Self-service Business Intelligence Tools For Ad Hoc Data Querying – One of my colleagues, Dave, has something like this: “No data tool can help you achieve data literacy in your company.” But we can definitely guarantee that we won’t get in the way.
As a tool developer, it’s easy to get overwhelmed and think of all the ways we can solve all of our customers’ problems and improve their lives, get them to write us happy emails, and so on. But the truth is that business intelligence problems are sociotechnical problems and generally require some combination of people (read: culture) to solve.
Self-service Business Intelligence Tools For Ad Hoc Data Querying
It should come as no surprise to anyone that self-service in the data analytics space is difficult to define. Ben Stencil has an entire article where he argues that “self-care is an emotion,” which I mostly agree with, and Stencil says, that analysis of self-care
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It depends on how the organization handles self-service data in its tools. Do they trust it? Do they feel comfortable getting what they need?
This, Stanchill continues, depends on the organization’s context (do they trust the numbers in their data systems) and data maturity (are they comfortable with their BI tool) and business user needs (the CEO defines metrics ? consumption tone).
So yes, organizational context matters when it comes to self-service analytics. A self-service setup that works for one company may not be equivalent self-service for another.
In one sentence. I believe self-service can be viewed as a business outcome that successfully avoids a general state of organizational failure. To be more specific, I believe that self-service analytics is a state where the business is sufficiently data-driven, but the data organization is not 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 other analytics platforms. Your analyst reports to management and everything is fine within a few months. But eventually your analyst can’t keep up with all the requests they receive from end users, so you hire someone else. And another. And another. And then your company grows, creates 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, all writing research or putting together Excel spreadsheets, just trying to keep up. with business requests your company throws at them.
These analysts are mostly English-to-SQL translators or Excel jockeys. They are all relatively young. Some are elderly, of course. But there is usually not much career advancement for them. And most of them are appropriately dissatisfied with their jobs, and a reliable percentage of them quit (read, leave the company) every six months or so. You continue to hire new analysts to keep up with business demand and face the ever-increasing challenge of managing employees.
Based on data that does not have this problem and will instead have a different set of problems and a different set of failure states. Anyway.)
This is the failure state that self-service analytics must address. It’s a failed state because it’s very difficult to maintain an army of English-to-SQL translators. Ideally, you want a smaller group of data professionals who can serve a much larger number of data consumers. And the only way to get it right
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The scale must have some form of “self-service” – that is, some way by which business users can get the data they need;
In other words, self-service analytics is a worthy goal because it increases your data team’s operational leverage. You can serve more people with fewer analysts. This is an ideal business outcome.
— This failed state is not where the company is data-driven, but they got there just by throwing people at the problem and having 100 data analysts spread across six departments writing 100 lines of SQL queries. Self-care, when viewed through the lens of my upside-down definition, is
In a data-driven, data-intensive enterprise, bad data organizations tend to look the same, but functional data organizations are very different from one another.”
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And all truly data-driven companies with good self-service capabilities look very different. For example, at a consumer software company I know, many people in the company’s hierarchical structure are SQL savvy, so they are able to solve their self-service problems with a combination of a SQL-based BI tool and a database. Well designed data. storage. and a visualization tool or two. Would it be this
Works for a cosmetics company where most of their employees don’t know SQL and prefer to create dashboards for themselves. Self-service in the first company is different from self-service in the second. (By the way, this second company works better for self-service analytics goals than 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 achieve it is by asking yourself at every step.
In this scenario, the best thing a tool can do is not get in the way. The best thing a Business Intelligence tool can do is prepare you when you want to grow your organization and get out of a failed state.
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To understand why true self-service analytics are difficult to find, let’s talk about the arc of adoption.
Well, most companies go through a very similar period of data adoption. They do so because the use of data is determined by organizational culture and organizations go through similar cultural changes when they have access to data. Understanding what this process looks like will help you understand why so many tools promote the ability to provide “true self-service.” It will also help you prepare for future growth.
How you respond to these requests largely depends on the tools available. If you have access to a centralized data warehouse, chances are you’ll write some ad hoc SQL query to generate the numbers you need.
If you are working in a more “decentralized” data environment, you will need to find the right data sources, capture the necessary subset of data, and then analyze it with whatever tools are available on your machine.
What Is An Ad Hoc Query And How Does The Process Work?
Eventually, as more businesspeople embrace the idea of getting data to bolster their arguments (and as the complexity of the business increases), the data team will start to feel overwhelmed by the volume of requests they receive. The data manager then moves on to the next obvious step.
This chief data officer started looking for a BI tool to create dashboards for these predictive metrics to free up his team for more ad hoc queries from other parts of the company. Once he created these reports, his data team immediately began to feel less overwhelmed.
“We’re really happy,” he told us, “The product team and the marketing team each had their own dashboard, and once we got everything set up, the number of queries from those two teams went down. Now we’re trying to give them a new report every time they ask for something, rather than doing ad hoc queries for them all the time.”
Many companies quickly realize the importance of having good reporting functions. If they don’t accept a panel solution, they will find some
Self Service Business Intelligence
Way to provide predictive data to your decision makers. For example, a small company we know uses email. The issue is that the numbers reach them in a repetitive and automated way.
Ultimately, new hires and existing operators learn to trust 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 queries. Over time, business operators who rely on their dashboards begin to adopt more sophisticated ways of thinking. They learn to rely less on courage to make pleas like “let’s target Japanese businesspeople in Ho Chi Minh City” or “let’s invest in fish instead of dogs.” This leads to an increase in requests for temporary exploratory data.
The data team is overwhelmed again. Some companies have experimented with SQL training for their business owners. Others buy into the self-service story sold by a second wave of BI tools. This includes things like the PowerBI usage paradigm and the Tableau Desktop drag-and-drop interface. “Give them tools like this,” they reason, “and they will be able to 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 measurement failures;
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