Big Data Analytics Can Help Improve the Bottom Line

Stephen DeAngelis

November 17, 2015

“There is irony behind a discussion of the business value and metrics related to big data analytics, since a lot of people in the business world still don’t understand much about big data,” writes Julie Hunt (), a Consultant & Analyst for B2B Software Solutions, “including those who think they have a handle on it.”[1] Hunt is correct. The term “big data analytics” refers to a lot more than the amount data a company has at its disposal to analyze. Both the right data and the right analytics are required for success. The goal, of course, is to discover actionable insights (sometimes called business intelligence) that lead to more effective operations and a better bottom line.

 

Donna Fritz (@TSC_Donna), Vice President of Product Management at Take Supply Chain, told Hailey Lynne McKeefry (@HaileyMcK), Editor in Chief of EBN, “Organizations have to learn how to pull the data spread through the organization together and what to do with all that data once they have it in one place. There comes a point that there is a realization that they can’t do more with just addressing cost centers or siloes.”[2] Organizational units too often maintain their own data (i.e., their own version of the truth) which makes corporate alignment and collaboration difficult. Breaking down silos by integrating data and providing a single version of truth is necessary if organizations want to transform into digital enterprises — something most analysts agree they need to do in order to be successful in the decades ahead. Ed Burns (@EdBurnsTT) notes, “Organizations looking to analyze big data typically have to pull it together from various systems, making data integration a fundamental component of a big data analytics platform.”[3] Data integration is never easy; but, cognitive computing systems (the latest iteration of artificial intelligence systems) are more adept at handling structured and unstructured data than previous systems. As a result, data integration and big data analytics are handled much better by cognitive computing systems than by previous IT systems.

 

The term “big data” may have been overhyped and promised benefits haven’t been as widespread as expected; but, Hunt insists companies should blame themselves not the data. She explains:

“Numerous misconceptions about big data — what it is, why it matters — make it difficult for organizations to know what to do with it and to understand the business value that can be acquired. Some organizations have been too quick to declare that big data is only hype or a big flop. Frankly, the same can be said for any business technology initiative: if the right approach and effort aren’t undertaken, if participants don’t understand why they’re working with it, if beneficial strategies aren’t in place. Big data is not just one thing and there’s not just one application for it. If big data analytics aren’t producing good results, it’s not the fault of the big data. Just like working with any data, you have to know what you want to do and why; you have to experiment and learn from the approaches that don’t work; you have to adhere to continuous improvement, identify other needed data sources and so on. It’s a very good idea for organizations to pursue an understanding of big data and where it might produce value for current circumstances and for future direction.”

The most important thing to take away from Hunt’s comments is that knowing what you want to do with big data is the best place to start. And knowing what you want to do starts with asking the right questions. “Some organizations are driving more value out of big data than others,” writes Lisa Morgan (@lisamorgan). “They’re the ones redefining how businesses interact with their customers. They’re the ones using data to transform their business models and to innovate.”[4] She goes on to point out that just as important as the data are the questions that are asked of that data. She explains:

“Asking better questions of data is both an art and a science, and it’s an iterative process. The most sophisticated and competitive companies are constantly striving to improve their understanding of what data can tell them, and what they can ask of the data.”

In addition to helping integrate and analyze data, some cognitive computing systems can actually assist in asking the right questions. For example, the Enterra Enterprise Cognitive System™ uses a hypothesis engine to help users formulate the right questions to achieve the best answers. It also has embedded within it three kinds of expertise:

 

  • Business domain expertise – which can help explain the drivers behind data anomalies and outliers.
  • Statistical expertise – to help formulate the correct statistical studies.
  • Data science expertise – to understand where and how to pull the data from across multiple databases or data feeds.

 

Eliminating the need for three experts dramatically decreases the time required to analyze, tune, re-analyze, and interpret the results. Enterra’s approach empowers the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Even some of the business expert’s logic is automated to help tune and re-analyze the data.

 

Boris Evelson (@bevelson), a principal analyst at Forrester Research, offers eight steps companies can use to get started on the road to better big data analytics.[5] They are:

 

1. Catalog your organization business units and departments.

2. For each business unit /department ask questions about their business strategy and objectives.

3. Then ask about what goals do they set for themselves in order achieve the objectives.

4. Next ask what metrics and indicators do they use to track where they are against their goals and objectives. Good rule of thumb: no business area, department needs to track more than 20 to 30 metrics. More than that is unmanageable.

5. Then ask questions how they would like to slice/dice these metrics (by time period, by region, by business unit, by customer segment, etc.).

6. Last but not least ask how do they envision tracking these metrics: printed reports, interactive visual dashboards, which metrics go onto which dashboard, which metrics are related to each other and should be tracked together on the same dashboard, etc.

7. Build a simple BI benefits business case (increased revenue, lower margins, cost savings, etc)

8. Then ask questions (and these you’ll probably know) what operational, transaction and other source applications contain raw data to populate these metrics.

 

Hunt concludes, “Usable, relevant and insightful analytics output frequently doesn’t come from big data alone, which can be fragmented and vague.” She explains:

“Big data needs further qualification through the context and relationships that come from core enterprise data, to make sense of what big data may be communicating. Other sources of data and information, including master data, extend the accuracy and value of big data and provide essential context to align big data with guideposts like customer identity, products, and locations or channels of interaction. … Critical thinking is still one of the most important tools for any analytics initiatives, and must be undertaken by many roles in an organization. Critical thinking must be used to determine what business problem you want to solve or which questions you would like to answer. For those involved in big data analytics certain questions should hover in the background: Why are we doing this? What are we trying to achieve?”

Cognitive computing systems can help companies understand context and relationships and they can analyze big data to help transform industrial age enterprises into digital enterprises.

 

Footnotes
[1] Julie Hunt, “How Big Data – and Critical Thinking – Lead to Business Value,” CMS Wire, 13 October 2014.
[2] Hailey Lynne McKeefry, “4 Steps to Bring Business Intelligence to Your Supply Chain,” EBN, 18 August 2015.
[3] Ed Burns, “Big data analytics architecture requires integration push,” TechTarget, October 2015.
[4] Lisa Morgan, “6 Ways To Ask Smarter Questions Of Big Data,” InformationWeek, 9 September 2015.
[5] Boris Evelson, “8 Steps to Business Intelligence Success,” Information Management, 11 August 2015.