In recent years, self-service analytics has been at the forefront of data-driven transformation. Data analytics platforms like Microsoft Power BI, Tableau, and Alteryx have enabled business users to take their own data and make data-driven decisions based on these insights. The job market has even evolved as companies prioritize the modern data analytics professional, focusing on data visualization and storytelling to provide insightful analytics and reporting. This is fantastic, of course it is. However, companies are starting to realize this creates new obstacles for data organizations. Here is where we find the true cost of this shift to self-service analytics.

As self-service analytics organizations strive to uncover new insights, they often ignore critical best-practices around data standards, data validation, security, and data governance. This is becoming a very serious and tangible problem which is frequently ignored and highlights the need for a unified data governance approach.

Build a Data Governance Strategy with Azure Purview

Data Standards

At Fulton Analytics, our Data Success Framework provides guiding principals to build a bridge between self-service data innovation and time-tested data standards. Breaking down data siloes and establishing common data definitions across departments/products is important to data success. In most data organizations the pendulum eventually swings back to governance.

Data Compliance

The very concept of “data at rest” is one of the most frighteningly unknown phrases in an era where compliance should be a commonly recognized data standard. The sheer amount of risk being produced by ignorance of such concepts and the lack of instruction or dialogue around it is tangible. Never have companies and individuals put themselves at as much risk as they currently are. If you ask yourself “have any of my developers or analysts lost a laptop?”. If the answer is yes, and they have been in developing or exported any data from a report, then you should start getting very nervous. Where these basic compliance practices have been lost is where great risk resides.

Data Validation & Testing

Database developers for decades have one thing drilled into them. Do your testing. Today, the modern data professional is woefully unequipped in this area. There are some very rudimentary tests that can be done when data modelling, writing measures, and building reporting. Simple concepts like ensuring the row count is consistent in both source and output and calculations match what the source reflects. In other words, test the basics. Standards that all developers should have as part of every report creation process before any UAT should even be considered. Too many times I have been in discussions with clients where data validation and trust is a fundamental issue within their reporting. In all recent cases this has been one of the fundamental issues and has stymied data driven adoption and slowed the development of a data culture.

Data Security

Luckily, data security is a common topic for most of us when it comes to company infrastructure, cloud migration, and data management of key assets. Unfortunately, as data moves through to the hands of the end user, there is some weakening of those controls. This however is not a unique scenario and has been an ongoing conversation for decades. What must be acknowledged is that the security conversations is an area of continuous improvement. At this point in time the security scenario is such that their new and existing challenges are presenting themselves as a rocky patch that needs to be addressed to ensure that self-service analytics can thrive while still having security best-practices in place.

Data Governance

This topic is huge right now and one that I am glad to see is growing in visibility. This is happening because people are becoming more aware of the disastrous pitfalls. Some organizations have dove headfirst into a well that cannot hold any water. This is where data and information culture comes into play.

Data Governance is costly and highly complex. Conversations on what the options are and how Data Governance can be handled are becoming increasingly common place. What is harder to appreciate though is how these conversations can be very challenging. For many this can be as challenging as changing a culture or a strategy. This requires buy in and time considering decisions and implementation. Microsoft as a company is also recognizing their role in this by coming out with tools such as Azure Purview, as well as creating some best practice documentation on approaches. Many companies avoid this massive undertaking, but it is possible to start small and align resources to the data-driven mission little by little.

What Is Azure Purview?

A Bridge to Data Success

Avoid falling to a harsh data death and be sure to build a bridge between these core data standards and your self-service analytics team. As an industry, these are the things that are going to get worse before they get better. That may sound excessively dramatic, however I do believe the base of the curve can be reached and the balance re-established quickly. This can be attained through mentorship, due diligence, and by taking personal responsibility for each of these four core areas:

Spend time learning what’s happening behind a report and agree on data standards so you can assume it is clean and holds integrity.
A little time focused on data validation/testing is all it takes to ensure your reputation for standards is set in place.
Do some research in basic data security standards. There will always be a myriad of articles out there claiming to be the gold standard in all things, so find the reputable ones.
Lastly, look at your organization and see if there are any of these data governance challenges currently flying under the radar. If you see something of concern, propose a plan of action or a simple change in process.

If your organization is experiences some of these same challenges and would like to speak with a data strategy expert contact: info@fultonanalytics.com