Big Data + Advanced Analytics = Big Value

Stephen DeAngelis

April 7, 2020

Businesses are accumulating data at increasing rates and they understand data lying fallow in a database provides them with little to no tangible value. Asim Rais Siddiqui (@asimrs) Co-Founder & CTO at TekRevol, rhetorically asks, “Does your business really require big data analytics?”[1] His answer is an unqualified, “Yes.” You’ve probably heard the analogy that data is the new oil. Siddiqui believes analyzed data is the new oil. He quotes Peter Sondergaard (@PeterSonderg), a former executive at Gartner and now head of The Sondergaard Group, who stated, “Information is the oil of the 21st century, and analytics is the combustion engine.” The editorial staff at CIO Applications notes, “Big data and data analytics have become the vogue words of the present-age business industry in a short time. They have made the vital operations convenient for businesses, which improves the performance as well as helps in achieving a superior degree of efficiency by the wide-ranging statistical insights.”[2] Nevertheless, they add, “Many organizations lack in the implementation of technologies.” A Forrester report provides evidence many companies are struggling to leverage their data.[3] The report found 41% of businesses struggle to turn data into decisions.


The value of advanced analytics


I like Sondergaard’s analogy of data as oil and analytics as the engine. Another way of stating the importance of analytics is that analytics can turn data into decisions. Siddiqui discusses several ways advanced analytics can help organizations. They include:


1. Improved decision making. Siddiqui writes, “Through a powerful combination of speed and efficiency, big data allows businesses to analyze information immediately. … From prevailing market trends to customer preferences and cost management, it allows you to scrutinize and compare minute details for each factor, before making key decisions.” Bain analysts, Michael C. Mankins and Lori Sherer (), add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”[4]


2. Risk analysis. Advanced analytics platforms can provide both predictive and prescriptive insights. These insights can help reduce decision-making risks. Siddiqui explains, “Taking any initiative in your business always involves a risk. … Because big data allows you to analyze large amounts of information in detail, you are given the opportunity to consider influencing factors thoroughly before making a final decision.”


3. Effective marketing. Marketers ultimate goal is to get the right message to the right person at the right time. Siddiqui notes, “Using big data, you will be able to compare results from different marketing strategies and create more effective marketing campaigns, based on the analysis. Another way big data analytics helps marketing is through providing businesses with better insights about their clients. This allows the production of specifically targeted advertisements and greater levels of personalization.”


4. Cost reduction. One of the big promises of advanced analytics is improved process optimization and efficiency. Siddiqui explains, “Big data helps cut down costs by streamlining processes and improving operational efficiency. Implementation and results can vary from industry to industry, but big data analytics can be used to identify trends, patterns, and probabilities in incurring costs.”


David Dixon, analytics practice lead at, predicts, “Data analytics will become more embedded in day-to-day processes specifically to provide analytics and AI insights within the flow of business, in the context of where decisions are being made and enabling a seamless flow from insight to action.”[5] He believes three things will help improve analytics for companies. They are:


  • Improved Natural Language Processing. He writes, “In 2020, we will see significant advancements in both natural language queries and conversational analytics which will further help in the democratization of analytics. The promise of self-serve analytics has been around for a while now, but has never fully materialized. Natural language processing advancements, along with AI-powered query construction can help bridge that gap.”
  • Data science and traditional data analytics will converge. According to Dixon, “Traditionally, business users have had to interpret analytics and decide what course of action to take. This will begin to change. This will begin to change as data science and machine learning are utilized to produce models that provide prescriptive actions to business users rather than the users having to interpret analytics themselves.”
  • Cognitive technologies will be embedded within analytics solutions. Dixon explains, “Analytics and machine learning/AI are becoming more commonly embraced in business operations and in 2020, we hope to see more leading companies adopting it. With increased reliance on new tools for data analytics, coupled with an accelerated need to access external data stores, networks and IoT devices, interconnectivity will be a key to building a cohesive data analytics machine for your business.”


Improving advanced analytics implementation


Bill Franks (@billfranksga), Chief Analytics Officer for The International Institute For Analytics (IIA), suggests asking twenty questions before embarking on any new project, especially analytic projects. He explains, “For scoping analytics, start with obvious questions such as ‘Is the goal to predict something?’, ‘Is the goal to optimize something?’, or ‘Are you trying to automate an ongoing decision?’.”[6] He continues, “While it is true that 20 questions will be enough to get to the heart of a matter in most cases, it isn’t possible to specify a magical list of 20 specific questions that will work each time. It will take a different mix of 20 questions for every case, even if there is a common structure and logic to those questions.” A smart, diversified group of people should be able to develop the right list of questions. You might call this game of twenty questions strategizing. Which is the activity Michael Zeller (@michaelzeller), CEO of Dynam.AI, recommends as the first step for any analytics project. He suggests companies should follow four steps if they are “looking to turn big data into big value.”[7] Those steps are:


Step 1. Strategize: Zeller writes, “Any changes in data strategy will require commitment from the executive team for up-front investment and room for experimentation through a few initial projects. Not all projects will succeed, but an agile project approach will allow you to fail fast and correct course quickly.”


Step 2. Prepare: “Create a joint task force of business domain experts and data scientists to identify and prioritize the highest value projects,” Zeller suggests. “If you don’t have the data science talent in-house, find a trusted partner to conduct the first projects hand in hand with the business stakeholder.” This would be the group to develop Franks’ twenty questions.


Step 3. Execute: “Once you identify the high-value AI-enabled solutions that can transform your business,” Zeller writes, “then it is time to be bold. Prioritize time-to-market over perfection and empower the joint task force of business domain experts and data scientists to run the project from beginning to end with strong support from the executive team.”


Step 4. Augment: “Afterwards,” Zeller writes, “share the use cases internally for other teams to learn, invest to accelerate adoption. Remember, the operational deployment of AI as the ultimate goal since that is where the true ROI of AI will emerge.”


He concludes, “While (big) data serves as the foundation, smarter, data-driven decisions deliver the business value.”


Concluding thoughts


Siddiqui concludes, “Effective incorporation of big data analytics, once paired with management wisdom, has the potential to transform businesses for the better. To keep up with the pace of today’s business world, big data analytics is no longer an option, but a necessity.” Roberto Torres (@TorresLuzardo) reminds us technology is only part of a successful advanced analytics implementation; people matter as well. He explains, “Companies with successful digital transformation projects make talent a key part of their approach, retooling workforce development strategies to better suit business needs.” As Mankins and Sherer asserted, decisions matter and advanced analytics help companies make better decisions.


[1] Asim Rais Siddiqui, “Does Your Business Really Require Big Data Analytics?Customer Think, 26 August 2019.
[2] Staff, “Brace Yourself for Big Data Analytics!CIO Applications, 16 July 2019.
[3] Roberto Torres, “41% of businesses struggle to turn data into decisions,” CIO Dive, 24 February 2020.
[4] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[5] David Dixon, “3 top ways data analytics will impact business strategies in 2020,” Information Management, 11 February 2020 (out of print).
[6] Bill Franks, “20 Questions That Make Any Analytics Project Successful,” International Institute for Analytics, 9 January 2020.
[7] Michael Zeller, “How to Translate Big Data into Big Business Value,” insideBIGDATA, 21 February 2020.
[8] Torres, op. cit.