The era of Big Data is in full swing. The term “big data” has both its supporters and detractors; but, like it or not, when people hear the term, they understand a massive amount of data is being discussed. Although big data is the sine qua non for the age in which we live, what makes big data valuable are the results obtained from its analysis. A couple of years ago, Jamie Davies reported a survey published by DNV GL found, “While the majority (52%) of companies have outlined the importance of big data for future operations, roughly only a quarter have the capabilities to fulfill the promise and capitalize fully on the benefits. The interest increases significantly for larger organizations, those of 1000 or more employees, as 70% highlighted it as a priority.”[1] The promise and benefits of big data can only be obtained through advanced analytics. Luca Crisciotti, CEO of DNV GL, made that point when he told Davies, “The ability to use data to obtain actionable knowledge and insights is inevitable for companies that want to keep growing and profiting.”

Advanced analytics is now a must-have capability

At the beginning of the Big Data Era, advanced analytics were a “nice-to-have” capability that often provided a significant competitive edge. That situation has changed. Sam Ransbotham (@Ransbotham), David Kiron (@DavidKiron1), and Pamela Kirk Prentice (@pamelakprentice) explain, “Competitive advantage with analytics is waning. The percentage of companies that report obtaining a competitive advantage with analytics has declined significantly. … Increased market adoption of analytics levels the playing field and makes it more difficult for companies to keep their edge.”[2] Richard Kestenbaum (@RKestenbaum), co-founder and partner at Triangle Capital LLC, bluntly states, “Your competitors are using [big data]. If you aren’t, you’re falling behind. … If you’re not using big data, your competitors are getting a better return on their marketing efforts than you are. How long can that go on?”[3]

Marketing isn’t the only business area in which advanced analytics are playing a significant role. “Analytics are literally everywhere,” writes Henry Canitz. “Open any supply chain periodical, blog, or report and chances there is a discussion around the importance of analytics.”[4] He continues, “At its most basic level, supply chain analytics are the application of mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns in the vast mountains of data produced by enterprise systems and external sources. As we tap into both structured and unstructured data sources, advanced analytics help us draw conclusions about demand, inventory, production and distribution operations to quickly drive more informed business decisions. An important goal of a supply chain analytics initiative is to enable better business decisions that improve operating results and allow you to be more responsive to customer needs.” There are few, if any, parts of an organization that can’t benefit from advanced analytics. Ozioma Ubabukoh explains, “In a rapidly changing business environment, businesses are under pressure not only to comply with constantly changing regulations but also to modernize their processes and systems. The purpose is to help reduce compliance costs, improve efficiency and effectiveness, stay competitive and drive innovation while looking for better ways to serve their customers. Against this backdrop, businesses will become more successful based on how they use data, analytics and collaboration in the new analytics economy.”[5]

Advanced analytics tools

Analytics packages are offered by a number of vendors, including my company. Every analytics suite has its strengths and weaknesses and businesses need to ensure the package they opt to use is optimized for the results they are seeking. George Lawton (@glawton), suggests there are ten “must-have” features businesses should look for when selecting an analytics suite.[6] They are:

• 1. Embeddable results. Lawton writes, “Big data analytics gain value when the insights gleaned from data models can help support decisions made while using other applications. … These features should include the ability to create insights in a format that is easily embeddable into a decision-making platform, which should be able to apply these insights in a real-time stream of event data to make in-the-moment decisions.” One of the benefits of a cognitive computing system is that users don’t need to know what applications they are using, they just need to ask plain language questions in order to receive plain language results.
• 2. Data wrangling. We’re all familiar with the term “garbage in, garbage out.” Data wrangling addresses that challenge. Lawton explains, “Data scientists tend to spend a good deal of time cleaning, labeling and organizing data for data analytics. This involves seamless integration across disparate data sources and types, applications and APIs, cleansing data, and providing granular, role-based, secure access to the data. Big data analytics tools must support the full spectrum of data types, protocols and integration scenarios to speed up and simplify these data wrangling steps.”
• 3. Data exploration. One of the challenges with big data is its size. Advanced analytics mine for the nuggets of insights trapped in big data. Lawton explains, “Data analytics frequently involves an ad hoc discovery and exploration phase of the underlying data.”
• 4. Support for different analytics. Lawton notes, “There are a wide variety of approaches for putting data analytics results into production, including business intelligence, predictive analytics, real-time analytics and machine learning. Each approach provides a different kind of value to the business.” Finding the right analytics model can be difficult. To ensure the right variables are in play, Enterra Solutions® leverages the Representational Learning Machine™ (RLM) created by Massive Dynamics™. The RLM can help determine what type of analysis is best-suited for the data involved in a high-dimensional environment.
• 5. Scalability. At Enterra, we recommend companies use a crawl, walk, run approach when taking on a big data project. In the end, however, being able to scale a pilot project is critical.
• 6. Version control. One of the reasons companies are moving to the cloud is to ensure their software is always up to date. Using cloud-based analytics can ensure the latest analytics suites are also being used enterprise-wide. Lawton explains, “In a large data analytics project, several individuals may be involved in adjusting the data analytics model parameters. Some of these changes may initially look promising, but they can create unexpected problems when pushed into production. Version control built into big data analytics tools can improve the ability to track these changes.”
• 7. Simple integration. Data integration is a serious concern when dealing with big data since it can be both structured and unstructured. Cognitive computing systems are generally capable of handling both types of data. Lawton observes, “Data analytics tools should support easy integration with existing enterprise and cloud applications and data warehouses.”
• 8. Data management. Ensuring all organizational divisions have access to a single source of truth is important for corporate alignment. Lawton writes, “A robust data management platform can help an enterprise maintain a single source for truth, which is critical for a successful data initiative.”
• 9. Data governance. It should come as no surprise that getting the data right is critical to big data project success. Three of Lawton’s “must-have’s” (data wrangling, data management, and data governance) address this challenge. Lawton explains, “Data governance features are important for big data analytics tools to help enterprises stay compliant and secure.”
• 10. Data processing frameworks. Lawton writes, “Many big data analytics tools focus on either analytics or data processing. Some frameworks, like Apache Spark, support both. These enable developers and data scientists to use the same tools for real-time processing; complex extract, transform and load tasks; machine learning; reporting; and SQL.”

Summary

Big data analytics are essential in today’s business environment. I agree with Kestenbaum; if you aren’t leveraging big data analytics, you’re falling behind.

Footnotes
[1] Jamie Davies, “Organizations struggling to capitalize on benefits of big data,” Business Cloud News, 23 May 2016.
[2] Sam Ransbotham, David Kiron, and Pamela Kirk Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” MIT Sloan Management Review, 8 March 2016.
[3] Richard Kestenbaum, “Your Competitors Are Using Big Data And You Should Too,” Forbes, 22 August 2017.
[4] Henry Canitz, “Advanced Analytics Are Everywhere!” Logility Blog, 28 June 2018.
[5] Ozioma Ubabukoh, “Embracing analytics in workplaces,” Punch, 4 July 2018.
[6] George Lawton, “10 must-have features for big data analytics tools,” TechTarget, September 2018.