Self-service Business Intelligence Tools For Exploratory Data Analysis Techniques – A colleague of mine, Dave, has this thing where he says, “No amount of data tools will ever help you achieve data literacy in your company. But, of course, we can make sure that we are not disturbed.”
As a tool maker, it’s easy to get introspective and think about how they can solve all our customers’ problems, make their lives better, get them to write us happy emails, etc. But the truth is that business intelligence problems are socio-technical problems, and usually you need a certain combination of people (read: cultures) to fix them.
Self-service Business Intelligence Tools For Exploratory Data Analysis Techniques
It should come as no surprise that self-service in the data analytics space is hard to define. Ben Stancil has a whole article where he argues that “self-service is a feeling” – which I largely agree with – and Stancil says that self-service analytics
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It depends on how the organization deals with selfish data from its tools. Do they trust him? They feel comfortable getting what they need,
This, Stancil continues, depends on the context of the organization (do they trust the numbers in their data systems?) and the maturity of their data (are they comfortable with their BI tools?) and the needs of the business users (set the CEO? Tone for consumption metrics ?)
So, yes, organizational context is important when you’re talking about self-service analytics. A self-service setup that works for one company may not be equivalent to self-service for another.
In a sentence: I think self-service can be seen as a business outcome that successfully avoids general organizational failure. More specifically, I think self-service analytics is a state where the business is sufficiently data-driven, but the organization of the data doesn’t look like an army of English-to-SQL translators.
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You realize you need a data analytics team, so you hire your first analyst and use Google Data Studio or Tableau or some other analytics platform. Your analyst sends reports to management and everything is fine for a few months. But eventually your analyst can’t keep up with all the requests you get from end users, so you hire another one. And more. And more. And then your company grows, you create departments that report to different managers, and each department hires its own analysts, and now you have an army of analysts in different parts of the company writing questions or perfecting Excel spreadsheets just trying to keep up with the demands business that your company presents to them.
These analysts are mostly translators from English to SQL or working with Excel. All of them are relatively small. Some are older, of course. But they generally have little career growth. And most of them aren’t very happy with their jobs, and a reliable percentage of them quit (read: quit the company) about every six months. You keep hiring new analysts to keep up with business demand and grind your teeth to keep up with an ever-changing workforce.
Which is data-driven, which doesn’t have that problem, but instead has a different set of problems and a different set of failure states. It doesn’t matter.)
This is a failed condition that should be resolved by self-service analytics. It’s a failed state because maintaining an army of English-to-SQL translators is pretty painful. Ideally, you want a smaller group of data people who can serve a much larger number of data consumers. And that’s the only way to hit
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Scale is to have some form of ‘self-service’ – that is, some way that business users can get the data they need,
In other words, self-service analytics is a worthwhile goal because it increases your data team’s operational leverage. You can serve many more people with fewer analysts. This is a perfect business outcome.
— It’s not this failed state where the company is data-driven, but they got there by just throwing themselves at the problem, and they have 100 data analysts working in six departments writing 100-line SQL queries. Self-care, when viewed through the lens of my upside-down definition, it is
In a data-driven company with high data demand, bad data organizations tend to look the same, but working data organizations look very different from each other.”
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And indeed, all companies that are data-driven and have good self-service capabilities look very different. For example, in one consumer software company I know, many people in the company’s reporting structure are fluent in SQL, so they can solve their own problems with a combination of SQL-oriented BI tools, a well-prepared data warehouse, and one or two visualization tools. It would be
They work for a cosmetics company where most of the employees don’t understand SQL and prefer to build dashboards for them. Self-service in the first company looks different than in the second. (btw, it works better in achieving self-service analytics goals in this second campaign as opposed to the first).
In other words, self-service business intelligence is most usefully described as a business outcome – a place you get to through a combination of tools, processes and organizational structure. And the way to get there is to ask yourself at every step: “is this step closer or further away from a failed state?”
In that case, the best the tool can do is not bother you. The best thing a Business Intelligence tool can do is give you guidance if you want to grow your organization away from a dysfunctional state.
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To understand why true self-service analytics are hard to come by, let’s talk about the adoption arc.
Well, most companies go through a very similar data adoption arc. They do this because the use of data is determined by organizational culture, and organizations undergo similar cultural changes when accessing data. Understanding what this process looks like will help you understand why so many tools tout the ability to provide “true self-service.” It will also help you prepare for future growth.
How you answer these questions depends a lot on the tools you have at your disposal. If you have access to a centralized data store, you’ll likely write a special SQL query to generate the numbers you want.
When you’re working in a more “decentralized” data environment, you have to find the right data sources, grab the subset of data you need, and then analyze it with whatever tool you have on your computer.
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Eventually, as more business people embrace the idea of obtaining data to support their arguments (and as the company expands across the board), the data team begins to feel overwhelmed by the sheer number of requests they receive. The CIO then moves on to the obvious next step: a business intelligence solution to take some of the requirements off his team’s back.
This CIO began looking for a BI tool to create dashboards for these predictable metrics to free his team from additional ad hoc requests they were receiving from other parts of the company. After he created these reports, his data processing team immediately began to feel less overwhelmed.
“We’re very happy,” he told us, “the product team and the marketing team got their own dashboard, and once we got everything set up, the number of requests from those two teams went down. Now we try to give them a new report every time they ask for something, instead of asking them special questions all the time.”
Many companies are quite quick to realize the importance of having good reporting features. If they don’t accept the dashboard solution, they will find it
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A way to deliver predictive data to decision makers. For example, a small company we know uses email notifications and Slack notifications to deliver timely metrics to their business users. The point is that the numbers reach them on an automated and repetitive basis.
Eventually both new hires and existing operators learn to rely on their ‘dashboards’. This brings us to the next stage.
More dashboard usage leads to more data-driven thinking… which in turn leads to more ad-hoc requests! Over time, business operators who rely on their dashboards are starting to adopt more sophisticated ways of thinking. They learn to rely less on their intuition to make calls like “let’s target Japanese businessmen, golfers in Ho Chi Minh City!” or “let’s invest in fish instead of dogs!” This leads to an increase in requests for special research data.
The data processing team again finds itself overwhelmed. Some companies have experimented with teaching SQL to their business people. Others are buying into the self-service narrative being peddled by the second wave of BI tools. This includes things like the PowerBI usage paradigm and Tableau Desktop’s drag-and-drop interface. “Give them tools like this,” they think, “and they can help themselves find the answers they need.”
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Both approaches have problems, but the biggest problem is that they often lead to a knife fight with metrics: different business users may accidentally enter subtly different metrics
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