Think Fast: The Importance of Time-to-Insight in Big Data Analytics

Stephen DeAngelis

March 28, 2016

When you were younger, you probably experienced one of your siblings or friends tossing something at you when you weren’t paying attention and yelling, “Think fast!” It was a warning that something was coming at you and only quick reactions would save you from suffering discomfort, pain, or injury. Today, the business environment is shouting, “Think fast!” Are you paying attention? How is your reaction time? Randy Bean (@RandyBeanNVP), CEO and managing partner of NewVantage Partners, reports that leaders of some of today’s largest companies are paying attention. He writes, “They say that time is money, but Fortune 1000 executives polled in the fourth annual Big Data Executive Survey conducted by NewVantage Partners have boldly confirmed that reducing time-to-insight rather than saving money is the primary driver for their Big Data business investment.”[1] Thanks to emerging technologies (like cognitive computing), business executives now have a reasonable chance of understanding the near-term future as well as the near-term past. As sailors are well aware, you can’t steer by your wake; but, traditional statistical analysis is basically forcing businesses to do just that.

To be fair, understanding the future does require feedback from the past. That feedback comes in the form of data; but, as Robert J. Bowman, Managing Editor of SupplyChainBrain, reports, “Companies are drowning in data today.”[2] He continues, “It’s the age of Big Data, which is supposed to give companies unprecedented control over their supply chains. But how can they even begin to get a handle on this massive amount of information?” Frederic Golin, Vice President and Research Director at Forrester Research, insists that “getting a handle” on big data starts with adopting a new perspective. He explains, “Companies need to approach analytics with a new mindset: The business discipline and technology to harness insights and consistently turn data into effective action.”[3] Bean adds, “Organizations feel a need to learn quickly and act faster. … Business analysts have long been bound by the time it takes to capture, organize, and make data available to non-technical users.” He suggests there are two ways that businesses can speed up: First, accelerate time-to-answer through test-and-learn processes; second, accelerate speed-to-market with data discovery environments. Concerning his first suggestion, Bean writes:

“Big Data processes have consolidated the time it takes to engage in analytics by reducing up-front data engineering and putting data into the hands of business users faster. By starting with smaller sets of data, business analysts can engage in iterative processes such as test-and-learn to identify patterns and correlations that allow them to focus on the most useful data quickly. This ability to accelerate the process of insight is alternately referred to as time-to-answer, time-to-analytics, or time-to-decision. The net result is the realization of greater insight faster.”

The advance of in-memory databases has also helped accelerate time-to-answer analysis. In-memory databases are data management systems that primarily rely on main memory for computer data storage. In-memory databases are faster than disk-optimized databases because internal optimization algorithms are simpler and execute fewer central processing unit instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk. Bean’s second suggestion — creating data discovery environments — takes advantage of things like in-memory databases. He also recommends “the deployment of analytical sandboxes, Big Data labs, data hubs, and data lakes. All of these approaches are designed to introduce greater flexibility and agility into the process of taking data and transforming it into business insights.” Gaurav Rewari (@GRewari), founder and CEO of Numerify, asserts, “For businesses to maintain a competitive advantage, analytics must be used on an ongoing basis.”[4] In other words, they must transform themselves into digital enterprises. And I believe cognitive computing systems will be at the heart of the best digital enterprises. As President and CEO of a cognitive computing firm, you would expect me to believe that. Fortunately, I’m not alone in that opinion. Accenture analysts state that cognitive computing will provide the “ultimate long-term solution” for many business challenges.[5]

Cognitive computing systems are ideal platforms for gathering, integrating, and analyzing both structured and unstructured data. In an earlier article, Bean wrote, “The ability to organize, digest and navigate through vast quantities of disaggregated or unstructured data is a requirement to identify the critical insights and address the most important questions that organizations must answer to make key decisions.”[6] He added, “The ability to accelerate time-to-answer is not only important in accelerating the time in which an organization can move from data-to-decision, but also translates to significant cost savings. Hence, the metric time-to-answer also directly correlates to the time-value-of-money.” Although Bean suggested that time-to-answer could be reduced by analyzing smaller data sets, Rick Delgado (@ricknotdelgado) warns that smaller data sets could result in skewed results.[7] He explains:

“Big data may hold a lot of potential, but it can still be held back if the data being analyzed is inaccurate. Due to restrictions on technology and other business considerations, the analyses companies are getting back may not reflect what is really happening. If businesses want to ensure their big data insights get the desired results, they need to improve the accuracy in their analytics efforts. In a perfect world, organizations would gather a vast amount of data, analyze it, and generate solutions to the problems they’re facing. The truth is, as most know, we do not live in a perfect world. Insights from big data often have to be derived in a short amount of time. The technology a business has on hand might not be advanced enough to process so much information quickly. These restrictions lead many companies to performing big data analytics using sampling. In other words, they don’t look at all of the data, but rather analyze only smaller subsamples of information. While this might be a go-to strategy for many businesses, the results have a greater chance of being inaccurate. Since it is vital for organizations to build accurate big data models, only looking at part of the data could lead to businesses forming the wrong conclusions.”

Rewari notes, “Many businesses cite a lack of tools and resources as barriers to using data to enhance performance, cost and capabilities.” He goes on to note, “There are exciting new solutions emerging.” Thanks to those emerging solutions, actionable insights can now provide value to almost any sized company. Dave Schubmehl (@dschubmehl), Research Director at IDC, concludes, “Many organizations often don’t realize how much value there might be in all of the content in their various information sources. But those organizations that do recognize this potential are generating insights from their own internal big data and advanced analytics techniques. In addition, they are turning those insights into increased revenue, improved knowledge worker productivity, and lower costs. … Taking advantage of your own big data can yield immediate and tangible benefits to your organization. Insights and actionable knowledge are the lifeblood of many organizations, and leveraging all of your information silos can improve productivity, help contain costs, increase innovation, and increase revenue by using what you already own, but are not making use of.”[8]

Footnotes
[1] Randy Bean, “How Time-to-Insight Is Driving Big Data Business Investment,” MIT Sloan Management Review, 26 January 2016.
[2] Robert J. Bowman, “CPG Suppliers: Drowning in the Sea of Big Data,” SupplyChainBrain, 14 April 2014.
[3] Frederic Golin, “Predictive Analytics Requires a Customer-Obsessed Innovation Culture,” Information Management, 14 December 2015.
[4] Gaurav Rewari, “The IT Moneyball: Using Analytics to Win the Digital Business Game,” Information Management, 14 December 2015.
[5] “From Digitally Disrupted to Digital Disrupter,” Accenture, 2014.
[6] Randy Bean, “Using Big Data to Accelerate Time-to-Answer,” CMS Wire, 24 July 2012.
[7] Rick Delgado, “Improving the Accuracy of Big Data Analysis,” Dataconomy, 31 October 2015.
[8] Dave Schubmehl, “Insights You Weren’t Expecting from Big Data,” CIO, 27 October 2015.