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How Companies Are Using Big Data and Analytics

How Companies

“Data are becoming the new raw material of business.”

Craig Mundie, Senior Advisor to CEO, Microsoft

Currently, the most valuable asset that a company has is data. By analyzing a large quantity of data and drawing valuable insights, companies can use raw materials (data) to work more effectively. In addition, many big data analytics case studies show that data gives businesses a big advantage over their less tech-savvy competitors.

Let’s explore more about big data and analytics in this article.


Why Do the C-Level Executives Need Big Data?

Every C-level executive is on the lookout for new insights that help them keep their company viable. In recent years, the use of data analytics has become crucial for business leaders to make important decisions.

According to McKinsey & Company, companies using big data analytics extensively across all business segments see a 126% profit improvement over companies that don’t. With the use of big data analytics, these companies see 6.5 times more customer retention, 7.4 times more outperformance than competitors, and almost 19 times more profitability. Here are some top reasons why the C-suite needs big data.

Take Calculated Actions

Harvard Business Review estimated that 70% of companies don’t feel that they understand the needs of their customers well enough to recognize what initiatives will drive growth. In such cases, you already know what you need to do, i.e., leverage big data and analytics.

Big data analytics for businesses can help in recognizing customer preferences and customer segments on the basis of those preferences. C-suites in any industry can align their structure and product offerings to create value and take calculated actions.

Recognize the Data

According to Statista, data creation will increase to more than 180 zettabytes by 2025, which is a huge number. So, you can’t keep an approach of ‘gather now and sort it out later.’ With this approach to big data, you will be buried under tons of non-structured data. Start tracking the data early and capture the ones that are customer-generated and provide value to your company.

Segment Your Customer’s Experience

Analyze your present data and utilize your analytics to evaluate which characteristics a group of customers have in common and which aspects they don’t share. Segment and organize customers according to their preferences to build a clear lifecycle structure for every segment.

Biggest Concerns About Big Data Analytics

According to Concepta, 80% of C-suites think that data analytics will be a transformative force for businesses, but only 1 in 10 deliberately use it. 48% describe analytics as critical to decision-making, but only 7.4% say they use analytics to guide corporate strategy. So, what are the issues or concerns that tech-savvy C-level executives face when it comes to big data and analytics?

Integrating Data with Current Technology

"Tech inertia" usually disrupts certain businesses from evolving. Sometimes, the analytics framework businesses have in place is outdated to accommodate new techniques. According to Concepta, more than half the C-suite feel their analytics infrastructure is too rigid, and 75% say that due to inflexibility, they could not fulfill their business needs. Changing or upgrading the current technology would result in a loss of productivity.

Companies must get the appropriate tools like Oracle Data Integrator 12c, SAP Data Services, MuleSoft, etc. to handle their data integration challenges. Another option is to seek professional assistance. You may either engage seasoned specialists who are far more knowledgeable about these instruments. Another option is to hire big data consultants.

Big Data Silos

There is a lot of unstructured data that is collected by different departments within a company, which leads to big data silos. The C-suite plays a critical role in developing a strategy, ensuring all departments communicate and integrate data from various sources to get a holistic picture of their business operations.

  • Integrating your software that collects and stores data correctly is one of the most effective ways to avoid data silos
  • Make a decision to use an all-in-one tool to unify and speed up your data management
  • Spare some time to filter your outdated data

Big Data Security

Big data security is one of the most difficult tasks. Businesses are often so preoccupied with understanding, storing, and analyzing data that they overlook data security. Unsecured data repositories may become fertile grounds for malicious hackers. A data breach may cost a company up to $3.7 million.

Businesses are hiring more cybersecurity experts to protect their data. Other measures taken to secure big data include: encryption of data, data segregation, identity and access management endpoint security implementation, real-time security monitoring, and use of big data security technologies such as the IBM Guardian.


Key to Big Success from Big Data

To get the most out of your big data and overcome the associated challenges, we have listed some key pointers that make a business successful and show how companies using big data are standing out.


Have a Calculated Approach

While laying the foundation of big data and business analytics, it is important to have a calculated approach as it reduces the risk in the early stages of setting up big data analytics. So, rather than attempting to implement it all at once, businesses should focus on resources that drive value from big data.

Programmatic Integration

In an action-driven system, success demands synchronizing big data, relevant analytics, and decision-making platforms at the appropriate time. The most successful companies using big data get insights directly from the data analytics tools used by executives who can act immediately according to the insights from the data.

Focus on Building Skills

Businesses must expand the big data capabilities of current workers through training and development since data analytics talent still remains one of the major challenges. 54% of the CEOs say that their companies have already set up in-house technical training programs for their employees.

State-of-the-Art Technology

To create strong big data and analytics capabilities, you need the right tools and technologies. Unfortunately, those who don’t have access to efficient big data analytics tools like Hadoop find themselves falling behind.

Conclusion

There's no going back when it comes to technology. Business decisions and activities are now made based on the use of data, so businesses that don't learn how to use their data will soon be out of date because data is now at the heart of everything.

Businesses can align their data structures according to the requirements of their product offerings to generate value by utilizing big data and analytics. It helps to determine consumer preferences and segment consumers based on insights.

FAQ


How much data does it take to be called “Big Data”?

There is no definitive answer to this question. Based on the current market infrastructure, the minimum threshold is somewhere around 1 to 3 terabytes (TB). However, big data technologies are also suitable for smaller databases.

Do I Need to Hire a Data Scientist?

The decision to hire a data scientist for your company is often a difficult one, and it depends entirely on your business's position. While there has been a huge demand for data scientists over the last few years, they are not easily available. Many businesses just use the support of a data architect or analyst.

How are big data and Hadoop related to each other?

Hadoop and big data are almost synonymous. Hadoop is a framework that specializes in big data processing that has grown in popularity with the advent of big data. Professionals may use the framework to analyze large amounts of data and assist companies with better decision-making.

Spotlight

Linguamatics

Linguamatics is the world leader in deploying innovative natural language processing (NLP)-based text mining for high-value knowledge discovery, information extraction and decision support. Linguamatics I2E is used by leading hospitals, the U.S. Food and Drug Administration (FDA), premier academic institutions and 18 of the top 20 global pharmaceutical companies.

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Big Data Management, Data Science, Big Data

Data-driven Content Marketing for 2022

Article | April 28, 2023

In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting. The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’. The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience. Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years. Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online. We have highlighted the steps you need for successful data-driven marketing. 5 Steps to Take for Successful Data-Driven Content Marketing Layout Objectives For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform. For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales. If you're not putting out relevant content in relevant places, you don't exist. Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality. Customize Campaigns for Target Audience Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them. Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where. Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts. Regular Content Optimization One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day. Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment. Keep running SEO audits on your pages regularly to keep your content in the best shape possible. Content Repurposing Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness. Track Analytics Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track. There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives. Final Thoughts In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns. Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges. Frequently Asked Questions What is the future of content marketing? Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business. What are the top content marketing trends for 2022? 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics How is content-based marketing a proven strategy? Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the future of content marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business." } },{ "@type": "Question", "name": "What are the top content marketing trends for 2022?", "acceptedAnswer": { "@type": "Answer", "text": " A. 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics" } },{ "@type": "Question", "name": "How is content-based marketing a proven strategy?", "acceptedAnswer": { "@type": "Answer", "text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness." } }] }

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Business Intelligence, Big Data Management, Data Science

Know the Text Analytics Use Cases in Businesses

Article | April 13, 2023

“Text analytics can help organizations discover patterns in large unstructured data sets. Unstructured data, such as videos, photos, and audio accounts for at least 80% of your company’s data, a true blind spot for most businesses.” - IBM Data scientists use advanced data science approaches to examine text. This textual data provides a better understanding of client attitudes toward certain topics or uncovers additional information. Using this text analytics, you can turn free-form text into structured data for use in prediction models or uncover hidden patterns in your data. If you want, you can use text analysis to identify prospective customers who might be interested in cross-selling, forecast customer attitudes, and understand fraud-prevention behaviors. Businesses understand the value of their raw text across all industries. As a result, with the help of data, they can reduce operational expenses, find previously unknown linkages, and get a better insight into the future trends. Is it hard to comprehend that text accounts for 80% of all corporate data? Online reviews, call center transcripts, consumer surveys, and other written documents are examples. This raw text data is a gold mine for understanding customer attitudes. Text mining and analytics transform these underutilized data sources into actionable information. However, each organization must have the expertise, infrastructure, and analytical perspective to implement this cutting-edge technology in their own way. How is Text Analytics Used in Companies? Companies can use NLP and untapped data sources in a number of ground-breaking ways. Many businesses are already successfully employing text to drive their operations. In addition, text analytics can help you improve your procedures if you're transitioning from business intelligence reporting to data science. Best Five Text Analytics Use Cases for Businesses Companies and people, regardless of industry, desire to make better-informed business decisions based on trackable and measurable data. Thanks to improvements in text analysis, companies can now mine the text for insights and improve their service or offering to thrive in their industry. Read on to understand some of the text analytics use cases that could be applied in your company. Voice of the Customer (VOC) to Extract Customer Opinion on a product Companies employ VOC applications to determine what customers say about a product or service. Emails, call center logs, surveys, and social media streams such as blogs, tweets, forum postings, newsfeeds, and so on are examples of data sources. A telecommunications company, for example, might use voice of customer text analysis to look for complaints about their online services on Twitter. It will give them an early warning when customers aren’t happy with the service's performance, so that they can act before the client calls to complain or publicly ask for the contract to be terminated. Lead Generation through Social Media A piece of social media information can be used to retain and get new clients. It is like the use of the VOC application. For example, if a person tweets that they are interested in a particular product or service, text analytics can detect this and pass the information to a sales representative, who can then pursue the prospect and turn them into a customer. Finding Out What Customers Value through Market Research According to numerous statistics, consumers are interested in other people's thoughts and experiences. According to a study, at least 90% of humans are influenced by what they read. Also, if the review is terrible, the sentiment is shared. In the last few years, several websites have been collecting reviews of local eateries, vacation spots, and, of course, commercial products. If your company is thinking about going into a new market or looking into new product ideas, why not start by looking into online market research reviews from real people? Market research helps you know what features are important to customers when you start your marketing effort. It's critical to know which characteristics influence purchasing decisions and contribute to customer unhappiness. Use Customer Complaints to Identify New Product Ideals Understanding the consumer experience is critical, and internet reviews offer a dependable means to do it. Of course, when a consumer encounters problems, no one expects them to be happy, but it can be positive if the support is speedy and helpful. Social media handles could be effectively used to understand the feedback and complaints of customers. Responding to them promptly makes the customers feel good. It also, gives an idea of the expectations of the customers and the new product ideals. Analyzing the Customer Sentiments Whether you're selling a handbag or consumer software on the App Store, text analytics may help you categorize reviews quickly. Unfortunately, a spreadsheet and hours of reading and categorizing reviews are generally required for the manual option. Aside from the discomfort of working long hours, we frequently find irregularities due to the physical nature of this labor. So why not create a data categorization and scoring model that you can use to rerun the data daily, weekly, or monthly? Summing Up Companies now have many options to perform text analysis thanks to the rise and availability of unstructured text data. However, simply wanting to use text analytics and predictive analytics isn't enough. You need to first understand where you are as a company from an analytical point of view, and then you need to create a plan on how to embrace these new opportunities. Understanding where you are now can help you determine your next steps and protect you from taking on more than you can handle. Frequently Asked Questions How do companies use text analytics? Text analytics is being used by businesses to analyze consumer comments, evaluate client interactions, assess claims, and uncover compliance concerns. Text analytics software based on natural language processing (NLP) can be used to quickly scan internal legal documents for words and phrases related to finance or fraud. What can text analytics be used for? Text analytics is used to gain deeper insights from unstructured text. For example, it can help you see a pattern or trend. Can business intelligence be improved through text analytics? Text analytics can help you understand trends, patterns, and actionable insights that you can apply to make data-driven decisions. It can be done by combining the findings of text analysis with business intelligence tools to put the numbers into easy-to-understand reports and images.

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Big Data Management, Data Science, Big Data

Thinking Like a Data Scientist

Article | May 16, 2023

Introduction Nowadays, everyone with some technical expertise and a data science bootcamp under their belt calls themselves a data scientist. Also, most managers don't know enough about the field to distinguish an actual data scientist from a make-believe one someone who calls themselves a data science professional today but may work as a cab driver next year. As data science is a very responsible field dealing with complex problems that require serious attention and work, the data scientist role has never been more significant. So, perhaps instead of arguing about which programming language or which all-in-one solution is the best one, we should focus on something more fundamental. More specifically, the thinking process of a data scientist. The challenges of the Data Science professional Any data science professional, regardless of his specialization, faces certain challenges in his day-to-day work. The most important of these involves decisions regarding how he goes about his work. He may have planned to use a particular model for his predictions or that model may not yield adequate performance (e.g., not high enough accuracy or too high computational cost, among other issues). What should he do then? Also, it could be that the data doesn't have a strong enough signal, and last time I checked, there wasn't a fool-proof method on any data science programming library that provided a clear-cut view on this matter. These are calls that the data scientist has to make and shoulder all the responsibility that goes with them. Why Data Science automation often fails Then there is the matter of automation of data science tasks. Although the idea sounds promising, it's probably the most challenging task in a data science pipeline. It's not unfeasible, but it takes a lot of work and a lot of expertise that's usually impossible to find in a single data scientist. Often, you need to combine the work of data engineers, software developers, data scientists, and even data modelers. Since most organizations don't have all that expertise or don't know how to manage it effectively, automation doesn't happen as they envision, resulting in a large part of the data science pipeline needing to be done manually. The Data Science mindset overall The data science mindset is the thinking process of the data scientist, the operating system of her mind. Without it, she can't do her work properly, in the large variety of circumstances she may find herself in. It's her mindset that organizes her know-how and helps her find solutions to the complex problems she encounters, whether it is wrangling data, building and testing a model or deploying the model on the cloud. This mindset is her strategy potential, the think tank within, which enables her to make the tough calls she often needs to make for the data science projects to move forward. Specific aspects of the Data Science mindset Of course, the data science mindset is more than a general thing. It involves specific components, such as specialized know-how, tools that are compatible with each other and relevant to the task at hand, a deep understanding of the methodologies used in data science work, problem-solving skills, and most importantly, communication abilities. The latter involves both the data scientist expressing himself clearly and also him understanding what the stakeholders need and expect of him. Naturally, the data science mindset also includes organizational skills (project management), the ability to work well with other professionals (even those not directly related to data science), and the ability to come up with creative approaches to the problem at hand. The Data Science process The data science process/pipeline is a distillation of data science work in a comprehensible manner. It's particularly useful for understanding the various stages of a data science project and help plan accordingly. You can view one version of it in Fig. 1 below. If the data science mindset is one's ability to navigate the data science landscape, the data science process is a map of that landscape. It's not 100% accurate but good enough to help you gain perspective if you feel overwhelmed or need to get a better grip on the bigger picture. Learning more about the topic Naturally, it's impossible to exhaust this topic in a single article (or even a series of articles). The material I've gathered on it can fill a book! If you are interested in such a book, feel free to check out the one I put together a few years back; it's called Data Science Mindset, Methodologies, and Misconceptions and it's geared both towards data scientist, data science learners, and people involved in data science work in some way (e.g. project leaders or data analysts). Check it out when you have a moment. Cheers!

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Data Science

Man Vs. Machine: Peaking into the Future of Artificial Intelligence

Article | March 15, 2021

Stephen Hawking, one of the finest minds to have ever lived, once famously said, “AI is likely to be either the best or the worst thing to happen to humanity.” This is of course true, with valid arguments both for and against the proliferation of AI. As a practitioner, I have witnessed the AI revolution at close quarters as it unfolded at breathtaking pace over the last two decades. My personal view is that there is no clear black and white in this debate. The pros and cons are very contextual – who is developing it, for what application, in what timeframe, towards what end? It always helps to understand both sides of the debate. So let’s try to take a closer look at what the naysayers say. The most common apprehensions can be clubbed into three main categories: A. Large-scale Unemployment: This is the most widely acknowledged of all the risks of AI. Technology and machines replacing humans for doing certain types of work isn’t new. We all know about entire professions dwindling, and even disappearing, due to technology. Industrial Revolution too had led to large scale job losses, although many believe that these were eventually compensated for by means of creating new avenues, lowering prices, increasing wages etc. However, a growing number of economists no longer subscribe to the belief that over a longer term, technology has positive ramifications on overall employment. In fact, multiple studies have predicted large scale job losses due to technological advancements. A 2016 UN report concluded that 75% of jobs in the developing world are expected to be replaced by machines! Unemployment, particularly at a large scale, is a very perilous thing, often resulting in widespread civil unrest. AI’s potential impact in this area therefore calls for very careful political, sociological and economic thinking, to counter it effectively. B. Singularity: The concept of Singularity is one of those things that one would have imagined seeing only in the pages of a futuristic Sci-Fi novel. However, in theory, today it is a real possibility. In a nutshell, Singularity refers to that point in human civilization when Artificial Intelligence reaches a tipping point beyond which it evolves into a superintelligence that surpasses human cognitive powers, thereby potentially posing a threat to human existence as we know it today. While the idea around this explosion of machine intelligence is a very pertinent and widely discussed topic, unlike the case of technology driven unemployment, the concept remains primarily theoretical. There is as yet no consensus amongst experts on whether this tipping point can ever really be reached in reality. C. Machine Consciousness: Unlike the previous two points, which can be regarded as risks associated with the evolution of AI, the aspect of machine consciousness perhaps is best described as an ethical conundrum. The idea deals with the possibility of implanting human-like consciousness into machines, taking them beyond the realm of ‘thinking’ to that of ‘feeling, emotions and beliefs’. It’s a complex topic and requires delving into an amalgamation of philosophy, cognitive science and neuroscience. ‘Consciousness’ itself can be interpreted in multiple ways, bringing together a plethora of attributes like self-awareness, cause-effect in mental states, memory, experiences etc. To bring machines to a state of human-like consciousness would entail replicating all the activities that happen at a neural level in a human brain – by no means a meagre task. If and when this were to be achieved, it would require a paradigm shift in the functioning of the world. Human society, as we know it, will need a major redefinition to incorporate machines with consciousness co-existing with humans. It sounds far-fetched today, but questions such as this need pondering right now, so as to be able to influence the direction in which we move when it comes to AI and machine consciousness, while things are still in the ‘design’ phase so to speak. While all of the above are pertinent questions, I believe they don’t necessarily outweigh the advantages of AI. Of course, there is a need to address them systematically, control the path of AI development and minimize adverse impact. In my opinion, the greatest and most imminent risk is actually a fourth item, not often taken into consideration, when discussing the pitfalls of AI. D. Oligarchy: Or to put it differently, the question of control. Due to the very nature of AI – it requires immense investments in technology and science – there are realistically only a handful of organizations (private or government) that can make the leap into taking AI into the mainstream, in a scalable manner, and across a vast array of applications. There is going to be very little room for small upstarts, however smart they might be, to compete at scale against these. Given the massive aspects of our lives that will likely be steered by AI enabled machines, those who control that ‘intelligence’ will hold immense power over the rest of us. That all familiar phrase ‘with great power, comes great responsibility’ will take a whole new meaning – the organizations and/or individuals that are at the forefront of the generally available AI applications would likely have more power than the most despotic autocrats in history. This is a true and real hazard, aspects of which are already becoming areas of concern in the form of discussions around things like privacy. In conclusion, AI, like all major transformative events in human history, is certain to have wide reaching ramifications. But with careful forethought these can be addressed. In the short to medium term, the advantages of AI in enhancing our lives, will likely outweigh these risks. Any major conception that touches human lives in a broad manner, if not handled properly, can pose immense danger. The best analogy I can think of is religion – when not channelled appropriately, it probably poses a greater threat than any technological advancement ever could.

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Big Data Management, Data Science, Big Data

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

Article | April 28, 2023

In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting. The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’. The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience. Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years. Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online. We have highlighted the steps you need for successful data-driven marketing. 5 Steps to Take for Successful Data-Driven Content Marketing Layout Objectives For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform. For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales. If you're not putting out relevant content in relevant places, you don't exist. Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality. Customize Campaigns for Target Audience Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them. Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where. Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts. Regular Content Optimization One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day. Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment. Keep running SEO audits on your pages regularly to keep your content in the best shape possible. Content Repurposing Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness. Track Analytics Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track. There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives. Final Thoughts In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns. Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges. Frequently Asked Questions What is the future of content marketing? Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business. What are the top content marketing trends for 2022? 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics How is content-based marketing a proven strategy? Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the future of content marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business." } },{ "@type": "Question", "name": "What are the top content marketing trends for 2022?", "acceptedAnswer": { "@type": "Answer", "text": " A. 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics" } },{ "@type": "Question", "name": "How is content-based marketing a proven strategy?", "acceptedAnswer": { "@type": "Answer", "text": "Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness." } }] }

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Business Intelligence, Big Data Management, Data Science

Navigating Big Data Integration: Challenges and Strategies

Article | April 13, 2023

“Text analytics can help organizations discover patterns in large unstructured data sets. Unstructured data, such as videos, photos, and audio accounts for at least 80% of your company’s data, a true blind spot for most businesses.” - IBM Data scientists use advanced data science approaches to examine text. This textual data provides a better understanding of client attitudes toward certain topics or uncovers additional information. Using this text analytics, you can turn free-form text into structured data for use in prediction models or uncover hidden patterns in your data. If you want, you can use text analysis to identify prospective customers who might be interested in cross-selling, forecast customer attitudes, and understand fraud-prevention behaviors. Businesses understand the value of their raw text across all industries. As a result, with the help of data, they can reduce operational expenses, find previously unknown linkages, and get a better insight into the future trends. Is it hard to comprehend that text accounts for 80% of all corporate data? Online reviews, call center transcripts, consumer surveys, and other written documents are examples. This raw text data is a gold mine for understanding customer attitudes. Text mining and analytics transform these underutilized data sources into actionable information. However, each organization must have the expertise, infrastructure, and analytical perspective to implement this cutting-edge technology in their own way. How is Text Analytics Used in Companies? Companies can use NLP and untapped data sources in a number of ground-breaking ways. Many businesses are already successfully employing text to drive their operations. In addition, text analytics can help you improve your procedures if you're transitioning from business intelligence reporting to data science. Best Five Text Analytics Use Cases for Businesses Companies and people, regardless of industry, desire to make better-informed business decisions based on trackable and measurable data. Thanks to improvements in text analysis, companies can now mine the text for insights and improve their service or offering to thrive in their industry. Read on to understand some of the text analytics use cases that could be applied in your company. Voice of the Customer (VOC) to Extract Customer Opinion on a product Companies employ VOC applications to determine what customers say about a product or service. Emails, call center logs, surveys, and social media streams such as blogs, tweets, forum postings, newsfeeds, and so on are examples of data sources. A telecommunications company, for example, might use voice of customer text analysis to look for complaints about their online services on Twitter. It will give them an early warning when customers aren’t happy with the service's performance, so that they can act before the client calls to complain or publicly ask for the contract to be terminated. Lead Generation through Social Media A piece of social media information can be used to retain and get new clients. It is like the use of the VOC application. For example, if a person tweets that they are interested in a particular product or service, text analytics can detect this and pass the information to a sales representative, who can then pursue the prospect and turn them into a customer. Finding Out What Customers Value through Market Research According to numerous statistics, consumers are interested in other people's thoughts and experiences. According to a study, at least 90% of humans are influenced by what they read. Also, if the review is terrible, the sentiment is shared. In the last few years, several websites have been collecting reviews of local eateries, vacation spots, and, of course, commercial products. If your company is thinking about going into a new market or looking into new product ideas, why not start by looking into online market research reviews from real people? Market research helps you know what features are important to customers when you start your marketing effort. It's critical to know which characteristics influence purchasing decisions and contribute to customer unhappiness. Use Customer Complaints to Identify New Product Ideals Understanding the consumer experience is critical, and internet reviews offer a dependable means to do it. Of course, when a consumer encounters problems, no one expects them to be happy, but it can be positive if the support is speedy and helpful. Social media handles could be effectively used to understand the feedback and complaints of customers. Responding to them promptly makes the customers feel good. It also, gives an idea of the expectations of the customers and the new product ideals. Analyzing the Customer Sentiments Whether you're selling a handbag or consumer software on the App Store, text analytics may help you categorize reviews quickly. Unfortunately, a spreadsheet and hours of reading and categorizing reviews are generally required for the manual option. Aside from the discomfort of working long hours, we frequently find irregularities due to the physical nature of this labor. So why not create a data categorization and scoring model that you can use to rerun the data daily, weekly, or monthly? Summing Up Companies now have many options to perform text analysis thanks to the rise and availability of unstructured text data. However, simply wanting to use text analytics and predictive analytics isn't enough. You need to first understand where you are as a company from an analytical point of view, and then you need to create a plan on how to embrace these new opportunities. Understanding where you are now can help you determine your next steps and protect you from taking on more than you can handle. Frequently Asked Questions How do companies use text analytics? Text analytics is being used by businesses to analyze consumer comments, evaluate client interactions, assess claims, and uncover compliance concerns. Text analytics software based on natural language processing (NLP) can be used to quickly scan internal legal documents for words and phrases related to finance or fraud. What can text analytics be used for? Text analytics is used to gain deeper insights from unstructured text. For example, it can help you see a pattern or trend. Can business intelligence be improved through text analytics? Text analytics can help you understand trends, patterns, and actionable insights that you can apply to make data-driven decisions. It can be done by combining the findings of text analysis with business intelligence tools to put the numbers into easy-to-understand reports and images.

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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

Introduction Nowadays, everyone with some technical expertise and a data science bootcamp under their belt calls themselves a data scientist. Also, most managers don't know enough about the field to distinguish an actual data scientist from a make-believe one someone who calls themselves a data science professional today but may work as a cab driver next year. As data science is a very responsible field dealing with complex problems that require serious attention and work, the data scientist role has never been more significant. So, perhaps instead of arguing about which programming language or which all-in-one solution is the best one, we should focus on something more fundamental. More specifically, the thinking process of a data scientist. The challenges of the Data Science professional Any data science professional, regardless of his specialization, faces certain challenges in his day-to-day work. The most important of these involves decisions regarding how he goes about his work. He may have planned to use a particular model for his predictions or that model may not yield adequate performance (e.g., not high enough accuracy or too high computational cost, among other issues). What should he do then? Also, it could be that the data doesn't have a strong enough signal, and last time I checked, there wasn't a fool-proof method on any data science programming library that provided a clear-cut view on this matter. These are calls that the data scientist has to make and shoulder all the responsibility that goes with them. Why Data Science automation often fails Then there is the matter of automation of data science tasks. Although the idea sounds promising, it's probably the most challenging task in a data science pipeline. It's not unfeasible, but it takes a lot of work and a lot of expertise that's usually impossible to find in a single data scientist. Often, you need to combine the work of data engineers, software developers, data scientists, and even data modelers. Since most organizations don't have all that expertise or don't know how to manage it effectively, automation doesn't happen as they envision, resulting in a large part of the data science pipeline needing to be done manually. The Data Science mindset overall The data science mindset is the thinking process of the data scientist, the operating system of her mind. Without it, she can't do her work properly, in the large variety of circumstances she may find herself in. It's her mindset that organizes her know-how and helps her find solutions to the complex problems she encounters, whether it is wrangling data, building and testing a model or deploying the model on the cloud. This mindset is her strategy potential, the think tank within, which enables her to make the tough calls she often needs to make for the data science projects to move forward. Specific aspects of the Data Science mindset Of course, the data science mindset is more than a general thing. It involves specific components, such as specialized know-how, tools that are compatible with each other and relevant to the task at hand, a deep understanding of the methodologies used in data science work, problem-solving skills, and most importantly, communication abilities. The latter involves both the data scientist expressing himself clearly and also him understanding what the stakeholders need and expect of him. Naturally, the data science mindset also includes organizational skills (project management), the ability to work well with other professionals (even those not directly related to data science), and the ability to come up with creative approaches to the problem at hand. The Data Science process The data science process/pipeline is a distillation of data science work in a comprehensible manner. It's particularly useful for understanding the various stages of a data science project and help plan accordingly. You can view one version of it in Fig. 1 below. If the data science mindset is one's ability to navigate the data science landscape, the data science process is a map of that landscape. It's not 100% accurate but good enough to help you gain perspective if you feel overwhelmed or need to get a better grip on the bigger picture. Learning more about the topic Naturally, it's impossible to exhaust this topic in a single article (or even a series of articles). The material I've gathered on it can fill a book! If you are interested in such a book, feel free to check out the one I put together a few years back; it's called Data Science Mindset, Methodologies, and Misconceptions and it's geared both towards data scientist, data science learners, and people involved in data science work in some way (e.g. project leaders or data analysts). Check it out when you have a moment. Cheers!

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Data Science

Man Vs. Machine: Peaking into the Future of Artificial Intelligence

Article | March 15, 2021

Stephen Hawking, one of the finest minds to have ever lived, once famously said, “AI is likely to be either the best or the worst thing to happen to humanity.” This is of course true, with valid arguments both for and against the proliferation of AI. As a practitioner, I have witnessed the AI revolution at close quarters as it unfolded at breathtaking pace over the last two decades. My personal view is that there is no clear black and white in this debate. The pros and cons are very contextual – who is developing it, for what application, in what timeframe, towards what end? It always helps to understand both sides of the debate. So let’s try to take a closer look at what the naysayers say. The most common apprehensions can be clubbed into three main categories: A. Large-scale Unemployment: This is the most widely acknowledged of all the risks of AI. Technology and machines replacing humans for doing certain types of work isn’t new. We all know about entire professions dwindling, and even disappearing, due to technology. Industrial Revolution too had led to large scale job losses, although many believe that these were eventually compensated for by means of creating new avenues, lowering prices, increasing wages etc. However, a growing number of economists no longer subscribe to the belief that over a longer term, technology has positive ramifications on overall employment. In fact, multiple studies have predicted large scale job losses due to technological advancements. A 2016 UN report concluded that 75% of jobs in the developing world are expected to be replaced by machines! Unemployment, particularly at a large scale, is a very perilous thing, often resulting in widespread civil unrest. AI’s potential impact in this area therefore calls for very careful political, sociological and economic thinking, to counter it effectively. B. Singularity: The concept of Singularity is one of those things that one would have imagined seeing only in the pages of a futuristic Sci-Fi novel. However, in theory, today it is a real possibility. In a nutshell, Singularity refers to that point in human civilization when Artificial Intelligence reaches a tipping point beyond which it evolves into a superintelligence that surpasses human cognitive powers, thereby potentially posing a threat to human existence as we know it today. While the idea around this explosion of machine intelligence is a very pertinent and widely discussed topic, unlike the case of technology driven unemployment, the concept remains primarily theoretical. There is as yet no consensus amongst experts on whether this tipping point can ever really be reached in reality. C. Machine Consciousness: Unlike the previous two points, which can be regarded as risks associated with the evolution of AI, the aspect of machine consciousness perhaps is best described as an ethical conundrum. The idea deals with the possibility of implanting human-like consciousness into machines, taking them beyond the realm of ‘thinking’ to that of ‘feeling, emotions and beliefs’. It’s a complex topic and requires delving into an amalgamation of philosophy, cognitive science and neuroscience. ‘Consciousness’ itself can be interpreted in multiple ways, bringing together a plethora of attributes like self-awareness, cause-effect in mental states, memory, experiences etc. To bring machines to a state of human-like consciousness would entail replicating all the activities that happen at a neural level in a human brain – by no means a meagre task. If and when this were to be achieved, it would require a paradigm shift in the functioning of the world. Human society, as we know it, will need a major redefinition to incorporate machines with consciousness co-existing with humans. It sounds far-fetched today, but questions such as this need pondering right now, so as to be able to influence the direction in which we move when it comes to AI and machine consciousness, while things are still in the ‘design’ phase so to speak. While all of the above are pertinent questions, I believe they don’t necessarily outweigh the advantages of AI. Of course, there is a need to address them systematically, control the path of AI development and minimize adverse impact. In my opinion, the greatest and most imminent risk is actually a fourth item, not often taken into consideration, when discussing the pitfalls of AI. D. Oligarchy: Or to put it differently, the question of control. Due to the very nature of AI – it requires immense investments in technology and science – there are realistically only a handful of organizations (private or government) that can make the leap into taking AI into the mainstream, in a scalable manner, and across a vast array of applications. There is going to be very little room for small upstarts, however smart they might be, to compete at scale against these. Given the massive aspects of our lives that will likely be steered by AI enabled machines, those who control that ‘intelligence’ will hold immense power over the rest of us. That all familiar phrase ‘with great power, comes great responsibility’ will take a whole new meaning – the organizations and/or individuals that are at the forefront of the generally available AI applications would likely have more power than the most despotic autocrats in history. This is a true and real hazard, aspects of which are already becoming areas of concern in the form of discussions around things like privacy. In conclusion, AI, like all major transformative events in human history, is certain to have wide reaching ramifications. But with careful forethought these can be addressed. In the short to medium term, the advantages of AI in enhancing our lives, will likely outweigh these risks. Any major conception that touches human lives in a broad manner, if not handled properly, can pose immense danger. The best analogy I can think of is religion – when not channelled appropriately, it probably poses a greater threat than any technological advancement ever could.

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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.

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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

NICE Actimize X-Sight DataIQ ClarityKYC Wins Best Data Solution for Regulatory Compliance in A-Team Group’s 2023 Data Management Insight Awards

Business Wire | November 01, 2023

NICE Actimize, (Nasdaq: NICE) was named a winner in A-Team Group's Data Management Insight Awards USA 2023 in the category for Best Data Solution for Regulatory Compliance. NICE Actimize’s X-Sight DataIQ ClarityKYC was the recipient of the most online votes in its category derived from reader/online nominations from within the data management community and verified by A-Team Group editors and its advisory board. NICE Actimize’s X-Sight DataIQ ClarityKYC is a SaaS workflow solution that automates data aggregation and simplifies KYC for financial services organization users. The solution facilitates compliance with KYC/Anti-Money Laundering (AML) requirements by integrating disparate datasets and streamlining the customer identification, due diligence, and credit investigation process. Customer onboarding is a critical first step in any financial services organization’s risk management strategy. Onboarding new customers and conducting ongoing reviews presents numerous competitive challenges, which include manual and error-prone processes, long onboarding times which result in longer time to revenue for the banks, and no practical way to make sure the bank’s global regulatory policies are met in an auditable process, said Craig Costigan, CEO, NICE Actimize. NICE Actimize’s DataIQ ClarityKYC addresses these issues effectively. We thank the A-Team group and the data management community for recognizing the innovation we offer with X-Sight DataIQ. “These awards recognize both established solution vendors and innovative newcomers providing leading data management solutions, services, and consultancy to capital markets participants across North America. Congratulations go to NICE Actimize for winning Best Data Solution for Regulatory Compliance,” said Angela Wilbraham, CEO of A-Team Group and host of the Data Management Insight Awards USA 2023. X-Sight DataIQ ClarityKYC leverages AI-powered technologies to access traditional content while intelligently orchestrating data from various global data sources. X-Sight DataIQ Clarity reduces the amount of effort needed to conduct research. Long IT integration projects and tasks formerly done manually or requiring steps can be completed quickly, automatically saving time and effort while enabling teams to comply with confidence while reducing customer friction.

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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

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

NICE Actimize X-Sight DataIQ ClarityKYC Wins Best Data Solution for Regulatory Compliance in A-Team Group’s 2023 Data Management Insight Awards

Business Wire | November 01, 2023

NICE Actimize, (Nasdaq: NICE) was named a winner in A-Team Group's Data Management Insight Awards USA 2023 in the category for Best Data Solution for Regulatory Compliance. NICE Actimize’s X-Sight DataIQ ClarityKYC was the recipient of the most online votes in its category derived from reader/online nominations from within the data management community and verified by A-Team Group editors and its advisory board. NICE Actimize’s X-Sight DataIQ ClarityKYC is a SaaS workflow solution that automates data aggregation and simplifies KYC for financial services organization users. The solution facilitates compliance with KYC/Anti-Money Laundering (AML) requirements by integrating disparate datasets and streamlining the customer identification, due diligence, and credit investigation process. Customer onboarding is a critical first step in any financial services organization’s risk management strategy. Onboarding new customers and conducting ongoing reviews presents numerous competitive challenges, which include manual and error-prone processes, long onboarding times which result in longer time to revenue for the banks, and no practical way to make sure the bank’s global regulatory policies are met in an auditable process, said Craig Costigan, CEO, NICE Actimize. NICE Actimize’s DataIQ ClarityKYC addresses these issues effectively. We thank the A-Team group and the data management community for recognizing the innovation we offer with X-Sight DataIQ. “These awards recognize both established solution vendors and innovative newcomers providing leading data management solutions, services, and consultancy to capital markets participants across North America. Congratulations go to NICE Actimize for winning Best Data Solution for Regulatory Compliance,” said Angela Wilbraham, CEO of A-Team Group and host of the Data Management Insight Awards USA 2023. X-Sight DataIQ ClarityKYC leverages AI-powered technologies to access traditional content while intelligently orchestrating data from various global data sources. X-Sight DataIQ Clarity reduces the amount of effort needed to conduct research. Long IT integration projects and tasks formerly done manually or requiring steps can be completed quickly, automatically saving time and effort while enabling teams to comply with confidence while reducing customer friction.

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