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Self-serve at the Data Buffet
July 10, 2018 Blog analytics Data


Everywhere, enterprises seem to be embracing self-service analytics.

And why not, since it’s incredibly appealing from an IT reporting perspective where there aren’t enough hours in a day to generate reports to satisfy the constant stream of requests from the business.

However, what’s not exactly clear is how to define the term self-service analytics. When discussing this with Qlik customers and prospects, there’s a wide range of answers with some companies suggesting that self-service is achieved by providing interactive applications to answer a set of pre-defined business questions repeatedly over time. Others suggest opening the data stores, warehouses and lakes to a business audience to have full access, within reason, to create their own output using any of the company’s sanctioned tools.

Most organizations seem to fall somewhere in between this spectrum and argue that there’s a need for a range of delivery mechanisms. It’s not feasible to try and predict every type of question and build those into dashboards in the same way that it’s not possible to give data level access to every business user and assume they can answer their own questions without assistance.

Everyone can eat at the data buffet when the food is already prepared. Having a buffet of raw ingredients would let some users channel their inner master chef and create incredible dishes, while others would struggle to cook rice.

Organizations that I see being successful around self-service analytics take several things into consideration. There are crucial steps from the get-go, like leadership embracing and cultivating a data driven culture with a focus on developing data literacy amongst employees. What also seems to work well is aligning a user’s data literacy level via certifications to data access and tool capabilities.

Take for example, company A, which provides access to pre-built dashboards. Most business users can use this to find the information they need in their daily decision making. The Marketing team analyses pipeline generation, the HR team might look at the recruitment funnel and all users only look at pre-defined data sets that are related to certain metrics. This is the base level that employees can access. Liz, who works for company A and has taken part in a data literacy training course and certified herself to have access to a wider data set from the data warehouse. She can use this information and combine it into existing apps to help answer questions that have come up in her team but can’t be satisfied by their baseline dashboards.

Rachel, another employee at company A, has taken an even higher-level certification and has gotten access to less structured data in the data lake as well as the web analytics data around customer visits and dwell time on the company A website. She uses this information to build new apps and explore how linking previously unused data sets can provide meaningful context and new insights.

The key investment at the end of the day, is in the people. Technology helps them become faster and more efficient. Enablement and a process for data access can help structure self-service analytics initiatives so that everyone can enjoy the buffet, regardless of what kind of food they like.