This is the third and final article in our series about tools that aim to help users make decisions using data. In our first article, Katie discusses why Dashboards Don’t Cut It, and in our second article, Jerry reviews why Self Service Tools Don’t Cut It (either).
What if there is a better, more fluid way to find the insights you seek in your data? We believe that there is and that way is true data exploration.
Allowing Ad Hoc Analysis
Every day we find ourselves with new questions and problems that data can solve. In the past decade, the standard way for teams to engage with their data has been through dashboards and reports. The problem with trying to answer ad-hoc and complex questions with dashboards is that they are fixed to predefined data, calculations, and charts. Industry trends reported by Gartner agree data and analytics will be seeing the “Decline of the Dashboard” in 2020, as organizations have recognized over and over again their painful limitations.
Standardized reporting is just not enough because most people have daily ad hoc needs that aren’t going to wait for a 3-month dashboard build; that’s where data exploration comes into play. When we have a brand new problem and need a quick solution to it, we want a tool that can provide a fluid, adaptable experience for finding the answers we need with whatever data we have.
Sometimes we don’t even really know what it is yet that we’re looking for. Underneath the surface of the data may be lurking insights that inspire us and push our imaginations to new questions and ways of thinking. Through data exploration, we can rapidly discover those insights and answer questions we didn’t anticipate having.
And what is even more powerful than solo ad hoc analysis is doing this analysis together with teammates, sharing the process so that we can build off of each other’s expertise. With multiple perspectives, we get higher quality answers and more holistic insights in record time.
Enabling Team Collaboration
Today, ad hoc questions get answered primarily through email. We call this chaotic workflow “data ping-pong”. Someone notices something odd and sends out a screenshot of a dashboard. “Oh, Jane might know about that”. Jane gets pinged and sends an Excel file to another colleague for validation. That person makes changes and sends the file to someone else, so on and so forth, etc. By the time the answer comes back, you have no idea where it came from, who touched it, or what was the process the data went through. You may get an email back containing a spreadsheet with 20 sheets. You have to figure out what it means, whether you can trust it, and if it’s answering your original question. At this point, you then need to go back through this circus to find out what version of the data you’re looking at and the rest of the details– not a smooth or fluid process.
Business problems are complex and require combinations of different skills to solve. It’s rare to have one person with all of the skills needed to solve them. Lucky organizations might have a couple of people that serve as the “data gurus” of the company. These gurus act as solicitors of analytics for people across the organization, not necessarily in an official role but mostly because they are the only people who can. This model is unfortunately not scalable nor easily replicable. If you could distribute problem-solving across multiple people with complementary skills, you could scalably get insights from data in an efficient way.
With a collaborative data analytics tool, you can include the IT person that knows all about how the data is stored, how it’s formatted, and how to transform it. This person can help you quickly figure out what assets exist to answer this question and how you can get them. You can then pull in another teammate who doesn’t necessarily know anything about data but knows all about the subject matter that you’re exploring. They’re from a particular area of the business and know the nuances and realities of the issue at hand, as well as what’s going on in real life and how to address it. This teammate can give you perspective on what you might be seeing and should be looking out for, tying the numbers back to real life. Then you might bring in a third person who is highly analytical and confident with their statistical understanding. They can combine the numbers and business perspective to paint the pictures needed to find answers in the data. When you get all three of these team members using their skills together in a seamless way, the insights flow.
Providing Needed Flexibility
Great data exploration also provides users the flexibility to bring and manipulate their data as needed using a Bring Your Own Data model. In the dashboarding world, exploration will lead you to bump against the boundaries of the data and metrics provided. In a seamless data exploration model, you don’t have to be stuck there. You can easily grab the additional data you need, or loop in another person to augment your work with their own data assets and perspective.
This is how innovation and insight happen: Taking old things and putting them together in different and unique ways to create something new. This basic concept is replicated in the data exploration process, and it’s why we need the ability to bring data and people together. In a shared analytics space, we can ensure that we’re looking at all pieces of the puzzle.
We built Knarr with data exploration at top of mind. Knarr provides the ability to engage in ad hoc analysis, it enables team collaboration, and it offers flexibility. When questions arise that need to be answered quickly, Knarr aims to provide the platform to empower your team to find the answers it needs using all the expertise and data that your team has available. To learn more about the tool that enables true powerful data exploration, visit us at knarr.io.