Analytics ROI often falls short because businesses overlook important parts of the process.
Businesses across industries want to compete on insights, and yet many of them aren't getting the ROI they expect. Somewhere between raw data and actionable insights, processes break down or they don't exist. It's not just a tool problem. Organizations need to change the way they think and behave.
Data is data
Every company has data, but not all organizations treat data as a valuable corporate asset. If they did, they probably wouldn't have multiple copies of customer records, none of which is "the golden record."
"There's no documentation so there's no traceability," said Ivan Chen, director of Enterprise Business Analytics at GPU manufacturer NVIDIA. "If you really want to get value out of your data, you have to put structure around it. We realized that, so we're putting plans in place to provide a foundation for analytics."
Chen is wrapping up the first month of a three-month pilot that will enable self-service analytics. As a first step, his team is inventorying all of NVIDIA's data assets, documenting them and putting them in a central repository.
"We're codifying all the business logic in the repository so that people can see how the transformation that was done," said Chen. "That way, there's a baseline for discussion."
In the next phase, analysts will get access to self-service analytics.
"The fundamental reason analytics is not successful is because different functions have different goals," said Chen. "Business professionals are trying to answer questions about the market, but the market conditions keep changing so you have to keep enhancing the data in reports. IT is supposed to make those changes, but IT has other priorities, so the whole thing breaks down."
NVIDIA dedicated three IT professionals to the pilot who are supporting Chen and his team. Before the pilot began, it took IT six months enhance the data in a report. Now it takes two or three days.
Analytics and outcomes aren't aligned
When analytics and business outcomes aren't linked, it's impossible to understand the ROI. All too often, people believe analytics is the outcome, rather than a means to an outcome.
Ivan Chen, NVIDIA
"Analytics are viewed as how many tools or dashboards you have, or how many reports you generate, " said Isher Kaila, CEO of management consulting firm Sapphire Nine Consulting. "No one is anchoring that to the amount of insights you are delivering, and by extension, what those insights translate to in terms of business outcomes."
Visualizing data in different ways can help clarify what the data says. However, it's also possible to visualize data over and over without producing any insights.
"You have to understand which insights are necessary for you to deliver an outcome, which people are attached to generating those insights, and who you will hold accountable for optimizing the insights linked to your outcomes," said Kaila. "Most organizations do a pretty good job of managing their reporting tool environment, but someone needs to be accountable for transforming insights into outcomes."
You're stuck in the past
More companies are using predictive analytics to accomplish something proactively, although a lot of companies are still doing business through the rear-view mirror.
"If you're just showing me the same data in a new way, how will that show me what I should be thinking about going forward? Is it challenging me to think about how I can optimize my business model? Are there processes I need to tweak? How much money am I leaving on the table? Are we cannibalizing our own revenue? Those are the kinds of questions you should be able to ask of your data," said Kaila.
Some organizations have hired a chief analytics officer or a chief data officer to ensure that data can be used as a strategic asset. That person is responsible for bridging the gap between business and IT, orchestrating resources, and driving value from analytics.
"Leaders need to ensure accountability for insights," said Kaila. "If you do that, you'll be able to align the definitions, the processes and ultimately how you operationalize predictive insights."
[Analytics and advanced use of data in AI and machine learning will once again be featured during Interop ITX in 2018. The Interop team has issued a Call for Speakers. Practitioners and independent experts are invited to submit.]
Analytics work best when they're executed as repeatable, sustainable processes that transcend any particular role. In many organizations, analytics are executed as a set of discrete functions and tasks, negatively impacting the potential value analytics can provide.
"Executives should be aware that they're likely not even harnessing the value of their analytics spend today. There's an analytics value chain in which outcomes are generated by insights, insights are generated by accountable business owners, and the accountable business owners have a strong partnership with their technical partners," said Kaila. "Too often we see a lack of alignment around metrics and insights."
Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio
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