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What is Data Integrity and Why is it Important?

Data Integrity
In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data.

Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions.

Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data.

The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data.

This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct.

Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail:

Types of Data Integrity

There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases.

Physical Integrity

Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more.

Logical Integrity

Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields.

There are four types of logical integrity:

Entity Integrity
A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways.

Referential Integrity
Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department.

Domain Integrity
Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls.

User-Defined Integrity
This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity.

Difference between Data Integrity and Data Security

Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning.

Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity.

Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics.

For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods.

Threats to Data Integrity

Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples:

Human Error

Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data.

Transfer Error

A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database.

Bugs and Viruses

Data can be stolen, altered, or deleted by spyware, malware, or any viruses.

Compromised Hardware

Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised.

Preserving Data Integrity

Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals.

The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist:

Validate Input

Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input.

Validate Data

Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data.

Eliminate Duplicate Data

Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates.

Data Backup

Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware.

Access Control

Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them.

Keep an Audit Trail

In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach.

Conclusion

Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction.

Frequently Asked Questions

What are integrity rules?

Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution.

What is a data integrity example?

Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are:

• When a user tries to enter a date outside an acceptable range
• When a user tries to enter a phone number in the wrong format
• When a bug in an application attempts to delete the wrong record

What are the principles of data integrity?

The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives.

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7 Top Data Analytics Trends

Article | May 2, 2023

The COVID-19 compelled organizations utilizing traditional analytics methods to accept digital data analytics platforms. The pandemic has also accelerated the digital revolution, and as we already know, data and analytics with technologies like AI, NLP, and ML have become the heart of this digital revolution. Therefore, this is the perfect time to break through data, analytics, and AI to make the most of it and stay a step ahead of competitors. Besides that, Techjury says that by 2023, the big data analytics market is expected to be worth $103 billion. This shows how quickly the field of data analytics is growing. Today, the data analytics market has numerous tools and strategies evolving rapidly to keep up with the ever-increasing volume of data gathered and used by businesses. Considering the swift pace and increasing use of data analytics, it is crucial to keep upgrading to stay ahead of the curve. But before we explore the leading data analytics trends, let's check out some data analytics use cases. Data Analytics Use Cases Customer Relationship Analytics One of the biggest challenges is recognizing clients who will spend money continuously for a long period purchasing their products. This insight will assist businesses in attracting customers who will add long-term value to their business. Product Propensity Product propensity analytics combines data on buying actions and behaviors with online behavioral indicators from social media and e-commerce to give insight into the performance of various campaigns and social media platforms promoting the products and services of your company. This enables your business to forecast which clients are most likely to purchase your products and services and which channels are most likely to reach those customers. This lets you focus on the channels that have the best chance of making a lot of money. Recommendation Engines There are recommendations on YouTube, Spotify, Amazon Prime Videos, or other media sites, "recommendations for you." These customized recommendations help users save time and improve their entire customer experience. Top Data Analytics Trends That Will Shape 2022 1. Data Fabrics Architecture The goal of data fabric is to design an exemplary architecture and advise on when data should be delivered or changed. Since data technology designs majorly rely on the ability to use, reuse, and mix numerous data integration techniques, the data fabric reduces integration data technology design time by 30%, deployment time by 30%, and maintenance time by 70%. "The data fabric is the next middleware." -ex-CTO of Splunk, Todd Papaioannou, 2. Decision Intelligence Decision intelligence directly incorporates data analytics into the decision process, with feedback loops to refine and fine-tune the process further. Decision intelligence can be utilized to assist in making decisions, but it also employs techniques like digital twin simulations, reinforcement learning, and artificial intelligence to automate decisions where necessary. 3. XOps With artificial intelligence (AI) and data analytics throughout any firm, XOps has become an essential aspect of business transformation operations. XOps uses DevOps best practices to improve corporate operations, efficiency, and customer experience. In addition, it wants to make sure that the process is reliable, reusable, and repeatable and that there is less technology and process duplication. 4. Graph Analytics Gartner predicts that by 2025, 80% of data and analytics innovation will be developed with the help of graphs. Graph analytics uses engaging algorithms to correlate multiple data points scattered across numerous data assets by exploring relationships. The AI graph is the backbone of modern data and analytics with the help of its expandable features and capability to increase user collaboration and machine learning models. 5. Augmented Analytics Augmented Analytics is another data-trend technology that is gaining prominence. Machine learning, AI, and natural language processing (NLP) are used in augmented analytics to automate data insights for business intelligence, data preparation, discovery, and sharing. The insights provided through augmented analytics help businesses make better decisions. According to Allied Market Research, the worldwide augmented analytics market is expected to reach $29,856 million by 2025. 6. Self-Service Analytics-Low-code and no-code AI Low-code and no-code digital platforms are speeding up the transition to self-service analytics. Non-technical business users can now access data, get insights, and make faster choices because of these platforms. As a result, self-service analytics boosts response times, business agility, speed-to-market, and decision-making in today's modern world. 7. Privacy-Enhancing Computation With the amount of sensitive and personal data being gathered, saved, and processed, it has become imperative to protect consumers' privacy. As regulations become strict and customers become more concerned, new ways to protect their privacy are becoming more important. Privacy-enhancing computing makes sure that value can be extracted from the data with the help of big data analytics without breaking the rules of the game. 3 Ways in Which the C-Suite Can Ensure Enhanced Use of Data Analytics There are many businesses that fail to realize the benefits of data analytics. Here are some ways the C-suite can ensure enhanced use of data analytics. Use Data Analytics for Recommendations Often, the deployment of data analytics is considered a one-time mission instead of an ongoing, interactive process. According to recent McKinsey research, employees are considerably more inclined to data analytics if their leaders actively commit. If the C-suite starts using analytics for decision-making, it will set an example and establish a reliability factor. This shows that when leaders rely on the suggestions and insights of data analytics platforms, rest of the company will follow the C-suite. This will result in broad usage, better success, and higher adoption rates of data analytics. Establish Data Analytics Mind-Sets Senior management starting on this path should learn about data analytics to comprehend what's fast becoming possible. Then they can use the question, "Where might data analytics bring quantum leaps in performance?" to promote lasting behavioral changes throughout the business. A senior executive should lead this exercise with the power and influence to encourage action throughout each critical business unit or function. Use Machine Learning to Automate Decisions The C-suite is introducing machine learning as they are recognizing its value for various departments and processes in an organization either processing or fraud monitoring. 79% of the executives believe that AI will make their jobs more efficient and manageable. Therefore, C-level executives would make an effort to ensure the rest of the organization follows that mentality. They will have to start by using machine learning to automate time-consuming and repeatable tasks. Conclusion From the above-mentioned data analytics trends one can infer that it is no longer only a means to achieve corporate success. In 2022 and beyond, businesses will need to prioritize it as a critical business function, accurately recognizing it as a must-have for long-term success. The future of data analytics will have quality data and technologies like AI at its center. FAQ 1. What is the difference between data analytics and data analysis? Scalability is the key distinguishing factor between analytics and analysis. Data analytics is a broad phrase that encompasses all types of data analysis. The evaluation of data is known as data analysis. Data analysis includes data gathering, organization, storage, and analysis techniques and technologies. 2. When is the right time to deploy an analytics strategy? Data analytics is not a one-time-only activity; it is a continuous process. Companies should not shift their attention from analytics and should utilize it regularly. Usually, once companies realize the potential of analytics to address concerns, they start applying it to various processes. 3. What is platform modernization? Modernization of legacy platforms refers to leveraging and expanding flexibility by preserving consistency across platforms and tackling IT issues. Modernization of legacy platforms also includes rewriting a legacy system for software development.

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Data Virtualization: A Dive into the Virtual Data Lake

Article | May 16, 2023

No matter if you own a retail business, a financial services company, or an online advertising business, data is the most essential resource for contemporary businesses. Businesses are becoming more aware of the significance of their data for business analytics, machine learning, and artificial intelligence across all industries. Smart companies are investing in innovative approaches to derive value from their data, with the goals of gaining a deeper understanding of the requirements and actions of their customers, developing more personalized goods and services, and making strategic choices that will provide them with a competitive advantage in the years to come. Business data warehouses have been utilized for all kinds of business analytics for many decades, and there is a rich ecosystem that revolves around SQL and relational databases. Now, a competitor has entered the picture. Data lakes were developed for the purpose of storing large amounts of data to be used in the training of AI models and predictive analytics. For most businesses, a data lake is an essential component of any digital transformation strategy. However, getting data ready and accessible for creating insights in a controllable manner remains one of the most complicated, expensive, and time-consuming procedures. While data lakes have been around for a long time, new tools and technologies are emerging, and a new set of capabilities are being introduced to data lakes to make them more cost-effective and more widely used. Why Should Businesses Opt for Virtual Data Lakes and Data Virtualization? Data virtualization provides a novel approach to data lakes; modern enterprises have begun to use logical data lake architecture, which is a blended method based on a physical data lake but includes a virtual data layer to create a virtual data lake. Data virtualization combines data from several sources, locations, and formats without requiring replication. In a process that gives many applications and users unified data services, a single "virtual" data layer is created. There are many reasons and benefits for adding a virtual data lake and data virtualization, but we will have a look at the top three reasons that will benefit your business. Reduced Infrastructure Costs Database virtualization can save you money by eliminating the need for additional servers, operating systems, electricity, application licensing, network switches, tools, and storage. Lower Labor Costs Database virtualization makes the work of a database IT administrator considerably easier by simplifying the backup process and enabling them to handle several databases at once. Data Quality Marketers are nervous about the quality and accuracy of the data that they have. According to Singular, in 2019, 13% responded that accuracy was their top concern. And 12% reported having too much data. Database virtualization improves data quality by eliminating replication. Virtual Data Lake and Marketing Leaders Customer data is both challenging as well as an opportunity for marketers. If your company depends on data-driven marketing on any scale and expects to retain a competitive edge, there is no other option: it is time to invest in a virtual data lake. In the omnichannel era, identity resolution is critical to consumer data management. Without it, business marketers would be unable to develop compelling customer experiences. Marketers could be wondering, "A data what?" Consider data lakes in this manner: They provide marketers with important information about the consumer journey as well as immediate responses about marketing performance across various channels and platforms. Most marketers lack insight into performance because they lack the time and technology to filter through all of the sources of that information. A virtual data lake is one solution. Marketers can reliably answer basic questions like, "How are customers engaging with our goods and services, and where is that occurring in the customer journey?" using a data lake. "At what point do our conversion rates begin to decline?" The capacity to detect and solve these sorts of errors at scale and speed—with precise attribution and without double-counting—is invaluable. Marketers can also use data lakes to develop appropriate standards and get background knowledge of activity performance. This provides insight into marketing ROI and acts as a resource for any future marketing initiatives and activities. Empowering Customer Data Platform Using Data Virtualization Businesses are concentrating more than ever on their online operations, which means they are spending more on digital transformation. This involves concentrating on "The Customer," their requirements and insights. Customers have a choice; switching is simple, and customer loyalty is inexpensive, making it even more crucial to know your customer and satisfy their requirements. Data virtualization implies that the customer data platform (CDP) serves as a single data layer that is abstracted from the data source's data format or schemas. The CDP offers just the data selected by the user with no bulk data duplication. This eliminates the need for a data integrator to put up a predetermined schema or fixed field mappings for various event types. Retail Businesses are Leveraging Data Virtualization Retailers have been servicing an increasingly unpredictable customer base over the last two decades. They have the ability to do research, check ratings, compare notes among their personal and professional networks, and switch brands. They now expect to connect with retail businesses in the same way that they interact with social networks. To accomplish so, both established as well as modern retail businesses must use hybrid strategies that combine physical and virtual businesses. In order to achieve this, retail businesses are taking the help of data virtualization to provide seamless experiences across online and in-store environments. How Does Data Virtualization Help in the Elimination of Data Silos? To address these data-silo challenges, several businesses are adopting a much more advanced data integration strategy: data virtualization. In reality, data virtualization and data lakes overlap in many aspects. Both architectures start with the assumption that all data should be accessible to end users. Broad access to big data volumes is employed in both systems to better enable BI and analytics as well as other emerging trends like artificial intelligence and machine learning. Data Virtualization can address a number of big data pain points with features such as query pushdown, caching, and query optimization. Data virtualization enables businesses to access data from various sources such as data warehouses, NoSQL databases, and data lakes without requiring physical data transportation thanks to a virtual layer that covers the complexities of source data from the end user. A couple of use cases where data virtualization can eliminate data silos are: Agile Business Intelligence Legacy BI solutions are now unable to meet the rising enterprise BI requirements. Businesses now need to compete more aggressively. As a result, they must improve the agility of their processes. Data virtualization can improve system agility by integrating data on-demand. Moreover, it offers uniform access to data in a unified layer that can be merged, processed, and cleaned. Businesses may also employ data virtualization to build consistent BI reports for analysis with reduced data structures and instantly provide insights to key decision-makers. Virtual Operational Data Store The Virtual Operational Data Store (VODS) is another noteworthy use of data virtualization. Users can utilize VODS to execute additional operations on the data analyzed by data virtualization, like monitoring, reporting, and control. GPS applications are a perfect example of VODS. Travelers can utilize these applications to get the shortest route to a certain location. A VODS takes data from a variety of data repositories and generates reports on the fly. So, the traveler gets information from a variety of sources without having to worry about which one is the main source. Closing Lines Data warehouses and virtual data lakes are both effective methods for controlling huge amounts of data and advancing to advanced ML analytics. Virtual data lakes are a relatively new technique for storing massive amounts of data on commercial clouds like Amazon S3 and Azure Blob. While dealing with ML workloads, the capacity of a virtual data lake and data virtualization to harness more data from diverse sources in much less time is what makes it a preferable solution. It not only allows users to cooperate and analyze data in new ways, but it also accelerates decision-making. When you require business-friendly and well-engineered data displays for your customers, it makes a strong business case. Through data virtualization, IT can swiftly deploy and repeat a new data set as client needs change. When you need real-time information or want to federate data from numerous sources, data virtualization can let you connect to it rapidly and provide it fresh each time. Frequently Asked Questions What Exactly Is a “Virtual Data Lake?” A virtual data lake is connected to or disconnected from data sources as required by the applications that are using it. It stores data summaries in the sources such that applications can explore the data as if it were a single data collection and obtain entire items as required. What Is the Difference Between a Data Hub and a Data Lake? Data Lakes and Data Hubs (Datahub) are two types of storage systems. A data lake is a collection of raw data that is primarily unstructured. On the other hand, a data hub, is made up of a central storage system whose data is distributed throughout several areas in a star architecture. Does Data Virtualization Store Data? It is critical to understand that data virtualization doesn't at all replicate data from source systems; rather, it saves metadata and integration logic for viewing.

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Business Intelligence, Big Data Management, Big Data

7 Best Ways Big Data Is Transforming The Real Estate Business

Article | August 17, 2023

Real estate firms can enhance their decision-making with the help of Big Data Analytics. The real estate industry was using the data of past events which were not that effective. But now Real estate businesses can use Big Data Analytics for accurate real-time data. The real estate business holds very big risks, not for only developers, it affects the businessmen and investors too. Big Data Analytics help them to get out of this situation and help them to identify their prime opportunities in real estate. With the help of Big Data Analytics real estate professionals will be able to use geographic as well as structured data and that too for targeted marketing. Big Data Analytics analysis the insights of the needs of investment trends and customer desires and the personalized interactions too. So what are those fields in which Big Data Analytics can contribute to the real estate firms? Accuracy in property appraisals Price predictions Risk managing Healthy selling and buying habits Now Bow Big Data Analytics Is Going To Transform The Real Estate Business? 1) Management of Risk With the help of Big Data Analytics Real estate businesses can precisely predict the age of the property and also they can redesign and renovate their property according to their needs. Well, with the help of this the potential risk factors for the buyers and the investors will be reduced. The buyer can make fair cash offers regardless of the condition of the property and also BIg data analytics will take care that their customers never be at a loss. 2) Prospective And Interested Buyers Let me tell you one use of big data analytics. With the help of this technology, the agents no longer need to project blindly they can actually predict the behavior of their customers. The needs of potential buyers can also be analyzed by Big Data and as agents find their customers the customers will be able to find relevant agents to buy their expected real estate property. 3) Higher Property Valuation Property valuation is one of the most important things in the Real Estate market. It decides to make or break the real estate firm. Big Data analytics give insights to the customers about the market conditions, buyer profiles, and other data precisely. Data analytics work on prediction bases and they provide the demographic changes with the help of these changes the estate marketing will be able to forecast the behavior of the customer. Based on the location with the help of Big Data Analytics real estate managers can design and develop their projects likewise. Also, there are apps that use big data analytics. Also Read | Banks in the Metaverse: Why to Get In Early and 3 Ways to Start 4) Improvement In Marketing Strategies The private and public data sources, business surveys, and social media gives them insights that enhance your ability to determine the right market for your project. For instance, take an example, Big Data analytics can give you the data by sorting gender, age, preference, interests, and region. Which will eventually enhance the market interaction of specific firms. 5) Customer’s Experience On Top Big Data insights collected from several platforms like CRM Systems, and social media can help customers to enhance their experience. The agents in the real estate industry can really use and utilize the data to target their potential customers. Well, the customers should rely on agents in the terms of Big Data analytics because Agents understand the needs of customers, and make suggestions on properties depending on buyer preferences. 6) Perfect Predictions With the help of Big Data analytics, the buyers and the sellers can avoid the risks which come in way of the project. Gone are those days when we guessed the real estate trends. Now we can actually analyze them in a proper manner and work on it like it’s left-hand’s play. Also with the great reduction in time. Perfect Predictions of Big Data can be done with the help of computer algorithms. Big data Analytics help buyers and sellers forecast the market fluctuations and that too real-time. There are two different perspectives. Let me discuss them in detail. Low risks properties can be appreciated well with the help of great predictions. Meanwhile, agents and investors think that high risks can give great results. 7) Personalization Of Property Data Big data companies focus on the things that go unnoticed. For example, the amount of sunlight that comes into a room. If you follow your steps to the real estate possession, it will be time-consuming, but with the Big Data analytics, it will be easier for you to get real-time and accurate information about the property. Conclusion Big data analytics is now in use in several industries. Meanwhile, the Real estate industry is also trying to step up and get the help of big data analytics. Well, you can not say that they are not using this technology, They are using it for their greater good. Big Data analytics have become a decision-making factor for all sectors. If you want to transform your business, real estate business particularly, this article will help you from the start to the end.

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Here’s How Analytics are Transforming the Marketing Industry

Article | July 13, 2021

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. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is marketing analytics so important these days?", "acceptedAnswer": { "@type": "Answer", "text": "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" } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "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." } },{ "@type": "Question", "name": "Which companies use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "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." } }] }

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Business Intelligence, Big Data Management, Data Science

Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | May 2, 2023

The COVID-19 compelled organizations utilizing traditional analytics methods to accept digital data analytics platforms. The pandemic has also accelerated the digital revolution, and as we already know, data and analytics with technologies like AI, NLP, and ML have become the heart of this digital revolution. Therefore, this is the perfect time to break through data, analytics, and AI to make the most of it and stay a step ahead of competitors. Besides that, Techjury says that by 2023, the big data analytics market is expected to be worth $103 billion. This shows how quickly the field of data analytics is growing. Today, the data analytics market has numerous tools and strategies evolving rapidly to keep up with the ever-increasing volume of data gathered and used by businesses. Considering the swift pace and increasing use of data analytics, it is crucial to keep upgrading to stay ahead of the curve. But before we explore the leading data analytics trends, let's check out some data analytics use cases. Data Analytics Use Cases Customer Relationship Analytics One of the biggest challenges is recognizing clients who will spend money continuously for a long period purchasing their products. This insight will assist businesses in attracting customers who will add long-term value to their business. Product Propensity Product propensity analytics combines data on buying actions and behaviors with online behavioral indicators from social media and e-commerce to give insight into the performance of various campaigns and social media platforms promoting the products and services of your company. This enables your business to forecast which clients are most likely to purchase your products and services and which channels are most likely to reach those customers. This lets you focus on the channels that have the best chance of making a lot of money. Recommendation Engines There are recommendations on YouTube, Spotify, Amazon Prime Videos, or other media sites, "recommendations for you." These customized recommendations help users save time and improve their entire customer experience. Top Data Analytics Trends That Will Shape 2022 1. Data Fabrics Architecture The goal of data fabric is to design an exemplary architecture and advise on when data should be delivered or changed. Since data technology designs majorly rely on the ability to use, reuse, and mix numerous data integration techniques, the data fabric reduces integration data technology design time by 30%, deployment time by 30%, and maintenance time by 70%. "The data fabric is the next middleware." -ex-CTO of Splunk, Todd Papaioannou, 2. Decision Intelligence Decision intelligence directly incorporates data analytics into the decision process, with feedback loops to refine and fine-tune the process further. Decision intelligence can be utilized to assist in making decisions, but it also employs techniques like digital twin simulations, reinforcement learning, and artificial intelligence to automate decisions where necessary. 3. XOps With artificial intelligence (AI) and data analytics throughout any firm, XOps has become an essential aspect of business transformation operations. XOps uses DevOps best practices to improve corporate operations, efficiency, and customer experience. In addition, it wants to make sure that the process is reliable, reusable, and repeatable and that there is less technology and process duplication. 4. Graph Analytics Gartner predicts that by 2025, 80% of data and analytics innovation will be developed with the help of graphs. Graph analytics uses engaging algorithms to correlate multiple data points scattered across numerous data assets by exploring relationships. The AI graph is the backbone of modern data and analytics with the help of its expandable features and capability to increase user collaboration and machine learning models. 5. Augmented Analytics Augmented Analytics is another data-trend technology that is gaining prominence. Machine learning, AI, and natural language processing (NLP) are used in augmented analytics to automate data insights for business intelligence, data preparation, discovery, and sharing. The insights provided through augmented analytics help businesses make better decisions. According to Allied Market Research, the worldwide augmented analytics market is expected to reach $29,856 million by 2025. 6. Self-Service Analytics-Low-code and no-code AI Low-code and no-code digital platforms are speeding up the transition to self-service analytics. Non-technical business users can now access data, get insights, and make faster choices because of these platforms. As a result, self-service analytics boosts response times, business agility, speed-to-market, and decision-making in today's modern world. 7. Privacy-Enhancing Computation With the amount of sensitive and personal data being gathered, saved, and processed, it has become imperative to protect consumers' privacy. As regulations become strict and customers become more concerned, new ways to protect their privacy are becoming more important. Privacy-enhancing computing makes sure that value can be extracted from the data with the help of big data analytics without breaking the rules of the game. 3 Ways in Which the C-Suite Can Ensure Enhanced Use of Data Analytics There are many businesses that fail to realize the benefits of data analytics. Here are some ways the C-suite can ensure enhanced use of data analytics. Use Data Analytics for Recommendations Often, the deployment of data analytics is considered a one-time mission instead of an ongoing, interactive process. According to recent McKinsey research, employees are considerably more inclined to data analytics if their leaders actively commit. If the C-suite starts using analytics for decision-making, it will set an example and establish a reliability factor. This shows that when leaders rely on the suggestions and insights of data analytics platforms, rest of the company will follow the C-suite. This will result in broad usage, better success, and higher adoption rates of data analytics. Establish Data Analytics Mind-Sets Senior management starting on this path should learn about data analytics to comprehend what's fast becoming possible. Then they can use the question, "Where might data analytics bring quantum leaps in performance?" to promote lasting behavioral changes throughout the business. A senior executive should lead this exercise with the power and influence to encourage action throughout each critical business unit or function. Use Machine Learning to Automate Decisions The C-suite is introducing machine learning as they are recognizing its value for various departments and processes in an organization either processing or fraud monitoring. 79% of the executives believe that AI will make their jobs more efficient and manageable. Therefore, C-level executives would make an effort to ensure the rest of the organization follows that mentality. They will have to start by using machine learning to automate time-consuming and repeatable tasks. Conclusion From the above-mentioned data analytics trends one can infer that it is no longer only a means to achieve corporate success. In 2022 and beyond, businesses will need to prioritize it as a critical business function, accurately recognizing it as a must-have for long-term success. The future of data analytics will have quality data and technologies like AI at its center. FAQ 1. What is the difference between data analytics and data analysis? Scalability is the key distinguishing factor between analytics and analysis. Data analytics is a broad phrase that encompasses all types of data analysis. The evaluation of data is known as data analysis. Data analysis includes data gathering, organization, storage, and analysis techniques and technologies. 2. When is the right time to deploy an analytics strategy? Data analytics is not a one-time-only activity; it is a continuous process. Companies should not shift their attention from analytics and should utilize it regularly. Usually, once companies realize the potential of analytics to address concerns, they start applying it to various processes. 3. What is platform modernization? Modernization of legacy platforms refers to leveraging and expanding flexibility by preserving consistency across platforms and tackling IT issues. Modernization of legacy platforms also includes rewriting a legacy system for software development.

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Big Data Management, Data Science, Big Data

The Future of Big Data: Trends and Innovations for 2023 and Beyond

Article | May 16, 2023

No matter if you own a retail business, a financial services company, or an online advertising business, data is the most essential resource for contemporary businesses. Businesses are becoming more aware of the significance of their data for business analytics, machine learning, and artificial intelligence across all industries. Smart companies are investing in innovative approaches to derive value from their data, with the goals of gaining a deeper understanding of the requirements and actions of their customers, developing more personalized goods and services, and making strategic choices that will provide them with a competitive advantage in the years to come. Business data warehouses have been utilized for all kinds of business analytics for many decades, and there is a rich ecosystem that revolves around SQL and relational databases. Now, a competitor has entered the picture. Data lakes were developed for the purpose of storing large amounts of data to be used in the training of AI models and predictive analytics. For most businesses, a data lake is an essential component of any digital transformation strategy. However, getting data ready and accessible for creating insights in a controllable manner remains one of the most complicated, expensive, and time-consuming procedures. While data lakes have been around for a long time, new tools and technologies are emerging, and a new set of capabilities are being introduced to data lakes to make them more cost-effective and more widely used. Why Should Businesses Opt for Virtual Data Lakes and Data Virtualization? Data virtualization provides a novel approach to data lakes; modern enterprises have begun to use logical data lake architecture, which is a blended method based on a physical data lake but includes a virtual data layer to create a virtual data lake. Data virtualization combines data from several sources, locations, and formats without requiring replication. In a process that gives many applications and users unified data services, a single "virtual" data layer is created. There are many reasons and benefits for adding a virtual data lake and data virtualization, but we will have a look at the top three reasons that will benefit your business. Reduced Infrastructure Costs Database virtualization can save you money by eliminating the need for additional servers, operating systems, electricity, application licensing, network switches, tools, and storage. Lower Labor Costs Database virtualization makes the work of a database IT administrator considerably easier by simplifying the backup process and enabling them to handle several databases at once. Data Quality Marketers are nervous about the quality and accuracy of the data that they have. According to Singular, in 2019, 13% responded that accuracy was their top concern. And 12% reported having too much data. Database virtualization improves data quality by eliminating replication. Virtual Data Lake and Marketing Leaders Customer data is both challenging as well as an opportunity for marketers. If your company depends on data-driven marketing on any scale and expects to retain a competitive edge, there is no other option: it is time to invest in a virtual data lake. In the omnichannel era, identity resolution is critical to consumer data management. Without it, business marketers would be unable to develop compelling customer experiences. Marketers could be wondering, "A data what?" Consider data lakes in this manner: They provide marketers with important information about the consumer journey as well as immediate responses about marketing performance across various channels and platforms. Most marketers lack insight into performance because they lack the time and technology to filter through all of the sources of that information. A virtual data lake is one solution. Marketers can reliably answer basic questions like, "How are customers engaging with our goods and services, and where is that occurring in the customer journey?" using a data lake. "At what point do our conversion rates begin to decline?" The capacity to detect and solve these sorts of errors at scale and speed—with precise attribution and without double-counting—is invaluable. Marketers can also use data lakes to develop appropriate standards and get background knowledge of activity performance. This provides insight into marketing ROI and acts as a resource for any future marketing initiatives and activities. Empowering Customer Data Platform Using Data Virtualization Businesses are concentrating more than ever on their online operations, which means they are spending more on digital transformation. This involves concentrating on "The Customer," their requirements and insights. Customers have a choice; switching is simple, and customer loyalty is inexpensive, making it even more crucial to know your customer and satisfy their requirements. Data virtualization implies that the customer data platform (CDP) serves as a single data layer that is abstracted from the data source's data format or schemas. The CDP offers just the data selected by the user with no bulk data duplication. This eliminates the need for a data integrator to put up a predetermined schema or fixed field mappings for various event types. Retail Businesses are Leveraging Data Virtualization Retailers have been servicing an increasingly unpredictable customer base over the last two decades. They have the ability to do research, check ratings, compare notes among their personal and professional networks, and switch brands. They now expect to connect with retail businesses in the same way that they interact with social networks. To accomplish so, both established as well as modern retail businesses must use hybrid strategies that combine physical and virtual businesses. In order to achieve this, retail businesses are taking the help of data virtualization to provide seamless experiences across online and in-store environments. How Does Data Virtualization Help in the Elimination of Data Silos? To address these data-silo challenges, several businesses are adopting a much more advanced data integration strategy: data virtualization. In reality, data virtualization and data lakes overlap in many aspects. Both architectures start with the assumption that all data should be accessible to end users. Broad access to big data volumes is employed in both systems to better enable BI and analytics as well as other emerging trends like artificial intelligence and machine learning. Data Virtualization can address a number of big data pain points with features such as query pushdown, caching, and query optimization. Data virtualization enables businesses to access data from various sources such as data warehouses, NoSQL databases, and data lakes without requiring physical data transportation thanks to a virtual layer that covers the complexities of source data from the end user. A couple of use cases where data virtualization can eliminate data silos are: Agile Business Intelligence Legacy BI solutions are now unable to meet the rising enterprise BI requirements. Businesses now need to compete more aggressively. As a result, they must improve the agility of their processes. Data virtualization can improve system agility by integrating data on-demand. Moreover, it offers uniform access to data in a unified layer that can be merged, processed, and cleaned. Businesses may also employ data virtualization to build consistent BI reports for analysis with reduced data structures and instantly provide insights to key decision-makers. Virtual Operational Data Store The Virtual Operational Data Store (VODS) is another noteworthy use of data virtualization. Users can utilize VODS to execute additional operations on the data analyzed by data virtualization, like monitoring, reporting, and control. GPS applications are a perfect example of VODS. Travelers can utilize these applications to get the shortest route to a certain location. A VODS takes data from a variety of data repositories and generates reports on the fly. So, the traveler gets information from a variety of sources without having to worry about which one is the main source. Closing Lines Data warehouses and virtual data lakes are both effective methods for controlling huge amounts of data and advancing to advanced ML analytics. Virtual data lakes are a relatively new technique for storing massive amounts of data on commercial clouds like Amazon S3 and Azure Blob. While dealing with ML workloads, the capacity of a virtual data lake and data virtualization to harness more data from diverse sources in much less time is what makes it a preferable solution. It not only allows users to cooperate and analyze data in new ways, but it also accelerates decision-making. When you require business-friendly and well-engineered data displays for your customers, it makes a strong business case. Through data virtualization, IT can swiftly deploy and repeat a new data set as client needs change. When you need real-time information or want to federate data from numerous sources, data virtualization can let you connect to it rapidly and provide it fresh each time. Frequently Asked Questions What Exactly Is a “Virtual Data Lake?” A virtual data lake is connected to or disconnected from data sources as required by the applications that are using it. It stores data summaries in the sources such that applications can explore the data as if it were a single data collection and obtain entire items as required. What Is the Difference Between a Data Hub and a Data Lake? Data Lakes and Data Hubs (Datahub) are two types of storage systems. A data lake is a collection of raw data that is primarily unstructured. On the other hand, a data hub, is made up of a central storage system whose data is distributed throughout several areas in a star architecture. Does Data Virtualization Store Data? It is critical to understand that data virtualization doesn't at all replicate data from source systems; rather, it saves metadata and integration logic for viewing.

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Business Intelligence, Big Data Management, Big Data

Choose the Right BI Solution: Top Business Intelligence Companies

Article | August 17, 2023

Real estate firms can enhance their decision-making with the help of Big Data Analytics. The real estate industry was using the data of past events which were not that effective. But now Real estate businesses can use Big Data Analytics for accurate real-time data. The real estate business holds very big risks, not for only developers, it affects the businessmen and investors too. Big Data Analytics help them to get out of this situation and help them to identify their prime opportunities in real estate. With the help of Big Data Analytics real estate professionals will be able to use geographic as well as structured data and that too for targeted marketing. Big Data Analytics analysis the insights of the needs of investment trends and customer desires and the personalized interactions too. So what are those fields in which Big Data Analytics can contribute to the real estate firms? Accuracy in property appraisals Price predictions Risk managing Healthy selling and buying habits Now Bow Big Data Analytics Is Going To Transform The Real Estate Business? 1) Management of Risk With the help of Big Data Analytics Real estate businesses can precisely predict the age of the property and also they can redesign and renovate their property according to their needs. Well, with the help of this the potential risk factors for the buyers and the investors will be reduced. The buyer can make fair cash offers regardless of the condition of the property and also BIg data analytics will take care that their customers never be at a loss. 2) Prospective And Interested Buyers Let me tell you one use of big data analytics. With the help of this technology, the agents no longer need to project blindly they can actually predict the behavior of their customers. The needs of potential buyers can also be analyzed by Big Data and as agents find their customers the customers will be able to find relevant agents to buy their expected real estate property. 3) Higher Property Valuation Property valuation is one of the most important things in the Real Estate market. It decides to make or break the real estate firm. Big Data analytics give insights to the customers about the market conditions, buyer profiles, and other data precisely. Data analytics work on prediction bases and they provide the demographic changes with the help of these changes the estate marketing will be able to forecast the behavior of the customer. Based on the location with the help of Big Data Analytics real estate managers can design and develop their projects likewise. Also, there are apps that use big data analytics. Also Read | Banks in the Metaverse: Why to Get In Early and 3 Ways to Start 4) Improvement In Marketing Strategies The private and public data sources, business surveys, and social media gives them insights that enhance your ability to determine the right market for your project. For instance, take an example, Big Data analytics can give you the data by sorting gender, age, preference, interests, and region. Which will eventually enhance the market interaction of specific firms. 5) Customer’s Experience On Top Big Data insights collected from several platforms like CRM Systems, and social media can help customers to enhance their experience. The agents in the real estate industry can really use and utilize the data to target their potential customers. Well, the customers should rely on agents in the terms of Big Data analytics because Agents understand the needs of customers, and make suggestions on properties depending on buyer preferences. 6) Perfect Predictions With the help of Big Data analytics, the buyers and the sellers can avoid the risks which come in way of the project. Gone are those days when we guessed the real estate trends. Now we can actually analyze them in a proper manner and work on it like it’s left-hand’s play. Also with the great reduction in time. Perfect Predictions of Big Data can be done with the help of computer algorithms. Big data Analytics help buyers and sellers forecast the market fluctuations and that too real-time. There are two different perspectives. Let me discuss them in detail. Low risks properties can be appreciated well with the help of great predictions. Meanwhile, agents and investors think that high risks can give great results. 7) Personalization Of Property Data Big data companies focus on the things that go unnoticed. For example, the amount of sunlight that comes into a room. If you follow your steps to the real estate possession, it will be time-consuming, but with the Big Data analytics, it will be easier for you to get real-time and accurate information about the property. Conclusion Big data analytics is now in use in several industries. Meanwhile, the Real estate industry is also trying to step up and get the help of big data analytics. Well, you can not say that they are not using this technology, They are using it for their greater good. Big Data analytics have become a decision-making factor for all sectors. If you want to transform your business, real estate business particularly, this article will help you from the start to the end.

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Here’s How Analytics are Transforming the Marketing Industry

Article | July 13, 2021

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. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why is marketing analytics so important these days?", "acceptedAnswer": { "@type": "Answer", "text": "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" } },{ "@type": "Question", "name": "What is the use of marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "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." } },{ "@type": "Question", "name": "Which companies use marketing analytics?", "acceptedAnswer": { "@type": "Answer", "text": "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." } }] }

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UE.co software and services company that builds cutting edge marketing and customer acquisition software for financial companies. Enterprise-level advertisers leverage the UE.co solution to manage the marketing and sales activity of their sales-force. The UE platform tracks lead outcomes using AI driven, real time reporting and provides access to unparalleled support, making click, call, and lead purchasing more efficient. The Platform is also leveraged to monetize online breakage, by providing additional distribution opportunities for partners with organic traffic. UE does not charge subscription or annual fees, and incorporates usage costs into products being sourced through the platform.

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NetApp Empowers Secure Cloud Sovereignty with StorageGRID

NetApp | November 08, 2023

NetApp introduces StorageGRID for VMware Sovereign Cloud, enhancing data storage and security for sovereign cloud customers. NetApp's Object Storage plugin for VMware Cloud Director enables seamless integration of StorageGRID for secure Object Storage for unstructured data. NetApp's Sovereign Cloud integration ensures data sovereignty, security, and data value while adhering to regulatory standards. NetApp, a prominent global cloud-led, data-centric software company, has recently introduced NetApp StorageGRID for VMware Sovereign Cloud. This NetApp plugin offering for VMware Cloud Director Object Storage Extension empowers sovereign cloud customers to cost-efficiently secure, store, protect, and preserve unstructured data while adhering to global data privacy and residency regulations. Additionally, NetApp has also unveiled the latest release of NetApp ONTAP Tools for VMware vSphere (OTV 10.0), which is designed to streamline and centralize enterprise data management within multi-tenant vSphere environments. The concept of sovereignty has emerged as a vital facet of cloud computing for entities that handle highly sensitive data, including national and state governments, as well as tightly regulated sectors like finance and healthcare. In this context, national governments are increasingly exploring ways to enhance their digital economic capabilities and reduce their reliance on multinational corporations for cloud services. NetApp's newly introduced Object Storage plugin for VMware Cloud Director offers Cloud Service Providers a seamless means to integrate StorageGRID as their primary Object Storage solution to provide secure Object Storage for unstructured data to their customers. This integration provides StorageGRID services into the familiar VMware Cloud Director user interface, thereby minimizing training requirements and accelerating time to revenue for partners. A noteworthy feature of StorageGRID is its universal compatibility and native support for industry-standard APIs, such as the Amazon S3 API, facilitating smooth interoperability across diverse cloud environments. Enhanced functionalities like automated lifecycle management further ensure cost-effective data protection, storage, and high availability for unstructured data within VMware environments. The integration of NetApp's Sovereign Cloud with Cloud Director empowers providers to offer customers: Robust assurance that sensitive data, including metadata, remains under sovereign control, safeguarding against potential access by foreign authorities that may infringe upon data privacy laws. Heightened security and compliance measures that protect applications and data from evolving cybersecurity threats, all while maintaining continuous compliance with infrastructure, trusted local, established frameworks, and local experts. A future-proof infrastructure capable of swiftly reacting to evolving data privacy regulations, security challenges, and geopolitical dynamics. The ability to unlock the value of data through secure data sharing and analysis, fostering innovation without compromising privacy laws and ensuring data integrity to derive accurate insights. VMware Sovereign Cloud providers are dedicated to designing and operating cloud solutions rooted in modern, software-defined architectures that embody the core principles and best practices outlined in the VMware Sovereign Cloud framework. Workloads within VMware Sovereign Cloud environments are often characterized by a diverse range of data sets, including transactional workloads and substantial volumes of unstructured data, all requiring cost-effective and integrated management that is compliant with regulated standards for sovereign and regulated customers. In addition to the aforementioned advancements, NetApp also announced a collaborative effort with VMware aimed at modernizing API integrations between NetApp ONTAP and VMware vSphere. This integration empowers VMware administrators to streamline the management and operations of NetApp ONTAP-based data management platforms within multi-tenant vSphere environments, all while allowing users to leverage a new micro-services-based architecture that offers enhanced scalability and availability. With the latest releases of NetApp ONTAP and ONTAP Tools for vSphere, NetApp has significantly made protection, provisioning, and securing modern VMware environments at scale faster and easier, all while maintaining a centralized point of visibility and control through vSphere. NetApp ONTAP Tools for VMware provides two key benefits to customers: A redefined architecture featuring VMware vSphere APIs for Storage Awareness (VASA) integration, simplifying policy-driven operations and enabling cloud-like scalability. An automation-enabled framework driven by an API-first approach, allowing IT teams to seamlessly integrate with existing tools and construct end-to-end workflows for easy consumption of features and capabilities.

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Big Data Management

Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

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Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

Read More

Big Data Management

NetApp Empowers Secure Cloud Sovereignty with StorageGRID

NetApp | November 08, 2023

NetApp introduces StorageGRID for VMware Sovereign Cloud, enhancing data storage and security for sovereign cloud customers. NetApp's Object Storage plugin for VMware Cloud Director enables seamless integration of StorageGRID for secure Object Storage for unstructured data. NetApp's Sovereign Cloud integration ensures data sovereignty, security, and data value while adhering to regulatory standards. NetApp, a prominent global cloud-led, data-centric software company, has recently introduced NetApp StorageGRID for VMware Sovereign Cloud. This NetApp plugin offering for VMware Cloud Director Object Storage Extension empowers sovereign cloud customers to cost-efficiently secure, store, protect, and preserve unstructured data while adhering to global data privacy and residency regulations. Additionally, NetApp has also unveiled the latest release of NetApp ONTAP Tools for VMware vSphere (OTV 10.0), which is designed to streamline and centralize enterprise data management within multi-tenant vSphere environments. The concept of sovereignty has emerged as a vital facet of cloud computing for entities that handle highly sensitive data, including national and state governments, as well as tightly regulated sectors like finance and healthcare. In this context, national governments are increasingly exploring ways to enhance their digital economic capabilities and reduce their reliance on multinational corporations for cloud services. NetApp's newly introduced Object Storage plugin for VMware Cloud Director offers Cloud Service Providers a seamless means to integrate StorageGRID as their primary Object Storage solution to provide secure Object Storage for unstructured data to their customers. This integration provides StorageGRID services into the familiar VMware Cloud Director user interface, thereby minimizing training requirements and accelerating time to revenue for partners. A noteworthy feature of StorageGRID is its universal compatibility and native support for industry-standard APIs, such as the Amazon S3 API, facilitating smooth interoperability across diverse cloud environments. Enhanced functionalities like automated lifecycle management further ensure cost-effective data protection, storage, and high availability for unstructured data within VMware environments. The integration of NetApp's Sovereign Cloud with Cloud Director empowers providers to offer customers: Robust assurance that sensitive data, including metadata, remains under sovereign control, safeguarding against potential access by foreign authorities that may infringe upon data privacy laws. Heightened security and compliance measures that protect applications and data from evolving cybersecurity threats, all while maintaining continuous compliance with infrastructure, trusted local, established frameworks, and local experts. A future-proof infrastructure capable of swiftly reacting to evolving data privacy regulations, security challenges, and geopolitical dynamics. The ability to unlock the value of data through secure data sharing and analysis, fostering innovation without compromising privacy laws and ensuring data integrity to derive accurate insights. VMware Sovereign Cloud providers are dedicated to designing and operating cloud solutions rooted in modern, software-defined architectures that embody the core principles and best practices outlined in the VMware Sovereign Cloud framework. Workloads within VMware Sovereign Cloud environments are often characterized by a diverse range of data sets, including transactional workloads and substantial volumes of unstructured data, all requiring cost-effective and integrated management that is compliant with regulated standards for sovereign and regulated customers. In addition to the aforementioned advancements, NetApp also announced a collaborative effort with VMware aimed at modernizing API integrations between NetApp ONTAP and VMware vSphere. This integration empowers VMware administrators to streamline the management and operations of NetApp ONTAP-based data management platforms within multi-tenant vSphere environments, all while allowing users to leverage a new micro-services-based architecture that offers enhanced scalability and availability. With the latest releases of NetApp ONTAP and ONTAP Tools for vSphere, NetApp has significantly made protection, provisioning, and securing modern VMware environments at scale faster and easier, all while maintaining a centralized point of visibility and control through vSphere. NetApp ONTAP Tools for VMware provides two key benefits to customers: A redefined architecture featuring VMware vSphere APIs for Storage Awareness (VASA) integration, simplifying policy-driven operations and enabling cloud-like scalability. An automation-enabled framework driven by an API-first approach, allowing IT teams to seamlessly integrate with existing tools and construct end-to-end workflows for easy consumption of features and capabilities.

Read More

Big Data Management

Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

Read More

Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

Read More

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