Last week's announcement that the CEO of Tableau Adam Selipsky withdraws felt more meaningful than the informal media coverage it received. For me, It was a sign that the murmurs of discontent he had heard were true: the Age of Tableau ended.
The glory days
Although Tableau first emerged in 2003, truly accelerated in the early 1990s. 2010, and what was. Users reported the tool as “revolutionary” Y “life changing”. His annual lectures sold out in minutes. The members met with hundreds of other people, proudly brandishing a booty that read ‘We Are Data People’ while attending roller skating social events and competitions “Iron Viz”. As I said, I was having a real moment.
To understand the hype, Importantly remembering life just before the Tableau era hit the scene. At the time, the teams of “data” it was actually just a few people in IT creating some SSRS reports, or the “type of numbers” appointed [sic.]Who knew how to use Excel. People struggling to inject data into the organization had little more than a spreadsheet and a dream.
By making data look good and easy to interact with, Tableau became the way to smooth the delivery of data to the rest of the company. We replaced cold spreadsheets with bright, intuitive dashboards that were accessible to anyone at any time. In return, data got a seat at the table, were invited to more meetings and got their own teams.
For many of us (I also drank the kool-aid), it was encouraging and exciting to see the data being celebrated, they were not relegated to the background. Tableau mentioned to us that being in the data wasn't just great, but also irrefutably important.
What has changed?
We live in a very different world from Tableau's Glory Days. It is no longer necessary to defend data; in reality, most companies are experiencing a FOMO data spike (fear of getting lost). With all the hype of data science, cannot devote enough resources to the data problem.
But instead of this being an even more glorious glory day, it's a disappointing experience all too often:
“Machine learning specialists topped their list of developers who said they were looking for a new job., with a 14,3 percent. Data scientists ranked a close second, with a 13,2 percent “. [1]
And even more damning:
“Between 90% of companies that have made some investment in artificial intelligence, less than 2 decade 5 report commercial gains of artificial intelligence in the last three years”. [2]
Eesh. Clearly, there is work to be done.
The disturbing
While it is essential to recognize that Tableau has enabled our current era of well-deserved attention and investment in data, We must also point out the challenges that remained after his reign so that we can take advantage of our current possibility.
Then, What are these ghosts that stand in our way?
Data === Dashboard
For many business users, data is now synonymous with dashboards. Although it's a seemingly benign misunderstanding, this actually causes a lot of after-effects, namely:
- Thinking that Tableau ‘will solve’ your data problems. Many companies make the mistake of realizing that all their data team needs is Tableau (o Power BI). This type of thinking ignores the most common pain points in gathering data sources., clean and transform the data and do the analysis itself, that, if you ask any analyst, are the most traumatic parts of any analysis. By not investing in these problems, you're telling your data team that your job is less important than the company's interpretation.
- Asking dashboards to do too much. Since Tableau is the only tool many teams have for presenting data, they are forced to turn everything into a dashboard, which significantly reduces the impact that a more detailed and detailed analysis could have. When clearing the context, the analyst's explanation and narrative, panels become a Rorschach test where everyone can see what they want to see.
- Although users are now more comfortable looking at basic graphics, we have made little progress in educating our trading partners on fundamental data concepts. The dashboards do not give us the necessary scenario to explain, as an example, why correlation is not equal to causality. This means that it became almost impossible to explain the relevance of our more complicated predictive models or statistical analyzes that are required to make the dreams of our current era come true..
Tool hyperspecialization
Crazy analyst workflow. Source: count.co
- One of the best things about Tableau in the beginning was that it just sat on top of your database, making it easy to connect to your existing stack of data tools without much effort. This model has been used by almost all data tools since then, creating separate tools for data pipelines, data cleaning, data transformation, data analysis and, of course, data visualization. This approach is completely fragmenting analyst workflows, leading to significant pain and delays in each scan. Due, Most analysts and data scientists have adopted a mindset of “not my data tool”: recognize Tableau as a necessary evil to make their work noticeable. Mira is Reddit hilo to see for yourself.
“If there was a button that could destroy every Tableau server in the world, I would press him”. -Anonymous data professional
Remember those 'murmurs of discontent’ what did i mention at the beginning …
Ghostbusters
We have an increasingly urgent need to find solutions to these problems before we find ourselves again struggling for relevance and attention to data.. To do that, we must start to focus on the following two areas:
Present more than numbers
Data + Context >> Data (source: Author)
Time to give data more voice. Dashboards are great for things where there is shared context and a simple decision. But for many things, those conditions are not met and, therefore, we need a new approach.
Me, and others, we have been beating the drum on data notebooks as a solution for a long time. Can tell the story, explain the methodology, Y Create beautiful images without sacrificing interactivity or presentability.
By using more notebooks, we can start to shed a culture that has been eager for dashboards. We can start working with our business partners instead of flinging questions and graphics back and forth onto an imaginary wall.
Choose the tools your data team wants
Data analysts and scientists see a red flag when a potential employer has Tableau and little else when it comes to data engineering or data analysis tools (as an example, run Tableau on your MySQL database 5 untransformed). This indicates that they are not prioritizing the work that these analysts will do.. This must end. AS SOON AS POSSIBLE.
Depending on the analysis your team is doing, the 'correct tools’ they will be different. But there are so many options out there, you just need to make sure you are investing in the work it takes to do great analysis as much as you are in a tool for the company to look at.
And well, you'll probably end up keeping some of those data scientists that, according to statistics, they are probably buying.
Conclution
We all owe a lot to Tableau for the current attention that data receives in our companies.. Despite this, to take advantage of this possibility and move towards a new Golden Age of data, we must address and remedy some of the Tableau-era ghosts that are holding us back.
Data Notebooks present an option that can give your team the flexibility it needs to start moving from Tableau to the next era..
And Count, we are excited to be a part of this new movement of data tools designed for modern challenges. You can learn more about the Count notebook here.
References
[1] Walter, Richard, “How Machine Learning Creates New Professions and Problems”, Financial Times, november 2017.
[2] S. Ransbotham, S. Khodabandeh, R. Fehling, B. LaFountain, D. Kiron, “Win with AI,”MIT Sloan Management Review y Boston Consulting Group, October of 2019.
[3] Header image of Luke Chesser about Unsplash
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