of business intelligence tools. IQVIA's global presence is another hallmark, with a team of over 86,000 experts operating in more than 100 countries. This expansive network accelerates the commercial impact of life sciences companies by furnishing market-relevant insights. In addition, Orchestrated Analytics is entrusted by seven out of the top ten pharmaceutical companies worldwide as they expand their brand portfolios. Tanveer Nasir, General Manager, Orchestrated Analytics, commented We are honored to be selected by Snowflake as the global leader in Measurement and Attribution in their Modern Marketing Data Stack report. [Source: IQVIA] He further explains that their insight recommendations and user adoption framework worked together effectively to enhance the sales force's efficiency and increase productivity. They had exhibited a significant ROI just by demonstrating Rx uplift for a top 10 pharmaceutical brand. Nasir conveyed that their commitment to enhancing brand performance and improving patients' lives worldwide by identifying the right customer at the right time through the correct channel and messaging continues to drive their passion.

Read More

Business Intelligence, Big Data Management, Big Data

Acoustic Builds Acoustic Connect℠ on the Snowflake Data Cloud

PR Newswire | August 21, 2023

Acoustic, a global marketing and customer engagement provider for B2C brands, today announced its partnership with Snowflake, the Data Cloud company, to optimize data sharing within the Acoustic Connect℠ platform. Acoustic Connect, Powered by Snowflake, enables marketers to seamlessly integrate multiple data sources into marketing activities, giving them a complete view of the customer journey and empowering them to create data-driven, personalization strategies based on behavior signals gathered throughout the digital customer experience. With Snowflake's Data Cloud, Acoustic Connect provides marketers with the ability to act on intent signals and engage customers across the buying journey as the first-of-its-kind platform to link campaign activities, customer data, and behavioral experience in real time. "By partnering with Snowflake, we have embraced a streaming-based model that enables marketers to understand and respond to customer behaviors in real time," said John Riewerts, SVP of Engineering at Acoustic. "With all data centralized in one place, our customers can now personalize their marketing efforts based on a wider selection of attributes. This partnership has allowed us to introduce Acoustic Connect, a game-changing solution that seamlessly ties together journey orchestration, multichannel marketing, and behavioral experience insights in an all-in-one platform." Building Acoustic Connect on Snowflake has allowed Acoustic to leverage Snowflake's Snowpipe Streaming capabilities to deliver enhanced data sharing and management as well as broader customer insights. Marketers using Acoustic Connect can integrate multiple data sources, make sense of this information quickly, and leverage it to launch personalized campaigns that capture consumer attention in the moment. Additionally, the time from implementation to execution is decreased due to Snowflake's ability to stream data, which helps marketers improve time to value and prove marketing ROI quickly. "We're excited to support Acoustic on its journey to transform how marketers orchestrate digital customer experiences by providing real-time data streaming capabilities that empower marketers to react in the moment," said Bill Stratton, Global Industry GTM Lead, Media, Entertainment and Advertising at Snowflake. "By building on the Snowflake Data Cloud, Acoustic is enabling marketers to leverage behavioral insights and create meaningful customer engagements." Joint customers will be able to gain transformative insights in real time with Acoustic Connect, powered by Snowflake. The combination of Acoustic's marketing platform with Snowflake's cutting-edge technology equips brands with the crucial data they need to create personalized, engaging experiences that drive customer satisfaction and loyalty.  Industry leading applications are Powered by Snowflake. By building on Snowflake, product and engineering teams are able to develop, scale, and operate their applications without operational burden, delivering differentiated products to their customers. With the Powered by Snowflake program, builders get access to resources to help them design, market, and operate their applications in the Data Cloud. To learn more about the Powered by Snowflake program and how organizations are building on Snowflake, click here. About Acoustic, L.P. Acoustic, L.P. helps brands build closer, lasting, more rewarding customer connections through data-driven visibility and personal, relevant, and frictionless engagement. Our diverse range of solutions includes unified marketing and digital customer experience insights, campaign execution, behavioral experience analytics, and optimized pricing, promotion, markdown, and collaboration technology. With the help of our award-winning technology and unbeatable client success teams, businesses across industries can drive customer lifetime value. Learn more about the Acoustic portfolio at www.acoustic.com.

Read More

Big Data Management

9Rooftops Launches Trajectory, New Data Driven Marketing Division

9Rooftops | October 08, 2021

Today, 9Rooftops, a leading integrated marketing agency, announced the launch of their new data-driven marketing division, Trajectory, which uses data science and machine learning to bring the most advanced analytical methods to common marketing issues. As marketers continue to shift strategies in response to privacy protections, business intelligence is transforming, and they need to understand why and what levers can be pulled in response to these changes. Trajectory helps clients unlock the true potential of their data, delivering unexpected insights that connect brands to their consumers. Co-led by young industry titans Amy Nixon, SVP, Director of Strategy, and Josh MacCarty, Vice President, Director of Data Science and Analytics, the new division is excited to help brands get the most from their data, using a flexible, scalable model. Nixon and MacCarty will continue to provide data marketing services to existing 9Rooftops clients, who are already leveraging Trajectory’s marketing strategies. “There’s an art and science to what we do – by harnessing the power of data and adding context and strategy, we deliver a data-backed strategic roadmap,” said Amy Nixon, SVP, Director of Strategy. “Trajectory was born from the idea that marketers have fundamental questions they ought to be able to answer but increasingly, can’t. ‘What was the effect of this program on sales?’ ‘How can I generate more traffic?’ Whether it’s identifying marketing attribution or forecasting with confidence, Trajectory provides answers to make marketers’ jobs easier.” “Brands with access to transactional data are sitting on a literal treasure trove of strategic assets and have only partially transformed this information – sometimes, not at all. We’ll help companies gain a significant advantage by leveraging data in highly competitive categories – this is where Trajectory will play an instrumental role in refining strategic analysis.” Kevin Meany, CEO at 9Rooftops Nixon hails from 9Rooftops and prior, FCB Chicago – she has more than 15 years of experience partnering with marketers to best reach consumers and has worked across CPG, retail, healthcare, wine/spirits and more, on brands including SC Johnson, Kraft, Bayer and International Truck. MacCarty is an economist and data science leader and has been bridging the art and science of data analysis for more than 12 years. Uniquely balanced between business acumen and economics, Josh has been recognized as DMN’s “National 40 Under 40 Marketers” for his use of data science to solve business problems and is a three times best-seller on learning platform Udemy for his course, “Foundations of Machine Learning.” MacCarty stated, “Whether you know exactly what you need, or just know you want to get more from your data, we work with clients of all sizes and levels of data sophistication – the breadth of our capabilities is sweeping. We have a top-tier data science team of machine learning experts, econometricians, statisticians, analysts and more who help clients navigate strategic data opportunities to elevate marketing and improve ROI, far surpassing data alone.” 9Rooftops, best known for their work with established brands like Qdoba Mexican Eats, New Amsterdam Vodka, Jelmar/CLR, Belle Tire, The Coca-Cola Company and Barilla, is headquartered in Chicago. About Trajectory Headquartered in Chicago, IL, Trajectory is a data-driven marketing consultancy of 9Rooftops, aiming to uncover valuable insights, fuel smarter marketing strategies and spark growth. Trajectory applies data science and machine learning to bring the most advanced analytical methods to common issues in marketing and helps clients unlock the true potential of their data, delivering unexpected insights that connect brands to their consumers. For more information about Trajectory, visit trajectorydata.com and LinkedIn. About 9Rooftops Headquartered in Chicago, IL, 9Rooftops is an integrated marketing agency, known for breakthrough creative that gets results. Enhanced with an in-house, multimedia content studio, data-driven health division and full-service branded media practice, 9Rooftops has offices in nine cities and more than 250 employees.

Read More

Events