Sailing Easy in an Ocean of Data

Stephen DeAngelis

September 6, 2018

In order to comprehend how large something is, we often compare it to the size of the ocean. For example, one often reads about companies collecting an ocean of big data; and, it’s a fitting comparison. The vastness of the ocean has always enthralled us. Humorist Dave Barry once wrote, “There’s nothing wrong with enjoying looking at the surface of the ocean itself, except that when you finally see what goes on underwater, you realize that you’ve been missing the whole point of the ocean. Staying on the surface all the time is like going to the circus and staring at the outside of the tent.”[1] The same can be said for companies that never delve deep into the ocean of data they collect. Big data is both wide and deep and skimming the surface never provides the insights that can be obtained through advanced analytics.

The growing ocean of data

Companies are often compared to swimmers in an ocean of data. For example, Hoyoung Pak, head of the transportation practice at Uptake, writes about companies “swimming in data”[2] and Deloitte analysts John Ferraioli (@Ferraioli) and Rick Burke discuss companies “drowning in data, but starving for insights.”[3] To ensure companies don’t drown in an ocean of data, they recommend using advanced analytics. Some people might see advanced analytics as a company life preserver; but, that analogy is too limiting. Actionable insights are never going to be found bobbing in place on the surface. A more apt comparison for advanced analytics would be to a nuclear-powered submarine that can dive deep and travel far. Only that kind of vessel can help companies sail easy in an ever-growing ocean data.

Streams of data continue to flow into the ocean of data every second of every day. Charles McLellan (@charlesmclellan) notes, “The data floodgates are now opening for businesses of all sizes and descriptions, bringing myriad opportunities for timely analysis in pursuit of competitive advantage. Although the focus is currently slanted towards customer behavior, data is available at multiple points in the product or service supply chain, and comes in many forms — traditional (structured), ad hoc (unstructured), real time, and IoT- or M2M-generated, to name but a few.”[4] Ferraioli and Burke assert one of the challenges companies face is organizing their data. “While data exists in every organization,” they explain, “it is often not fully organized or understood. Rather, it may be housed in disparate sources, data marts and warehouses, trapped in systems, and in various formats with limited context. The challenge, then, for some companies is they may not know what data they already have, where it lives, what may be useful, or how to turn it into meaningful insights that they can act upon.” They note that data not only comes from many places, it takes many forms, including:

  • Master data: “Business-critical data that is consumed by applications to enable business processes. It largely relates to material master, supplier master, customer identities, product material specifications, etc.”
  • Transactional data: “Post-business-process information such as purchasing inventory records or sales volumes by region.”
  • Sensor data: “Unstructured data that characterizes the conditions of the enterprise’s physical assets, from voltage to vibration.”
  • Other unstructured data: “Data existing within the organization such as spreadsheets, emails, engineering schematics, drawings, and beyond.”

Pak explains there are different types of advanced analytics that can be used to garner insights and knowledge from data. He specifically cites three types: descriptive analytics, preventive (or diagnostic) analytics, and predictive analytics. Cognitive computing adds a fourth, more advanced, category — prescriptive analytics. Cognitive technologies generally have each of these types of analytics embedded within the system. Each type has its place in helping companies sail easy on the ocean of data.

The usefulness of advanced analytics

Gary Cokins (@GaryCokins), founder of Analytics-Based Performance Management LLC, asserts, “Analytics is becoming a competitive edge for organizations. Once being a ‘nice-to-have,’ applying analytics is now becoming mission-critical.”[5] As noted above, there are four types of analytics companies will find useful.

Descriptive Analytics. Alexander Bekker, Head of Database and BI Department at ScienceSoft, notes descriptive analytics is the simplest analytics to conduct and answers the question of “what happened.”[6] He adds, “Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, highly data-driven companies do not content themselves with descriptive analytics only, and prefer combining it with other types of data analytics.”

Diagnostic Analytics. Diagnostic analytics, Bekker notes, allows a company to use historical data, measured against other data, to answer the question of “why something happened.” He explains, “Thanks to diagnostic analytics, there is a possibility to drill down, to find out dependencies and to identify patterns. Companies go for diagnostic analytics, as it gives a deep insight into a particular problem.”

Predictive Analytics. “Predictive analytics,” Bekker writes, “tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting.” David Weldon (@DWeldon646), editor in chief of Information Management, notes, “As businesses look to cash in on the hype of predictive analytics, artificial intelligence is becoming one of the main drivers behind those efforts.”[7] John Crupi, vice president of IoT analytics at Greenwave Systems, told Weldon predictive analytics can pay big benefits in the area of preventive maintenance. He explained, “AI models can continuously learn as more data arrives and the models refined to achieve increasing accurate results. Predicting abnormalities in minutes or hours in advance can save companies millions of dollars by avoiding catastrophic failures and downtime.”

Prescriptive Analytics. “The purpose of prescriptive analytics,” writes Bekker, “is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend.” Mark van Rijmenam (@VanRijmenam), founder of Datafloq, writes, “Prescriptive analytics comes with some benefits you can leverage with Big Data, such as enhanced awareness of the impact of new technologies or techniques, improved utilization of resources and increased insight into patterns and habits of consumers. For example, you can use prescriptive analytics to determine the best social media engagement opportunities to take.”[8] He adds, “The future of prescriptive analytics will facilitate further analytical development for automated analytics where it replaces the need of human decision-making with automated decision-making for businesses. … With the expanding use and value of prescriptive analytics, it is driving the future of Big Data. Enterprise leaders can avoid risky business moves and reduce financial losses with the power of prescriptive analytics to evolve the logic of their business decisions. When you incorporate prescriptive analytics in your Big Data strategy, it can help you make business decisions faster to enhance efficiency and productivity of your enterprise.”

Summary

McLellan concludes, “Companies that implement big data analytics successfully can reap rich rewards from cost-saving efficiencies and revenue-generating innovations. This can help businesses achieve a digital transformation, allowing them to maintain competitiveness in the face of any disruptive startups — which are data-driven almost by definition — that spring up in their markets.” Without advanced analytics, companies risk being tossed to and fro on an ocean of data without ever moving in the right direction or gaining deeper insights. Isn’t it time you started sailing easy on the ocean of data?

Footnotes
[1] Eric Atlas, “Ocean Quotes 6: Dave Barry,” University of Southampton, 9 February 2014.
[2] Hoyoung Pak, “Swimming in data? 3 types of analytics can help,” Supply Chain Dive, 10 July 2018.
[3] John Ferraioli and Rick Burke, “Drowning in data, but starving for insights,” Deloitte Insights, 11 April 2018.
[4] Charles McLellan, “Turning big data into business insights: The state of play,” ZDNet, 1 September 2017.
[5] Gary Cokins, “Why analytics will be the next competitive edge,” Information Management, 17 November 2017.
[6] Alexander Bekker, “4 types of data analytics to improve decision-making,” ScienceSoft, 11 July 2017.
[7] David Weldon, “AI may hold the key to success with predictive analytics,” Information Management, 14 February 2018.
[8] Mark van Rijmenam, “Why Prescriptive Analytics Is the Future of Big Data,” Datafloq, 5 October 2017.