If You Want to Beat the Competition, Ask More Interesting Questions

This article is by Joe DosSantos and originally appeared on the Qlik Blog here: https://blog.qlik.com/if-you-want-to-beat-the-competition-ask-more-interesting-questions

If You Want to Ask More Interesting Questions, Get Yourself More Interesting Data

Over the past few months, as I have spoken with Qlik customers, I have been amazed by the clever solutions that have been proposed to sticky business problems. I have seen an app that steers trucks away from inclement weather and another that identifies pre-diabetic patients to trigger alternative care management. These apps deliver profound value to the organizations that developed them, and, usually, they dramatically drive down cost.

What has been interesting to me, as I listen to the stories, has been how carefully the organizations planned the apps — they were based on a concrete understanding of a business problem, where the challenge was assembling and presenting data. In the first example, there was a well-established challenge with costly delivery failures in natural disasters, despite the general availability of data from the National Weather Service. In the second example, there was extensive research on the characteristics of pre-diabetic patients, but a failure to combine it in a systematic way to make good medical decisions.

But, what if you don’t have a concrete understanding of the problem? What if you don’t know what you’re looking for? What if, rather than, “what truck routes are headed into a storm?”, my question was should I even be using a truck for this shipment ?” Now, this is a more interesting question. In order to answer this, I need to have some understanding of the alternatives. Would I consider evaluating rail data? Flight data? How about public transportation or even drones? How do I find this information? How do I know if it is even useful?

At Qlik Qonnections this year, we conducted our annual Hackathon for demonstrating Qlik solutions. This year, the Qlik team presented the participants with the flight data from travelers at the United Nations (UN). They asked the participants, “Can you create a greener travel recommendation for 2020 for UN ambassadors and employees?” This is an interesting question, as it is so open ended and relies on the ingenuity of the analyst to derive insightful answers. The respondents considered the alternative of hosting web meetings instead of sit-down, on-site sessions between ambassadors and their staff. This was a simple app that calculated the greenhouse cost of flights and set it at zero. Of course, this is useful, but a more interesting solution may have contemplated the role of trains and buses. Perhaps, the UN should invest in a fleet of airplanes to consolidate flights. Perhaps, the UN should invest in a fleet of electric vehicles to transport travelers from LaGuardia, John F. Kennedy International, and Newark Liberty International Airports to the UN. The possibilities are endless and almost entirely dependent on the availability of data to understand the relative cost benefit and greenhouse gas impact of each option.

And, note how many times above I used the word perhaps. What a pain in the neck. I know that I need data to make decisions, but when I ask for data, I have to sheepishly admit that I am not really even sure if that data matters. In theory, I could get train schedules only to find that they do not cover the areas in question. Thus, in order to be effective in this kind of analysis, we have to have a system to reduce the friction involved in procuring, understanding and using new data sets. We need to think about establishing a catalog of available information assets and putting them to use. We need to think about getting this catalog published in hours and days, instead of weeks and months. And, we need to delight in our failures in analytics, as they are our best teachers and motivators, encouraging us to come back for even more data. At Qlik, we have begun to start talking about this effort as Qataloging: the methodology and technology that gets data into the hands of people quickly and cost effectively. It requires process change, possibly organizational change and most certainly executive support. And, it becomes the cornerstone of 3rd Generation BI.

As you contemplate evolving toward 3rd generation BI in your organization, ask yourself a few simple questions:

  1. Who owns the problem of data availability in my organization?
  2. Are we measured on cost and speed of delivery of information?
  3. Does my organization ask interesting questions or simply automate KPIs?

How you answer these questions may highlight the need for a CDO role, a misalignment of technology and business objectives, or, in the best cases, a window of opportunity to improve the data agility of your organization.