How to Rescue Your Data Analytics Program from Failure

You've invested in your analytics program, but where's the return? Here's how to make this investment of time and money pay for itself.

Everybody knows that analytics programs can be game-changing for business. That's why so many companies are investing in data-centric solutions and services. But is your analytics program on the right track, or is that investment your organization made going to waste? Does your program yield actionable results?

If not, it's time to wake up and smell the coffee. Meta Brown, a statistician, engineer, and president of the consultancy A4A Brown, has spent years in the field, helping organizations with their analytics programs, and she has some real-world experience and best practices to share with attendees of her Interop ITX session, What IT Needs to Know About Analytics Process.

In her session she'll share the secrets behind why some analytics programs fail and why others succeed.

The biggest mistake that organizations make when investing in analytics? Not starting with a plan to succeed. Instead, many organizations start with a vague idea about analytics being important and strategic.

"Somebody hears that some kind of data analysis will do great things, and they decide to buy software," Brown told InformationWeek in an interview. "They buy it on the vendor's promise that it will provide them with insight...Now we have spent money. We have bought software. We have devoted staff time. But we have never figured out how to tie the investment to action and make money."

Brown notes that many vendor-provided analytics success stories are actually back-filled narratives created to demonstrate value after the investment, because the customer company didn't start with a success plan or goal. So maybe a retail women's clothing company hears from customers that they want larger-sized clothing. They offered a few garments in larger sizes, and women bought them. That's a success story, right?

"That is not an analytics success," Brown said. "You didn't need to invest in analytics to have the simple information that women would like larger sizes. All you needed to do was read the newspaper or walk down the street or ask me. I would provide you that information for free." (So would I.)

Brown said this type of narrative is often defined as a success story for an analytics program. But, again, the retailer didn't need to invest in software to gain that insight, and the retailer probably didn't start with that goal when it made the investment. This is the story that is put forth to justify the investment after the fact.

Brown recommends a more disciplined approach to investing in analytics, and believes IT can help analytics practitioners, particularly when it comes to process.

"IT has a fabulous opportunity to play a role in establishing good processes," Brown said. Process means not making changes until you test and measure many different variations, according to Brown. Process means you know how you are going to measure value. IT builds the structure. Data analytics professionals put the information into it. They need to work together to build the right system.

Besides having a plan for success, measuring that success is key component to a successful analytics program, Brown said.

"A lot of people can't measure [whether] they are making any value from their analytics. Not everyone has made an analytics plan that is tied to business value," she said. "They are not preserving the intellectual capital that is being created by an analytics program."

In Brown's session at Interop ITX, attendees will learn why analytics programs fail to break even, and what is a good process is for ensuring they succeed. 

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