Turning Big Data Into Smart Data

We all want to work smarter. Can more data really help?

Reading Time: 4 min 


Companies are getting more data; in fact, the typical company doubles the amount of data it stores every two years. But more data isn’t necessarily a good thing.

Data, after all, is inherently dumb. Getting more of it often seems to compound its lack of intellect.

Plus, data brings with it some significant baggage.

“All that data can be a bother, an unwise expense,” said Jeanne Ross, director and principal research scientist at MIT’s Center for Information Systems Research, speaking recently to a group of executives gathered for a recent Sloan School of Management Executive Education program, “Revitalizing Your Digital Business.”

Large volumes of data present security and reputation risks, given how often data ends up in compromising positions. The challenge for executives, she said, is to change the business culture to make the expense and risk of data worth it.

“Think about every single person who takes action in your company. In other words, pretty much everyone. The big opportunity is at the operational level,” Ross said. She pointed to Seven-Eleven Japan, which has pushed decision-making down to the store level — in fact, to the level of clerks. Store clerks decide what goes on the shelves in their individual Seven-Elevens. These clerks push incredible inventory turns; some 70% of the products on the shelves are new to stores each year. This might sound like a recipe for disaster to anyone who’s seen the movie Clerks, but Seven-Eleven Japan has been the most profitable Japanese retailer for 30 years running.

Ross said there are three main cultures companies adopt around data:

  1. A culture of heroics. In the culture of heroics, individuals respond to requests and take on extra tasks, like finding an item a customer wants when inventory says it’s out of stock. To the customer in the front of the store, the person who found the item is a hero — but it may be that the item brought out to satisfy that customer had already been sold online or over the phone, and simply hadn’t been shipped yet. That means some other customer is going to be unhappy unless steps are taken to expedite restocking and shipping. Still, Ross says such a culture can work well in small businesses, where employees tend to see across all channels.
  2. A culture that emphasizes discipline around processes. A disciplined process culture uses template approaches to data — with common processes, reuse of components, and a single face presented to customers and the general public alike. Employees must consistently follow standard procedures, which can be a challenge for many employees. Companies that cannot develop such consistency will not gain benefits of ERP or CRM systems. Companies that succeed in developing disciplined processes destroy the culture of heroics in the process. It’s a scalable model, but employees in a disciplined process culture can feel like they’ve lost personal authority. Ross noted that it’s difficult to build such a culture that is both successful and lasting.
  3. A data-smart culture. A culture that routinely handles data in an intelligent way demands evidence-based management. It builds on the disciplined process culture, but instead of being centralized, decision-making gets pushed out to the ranks. Seven-Eleven Japan, with its empowered sales clerks, provides a good example of evidence-based management.

To make data-smart cultures work, Ross said companies need to incorporate several key practices:

  • Establish a single source of truth. People need to know what’s expected of them, and how they will be measured. It sounds simple, but it can be tricky; Ross cited a case where, after a year where Aetna had lost money, every business unit head had spreadsheets showing that their part of the business made money. Each unit had their own financial truths, which they defined — even when their definitions didn’t hold up against the overarching truth of the company’s losses.
  • Use scorecards. In the Aetna example, a new president had his IT department develop a scorecard that showed daily performance. It started out with mediocre data, but over time, scorecarding worked, not least because the act of measuring made department heads turn in better data. Ross likes scorecards, especially daily ones, as a way to motivate employees across the board — with the caveat that scorecards don’t work well if they contain too many measures. Scorecarding also may not work in some cultures; one of the European managers attending the course said European business culture would bristle at scorecards.
  • Create ownership of business rules. Businesses run by rules, Ross said. For example, Allstate Insurance had a rule that it must wait 30 days to pay out a policy when a car was stolen. The company realized this policy was costing it customer goodwill and a lot in rental car fees. When they looked at the policy more closely, they found that there were certain parts of the country where stolen cars simply weren’t retrieved. Allstate modified its business rules so that in these parts of the U.S., an automated process cuts the insurance check within 24 hours of the car being reported stolen. Making such changes in the business means that someone must “own the process” of business rules, and be able to set new rules.
  • Cultivate your talent. Companies also must develop their people, coaching them to work effectively with data and giving them regular feedback (with or without a scorecard). Key to this development is having managers act almost as coaches, engaging consistently in one-on-one interactions to help each employee perform better.

By putting these cultures and practices into play, businesses can use data to run smarter. It doesn’t require rocket science, but it does demand a culture that’s shaped to work with data, at all levels.


More Like This

Add a comment

You must to post a comment.

First time here? Sign up for a free account: Comment on articles and get access to many more articles.

Comments (2)
Amit Sheth
Here is a talk given on the topic of "Transforming Big Data into Smart Data" which gives more technical take on the terms that cover these buzz words:
[July 1, 2013]: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
(can also find a link to more recent version from there).
Finally, another voice of sanity in the wilderness of pipe dreams.  The theory must go something like this: "data is good so _big_ data must be better."  Here’s the flaw:  Data is good IF you use it.  But IF you can’t (or don’t or won’t) use it, big data is just an expensive chimera. Some of my thoughts on how this applies to the customer experience... http://bit.ly/15Pqnfa