It’s time for the death of the analytics dashboard

The demise of the analytics dashboard is upon us.

Dashboards are simply no longer able to keep up with the pace of business, and have therefore outlived their usefulness, according to Cindi Howson, chief data strategy officer at ThoughtSpot and host of The Data Chief Podcast.

Analytics dashboards take time to develop and update. The data that feeds the dashboards needs to be wrangled and prepared, and that takes the expertise of a trained data scientists or analyst. Then, as new questions arise, the dashboards need to be updated with more wrangled and prepared data.

In addition, organizations often are dealing with a queue of requests for new analytics dashboards and updates, so the process of building new dashboards and updating existing ones can take weeks or even months. When that time has passed, more questions may have arisen, the information in the dashboards may be outdated, and still more updates are required.

Meanwhile, the COVID-19 pandemic starkly showed that weeks — and certainly months — is too long to wait to make decisions that might affect lives amid the spread of a deadly virus or the success or failure of businesses with employees who rely on their paychecks for economic survival.

Hospitals didn’t have weeks or months to know how much personal protective equipment to order, and restaurants didn’t have the luxury of time to make decisions about whether to lay off staff or try to remain open while making delivery their primary focus.

They needed to make data-driven decisions in real time.

And augmented intelligence capabilities are the means to make data-driven decisions in real time, according to Howson. Using improving AI capabilities, business users can query data themselves and quickly get the answers they need, eliminating the need for analytics dashboards.

Howson, a former vice president at Gartner with more than 20 years of experience in analytics, recently discussed what she sees as the demise of the analytics dashboard, including why traditional dashboards are no longer useful and how AI can enable organizations to move beyond them.

Why is it time for the death of the analytics dashboard?

Cindi Howson: The biggest thing is that dashboards have not been able to keep up with the needed time to insight. If the COVID-19 pandemic showed us anything, a backwards-looking dashboard that takes three months to build is not commensurate with the pace of business. Data workers report that 92 percent of their time is being spent on low-value operational tasks, and 84 percent of workers who need these granular insights reported poor experiences with their analytics solutions.

If analytics dashboards can no longer keep up with the pace of business, what should replace them?

Howson: Search and AI-generated insights that every worker can personalize into their own personalized pinboard. I don’t want to use the word dashboard there. People still need a consistent view of key metrics, but they don’t want to have to go through these intermediaries or wait weeks or months to get them. Something like a Google-like search can make it easy enough so that true business users — the non-trained analysts — can ask questions and assemble the answers into their own pane of key performance indicators.

What is the difference between a typical analytics dashboard and a pinboard?

Howson: A pinboard is something a novice — a non-analyst — can create, and it’s personalized. It’s based on the data that you, individually, need to see, whether it’s a new question and you need it answered right now or whether it’s something you’re looking at every month. You may have created it, assembled it yourself, or an AI engine may have told you what you need to be paying attention to because there’s a change in behavior, a change in the data, or there’s something unexpected going on.

The biggest thing is that dashboards have not been able to keep up with the needed time to insight.

I liken it to the telephone. If you think about a landline versus a smartphone, the phone didn’t go away, but the way that you communicate certainly did. It became more versatile. You can text, you can FaceTime, and you do so much more than you can with a landline. Another analogy is a musical playlist. You might still have professional DJs to play the music at a wedding or another event, but you’re not relying on only DJs to come up with your personalized playlists.

Is it essentially about enabling a business user rather than forcing them to rely on someone to create the data assets the business user can then use?

Howson: People talk about the dashboard backlog that they have. If you look at dashboards, it takes weeks, and in some organizations months, to build. They’ll have a three-month backlog of requests to add a new filter, to add a new visualization. [During the pandemic] there were so many new questions — which employees don’t have personal protective equipment, what can we do online if we have to shut down the restaurant? Analysts created reports, but perhaps the reports didn’t quite answer the question, and then there was a back-and-forth. In [traditional dashboard] world, you’re creating data extracts, so it just adds to the dashboard backlog.

The other thing is small data sets. If you think about cloud data warehouses, whether it’s Snowflake or Databricks Delta Lake, you want to be able to get all your data and not have to pre-aggregate things. Think about when we were having shortages of toilet paper. You have to get to the granular level of detail to be able say, ‘Here’s the SKU that we need to restock.’ Look at people who were laid off and were late on their credit card bill. You don’t need the aggregate data. You want to know that this is an individual customer who is never late on their bill and be able to figure out what’s going on with them. Let the business user ask the question of the data, or let the AI ask and answer it.

What about interactive dashboards that update constantly, such as dashboards that track COVID-19 vaccination rates down to the county level — is the demise only coming to static, rear-facing dashboards or to those real-time dashboards as well?

Howson: It comes down to the degree of interactivity and explanation. If you go to a website, you can look at a dashboard and see the rate of COVID case counts in New York. But today I might ask, ‘What’s the percent vaccinated by age group, and what’s the percent Delta variant, and how many people have traveled from India?’ That dashboard doesn’t allow me to answer those questions.

Have you begun to see organizations move away from analytics dashboards, or is the demise of the dashboard still more theoretical?

Howson: It’s absolutely happening. I think about [ThoughtSpot] customers who are speaking publicly about this, like Scott Peck of PricewaterhouseCoopers who says, ‘No more dashboards,’ and Juergen Kallinger from HP who said they’re out of the dashboard business during [cloud data warehouse vendor] Snowflake’s conference. The key thing that all of them say is that they’re getting to the higher level analytics — rather than just descriptive about what’s going on, they’re getting to the diagnostic level about why — and the harder business questions.

Another customer is Schneider Electric, which has tied its message about moving away from dashboards to the core values of the company, which is providing sustainable energy around the world. They talk about freeing up your energy. They talk about freeing up energy so you’re not in a dashboard backlog.

When did you start seeing organizations move away from analytics dashboards — is this something that was sparked by the pandemic or what it happening before the pandemic?

Howson: It was happening well before then, but those were the early adopters. Gartner actually included the decline of the dashboard in its Top 10 Data and Analytics Technology Trends for 2020, so this is over a year ago. COVID just accelerated it.

How did the pandemic accelerate the demise of the analytics dashboard?

Howson: The pandemic disrupted supply chains, it accelerated digital transformation, and prior to June 2020 people were not asking questions about diversity and inclusion efforts, although they should have been. There are new questions related to all of that, and new technologies enable [rapid insights].

What is the price organizations will pay if they continue to use rear-facing analytics dashboards?

Howson: It depends on the industry, but as Accenture reported, the gap between data leaders and data laggards is widening. In 2020, the leaders had two to three times the revenue growth of the laggards. You can also look at the rate for companies that have dropped out of the Fortune 500, and it keeps accelerating. It’s a matter of survival of the fastest.

What prevents organizations from moving beyond analytics dashboards — or any familiar technology — and embracing something new?

Howson: People fear change, and they get emotional about hanging on to things for different reasons. If I think about the dashboard developer, they’re afraid for their jobs, but I would say this new way of working has invigorated those who have embraced it and they feel more valued rather than feeling the drudgery. My dad used to say that there are three types of people. There are people who watch things happen, people who make things happen, and people who say, ‘What the hell happened?’ You don’t want to be a chief data officer or business leader who says, ‘What the hell happened?’ There are many watching and waiting for inflection point, or point when they feel safe enough, to change. The year 2020 was about being pushed into react mode and everything was firefighting, but 2021 has to be about strategic and intentional decisions to drive change forward.

Editor’s note: This Q&A has been edited for clarity and conciseness.