Self-Service Analytics gone wrong

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Self-service analytics is the promise of data democratization and faster data-based decisions and business strategy. It’s better when you can do it yourself, right? And in many cases, your analytics set-up might give you the opportunity to run queries on your own. However, this does not happen without its limitations.

SageData Self-Service Analytics / Freepik

What can go wrong when using self-service analytics tools?

On one hand, self-service analytics is the desire by IT departments to free themselves from effectively creating reports to business users so they can focus on broader enterprise tasks. On the other hand, business people get the information they want, when they want it, how they want it. You lessen the load on the analytics team and open up access so people can sort themselves out. The smaller jobs go away and take care of themselves. It sounds like the perfect win-win situation, so what can possibly go wrong?

In the short term, it might seem like your team has increased the speed of making data-driven decisions, but in the long term you might observe the following scenarios:

  1. Paradoxically, too much accessibility

After time you will find it difficult to audit who has access to what. And should they have access at all?  This increases the risk of poor-quality reports. Non-data scientists might not look beyond the quick analysis that confirms their suspicions. Such reports might get shared with a broad audience, thus spreading misinformation.

Furthermore, your data store can suddenly explode by having hundreds of new tables/files/etc, that no one fully understands, or feels responsible to sanitize.  At the same time, everyone is too scared to delete them in case something breaks.

  1.  Licensing costs

Democratizing analytics capabilities sounds wonderful until you start blowing your budget with tooling. Suddenly everyone requires a license to do their own BI.

  1. Getting false sense of security

Valuable data analytics will require solid data science knowledge, as well as experience in properly interpreting results. In general, the users will have the means and tools to identify general patterns but will lack details and the knowledge how to interpret them properly. In general, if the team is not very data literate, this can also cause users to come to poor decisions i.


How to get self-service analytics right

Not all hope is lost, when using self-service analytics tools! They create a very wide range of possibilities for enterprises and can be a game changer. Self-service is great, but it needs guidance: protections in place to ensure it achieves the outcomes intended without creating more problems along the way.

  1. Set a clear goal

Self-service is not the end goal. Define clear targets and end goals beyond just enabling your user to self-service. For example, do you want to show your business users couple of dashboards, letting them know what is happening? Or do you want to help them understand why something is happening?

  1. Increase data quality.

A lot of data might be scattered in different applications or only accessible by certain departments or team members. A Forrester research study found out that as much as 60% to 73% of all data gathered by companies goes unused for analytics.

Define and maintain a single source of truth –  bring data together into a single platform.

  1. Scale data literacy.

Self-service can generate innovation, find answers faster, and generate ideas. However, it has to be paired with a product mindset on the data team, and question finder. Collaboration and education matter. Can an analyst and product manager pair and learn together? Can someone start an analysis, make a notebook, and then share that with someone more skilled to get feedback?

Furthermore, the users need to be coached to lead the way to a conclusion. Decide carefully who should have access to the data and the tools, and train them accordingly.

  1. Make tools accessible by Tech and Non-Tech Users.

Self-service data analytics tools must be useable at a variety of levels, so you can achieve a wide level of adoption. Your data team may need a higher level of access and analytic tools. However, other teams might only need basic dashboard (at-a-glance) visualizations or basic sorting, filtering, and grouping capabilities.

By supporting testing, recommendations, personalization, and feature flagging, we get teams close to the action. At the end of the day, the goal is to build a system that creates value for customers, business, etc.

  1. Implement a governance model

Data governance is essential for balancing an organization’s need for analytics access with IT’s requirement of maintaining appropriate security. You need to be able to manage rights and access privileges to ensure that the right people can access the data they need while preventing unauthorized users from accessing it.

Data governance is essential to establishing processes, roles, policies, standards, and integrity to assure data quality and reliability. Tight controls may also be needed to maintain compliance with company, industry, or government regulations.


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