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Swim with the tides of change to visualize data -- and decisions

Completing a three-part series on his blue ocean strategy for decision visualization, Lee Feinberg explains how you can take decision making to the next level by giving more people access to business intelligence and data visualization capabilities.

In my first and second articles on what I call the blue ocean strategy for decision visualization, I discussed the first seven of 11 steps to transform your organization from a data-driven model to a decision visualization one. The approach is based on the book Blue Ocean Strategy: How to Create Uncontested Market Space and Make Competition Irrelevant, by W. Chan Kim and Renée Mauborgne. This article covers steps eight through 11, addressing organizational changes.

Implementing new processes to manage, analyze and visualize data will undoubtedly be met with some resistance. Here, I provide tips on how to overcome that resistance and steer your organization into the blue ocean of opportunity.

Step 8: Openness

Have you heard of democratization of data, a trending term in business intelligence? To someone outside your company, it would seem unbelievable that data is being held captive by a central governing body. However, most organizations do, in fact, control the flow of data and provide access to it on a need-to-know basis only. But doesn't everyone in the company need to know about the business?

Remember that data just sitting in a database has zero value.

Also, software now makes it easy to connect to databases that once required special programming skills to access. You cannot stop the change; you need to have a plan to increase data openness. I'm not saying you should make all data accessible to everyone at once. That doesn't make sense for a range of reasons, from security to system performance. But think about limits as the exception, and remember that data just sitting in a database has zero value.

Let me add a different spin: Data openness is really about democratization of decision making. By allowing more people to have better and faster access to data, you foster better and faster decisions by more people -- a powerful concept that will transform organizations. A common argument against this idea is that people don't know how to use the data properly. If that's really the case, the organization needs to make significant investments to ensure that everyone properly understands their jobs and the business.

Step 9: Speed

Most software development work is done in a serial (i.e., waterfall) process. If you look at high-performing companies like Amazon, Google and Facebook, they use Agile and lean approaches. We need to apply these ideas to working with data and delivering data visualizations. If you continue to work within the same processes, with the same organizational shortcomings, new software might help you go a little faster, but you aren't going to see big changes.

The fear of making mistakes is what causes organizations to spend countless hours defining and scrutinizing highly detailed BI requirements before eventually signing off that they're complete and correct. That way of working just doesn't make sense, though, because the requirements always change, whether from outside forces, the impossible task of getting everything right or people changing their minds.

It's better to start with a simple set of requirements and build iteratively. By using Agile development techniques to rapidly create a product people can use to analyze and visualize data, new ideas will surface about what's most important to do next. And, by not going too far or deep with detailed specifications, it's much easier to move in a different direction and not have to defend a big investment.

You don't have to make an abrupt shift. Test the waters with a small project and see what works and what doesn't work for your organization. Pay especially close attention to how people interact with each other in new ways -- the good and the bad.

Step 10: Lifecycle management

Have you heard "Demons," the amazing song by Imagine Dragons? Every time I hear it, it reminds me that there are way too many demons in BI. All the dashboards and reports that you've created are some of the most dangerous! What percentage do you think are used at least once a week? If it's not 100%, you need to change. Would your company keep building a product that no one purchased? Of course not. Stop producing unused products.

Do you find the word "product" a bit strange? I chose it purposefully to change your perception and that of others in your organization. BI is generally seen as a cost center. Instead, make it be seen as a center of value creation. Having a product mind-set is a good first step.

To manage your product suite, measure whether your products create value and are desired. You're in BI -- set a good example! Here are a few starting points that you can adapt for your organization.

  • Purchases: How many people use each report?
  • Repeat purchases: How many people use a report more than once?
  • Conversions: What percentage of users access specific data drill-downs?
  • ROI: What actions are taken based on reports, and what are the results?

Step 11: Customer focus

Embrace your customers -- and stop calling them users, internal clients or partners. The word customer is important because it lets you think about the expectations and experiences you have as a customer in other aspects of your life. Outside of having a great product, two of the strongest influences on your ability to acquire and retain customers are support and marketing.

For support, you can do something as simple as letting all members of the BI team access a main support inbox and respond to any question they feel comfortable answering. This inbox is also a great way to collect new ideas from your customers and then personally reach out to individuals to better understand their needs. Feed these ideas into your Agile product cycle and make sure people know their ideas are being implemented -- you could even name features after them!

Training is another key aspect of support. You might create an amazing dashboard, but don't assume that your customers will know how to use it to analyze data. It's likely that many of them have never done a true analysis; most are used to looking at a mix of charts and hoping an insight appears. Some training ideas: Make a video walking through an example analysis from the view of different users, hold lunch-and-learn sessions (everyone loves a free meal), form internal user groups.

By marketing, I mean that you cannot "deliver and move on." You have to maintain a connection with your customers. Think about the ways companies try to keep in touch with you and adapt them to your situation. It's not necessary to plan extensively or set up sophisticated systems. Try a few ideas and see what works best. For example, create a dashboard showing all the measures you're now tracking -- and make it available to everyone.

Set your action plan

Now it's time to get going. To accelerate the flow of business, make data that can aid decision making more accessible. Retire the technique of gathering requirements and do more hunting for the next great idea. Maintain a portfolio of products that customers love, and measure how much love they have for those products. Build great experiences through support and marketing.

In this series, I've laid out the blue ocean strategy for decision visualization. While I've numbered the steps, you don't have to go in any particular order or limit yourself to one at a time. Think about which step makes the most sense as a starting point for your organization as it moves to better manage and visualize data for BI uses, then begin the change. Excelsior!

About the author:
Lee Feinberg is the founder of DecisionViz, a management consultancy that helps companies escape the legacy of reporting data by transforming complex data into simple pictures for making decisions. He also is a frequent presenter at domestic and international events. Email him at Lee@DecisionViz.com.

Email us at editor@searchbusinessanalytics.com, and follow us on Twitter: @BizAnalyticsTT.

Next Steps

Get more advice from Lee Feinberg on fighting fears about expanding access to BI and data visualization tools

Read about best practices for evaluating and selecting data visualization software

Find additional tips and case studies in our guide to managing data visualization programs

This was first published in June 2014

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