Data governance with self-service data analytics – what to look for

Self-service data analytics is great, but it’s not without its hiccups.

A client learnt this the hard way after implementing self-service BI in their IT operations without giving much thought to a governance framework. While the company did make some gains, they have also encountered a lot of problems.

“Data quality has come under serious scrutiny,” the company representative explained. “I can share the specifics with you later, but it’s had a huge effect on our findings. The CEO is also starting to ask questions about compliance,”

While there is no denying that self-service data analytics is incredibly powerful, it is also just as important to recognise that it needs to be regulated with a data governance framework. Without that framework, your business is going to face a lot of problems. Let us explore some of the dangers of neglecting data governance and how to resolve that problem.

Dangers of neglecting data governance

Neglecting data governance hurts businesses on two fronts: Revenue generation and data compliance.

No data governance leads to compliance problems

Data compliance is something all organisations should be concerned with. The world’s governments are starting to wake up to the power of data and passing laws to regulate access.

The GDPR passed by the EU is the most comprehensive law on data access and usage. But there are others to consider as well, especially for companies operating in several parts of the world. For example, Australia has the Privacy Act.

To be clear, I am not trying to suggest that organisations are actively violating data laws without data governance. But, if there are no standards in place, it becomes difficult to verify if organisations are breaking compliance laws or not.

Business-related problems

Besides the problems related to compliance, there is also the issue of business outcomes. Data governance not only helps with compliance but also improves data quality. Without data governance, your self-service data analytics platform will be processing poor quality data.

Poor quality data will severely compromise your findings, making it much harder for business people to make decisions. Data governance ensures that data is of high quality and renders more accurate readings.

By contrast, data governance ensures that data is clean and consistent across the board. Furthermore, it’s been my experience that when there is a governance framework in place, organisations inevitably get better at organising the hierarchy of roles and responsibilities around managing data.

Setting the right governance framework

There are several ways for organisations to set a data governance framework that ensures compliance, transparency and better business outcomes. Here are just some of the methods you can follow.

Integrate self-service analytics with data preparation carefully

A key part of establishing a framework for governance is addressing data preparation. Organisations should look to integrate data governance with data preparation processes to meet compliance standards. Automation and web administration technologies can help tremendously in this process.

Make sure teams are trained

Self-service data analytics might be easy to use, but that doesn’t guarantee that everyone can use it, nor is it a guarantee that people with access will follow data governance laws. So it is important to keep everyone up to speed by providing some training sessions.

Maintain a balance between standardisation and agility

Self-service data analytics often runs the danger of being poorly coordinated. To make sure this doesn’t happen, organisations need to maintain a balance between user agility and BI standardisation. Some ways this can be done is by creating a self-service application with standard choices or offering guidance within self-service platforms.

Setting a standard in self-service data analytics

With data analytics becoming a bigger part of the organisation, self-service data analytics would become a vital tool to generate reports and answer business-oriented questions. So, it is important to ensure that the system follows data governance laws to ensure accurate results and that organisations are not unintentionally violating data regulations.

While this might seem like an extra step in the short-run, it yields tremendous value in the long-run.

However, while self-service data analytics is crucial for modern organisations, they are not quite up to the standard of more advanced analytics systems, like machine learning and NLP. To integrate those systems into their infrastructure, organisations should consult with SAS experts, like the Selerity team.