What do modern self-service BI and analytics really mean?
Self-service business intelligence (BI) is often touted as the answer to your data analytics needs, and while this may be the case, understanding what it is and how to achieve it should be your first task before pulling the trigger on an analytics solution.
Read on, to understand what self-service BI is, why it could be the right solution for your organization, and how to go about successfully implementing and achieving it.
Defining self-service BI
In short, self-service business intelligence can be defined as; allowing end-users without a technical background, or in-depth knowledge of data analytics, to access data and create or customize their own reports and analyses.
Unfortunately, while its definition is fairly straightforward, it is often easier said than done.
Agility in analytics
Agile analytics are an imperative in today’s market
The first step to achieving self-service BI is to strive for an agile analytics environment. There are well-established agile methodologies adopted by the most famous software companies of today. Most of them have a number of things in common:
Simplicity. Avoid analysis paralysis. Prototype new ideas quickly.
Small, iterative, and frequent releases reflecting users’ needs.
Communication and collaboration. Business and technical people work together (this one requires some clarification in the context of BI and analytics - see below),
Process. Please don’t take “Individuals and Interactions Over Processes and Tools” from the Agile Manifesto too literally. Yes, it can be a lightweight process, but you do need one.
An agile analytics environment will give you the flexibility to roll-out a self-service BI tool that caters for your entire audience, be it internal teams, or external partners or customers.
Modern BI vs. Consumer software
In order to successfully implement modern BI it is important to distinguish how it differs from something like consumer software. Unlike with the development of consumer software products, the distinction between business users and BI developers is blurry.
When you use Google to search the internet, it’s clear; you are the user while the folks in Mountain View wearing t-shirts, jeans, and flip-flops are the developers. With BI, the divide is more complicated.
For example, what if you are a user of a business analytics application who is somehow literate with data? You don’t have to be a BI developer or a data scientist. You can still do some ad hoc reporting or data analysis, and you can prototype reports and dashboards that are worth sharing. Of course, not everyone is like you, and it’s for this reason that it’s good to start your self-service analytics efforts with user personas.
You may be literate with data to the point that you can create some promising prototypes, but you may easily miss a detail or two. The lady from the marketing team may be on top of it when it comes to data flowing from digital marketing systems, but she may be a little lost about product telemetry. The receptionist may not be the right person to create a sales forecast dashboard, and neither is the CEO; she may have the knowledge but not the time.
Finally, you have a data engineer or a data scientist who can crunch the data to get insights you never knew you might need. Or, more importantly, an engineer’s discipline when it comes to taking care of data quality, testing things out, and keeping them maintainable. However, their intuition may be somewhat limited when it comes to which insights are actually useful for running your business.
True agility happens when all these personas get the right level of analytical power and can work together in a well-defined collaborative environment.
The three principles of agile self-service BI
The best-kept secret of agile self-service business intelligence is in these three principles:
Give the right people the right set of tools and capabilities
Create a collaborative environment
Create a process to govern how this collaboration leads to continuous improvement of your analytics
If these principles are followed, each of your personas — like the marketing leader, the CEO, or the data engineer — can use analytics and BI successfully.
The marketing leader has the tools she needs to drill down into relevant data and use it to improve how her team functions. The CEO can reference high-level insights without getting bogged down by more detailed team-level data. A more data-savvy individual contributor can easily create new insights, new computations, or even upload new data sets to prototype what the boss needs. Finally, an administrator with an engineer’s discipline, and a sense for software life cycle management, makes sure that the good ideas come to production quickly and on time, while still meeting certain quality criteria.
Together, each of these distinct personas can use BI to improve their own performance, the performance of their team, and, ideally, the performance of their organization as a whole.
How do you get there?
Your approach to achieving a streamlined self-service BI experience should be two fold:
Acquire a BI tool that is able to achieve the results you are striving to achieve, both in the short and long term
Develop an agile self-service data analytics culture where each user gets just what they need - i.e. avoid a one-size-fits-all approach
There is some obvious minimum set of analytics capabilities for a data platform that will help you achieve this sort of agile self-service environment (besides table-stakes features such as data visualization, pivot tables, or dashboards):
Semantic layer — enabling business users to articulate powerful data queries across multiple datasets in a straightforward way
Strong role-based access control — giving the proper level of content management rights to the right people
Data governance — a framework to prevent chaos from taking over your solution
Customization support — allowing business users to extend the curated semantic layer by uploading their data or creating their own calculation while distinguishing them from the curated set of data
Prototyping support — enabling power users to clone existing analytics artifacts or environments to prototype new capabilities