Post: Free Form Data Analysis vs. Data Governance and how it Correlates to a Japanese Delicacy

Standing before the screen, I carefully reviewed my options for udon. It was my first trip to Tokyo, and the lure of the wheat noodles was too strong. I made my lunch selection, grabbed a receipt marked in Japanese, and walked inside the noodle shop.

I handed a woman my ticket, and she scooped out my allotted noodle amount. My receipt and bowl were passed to another employee who added the broth. When my meal was handed back to me, I took a spoon and sat at the wooden table. There was no need for a cashier as I prepaid at the machine.

As an introvert whose Japanese speaking skill is minimal, I felt comfortable in this restaurant. It was so easy! No talking required, no chance of cultural blunders occurring. I had a mission- to eat udon. And nothing got in my way.

This self-service restaurant can be aligned to self-service Business Intelligence. If I am the business user, and my bowl of udon is a dashboard, I was just picking what I wanted and making it come to be.

Of course the ease of building your own reports saves time and puts the user in control. Instead of submitting help tickets to IT, you can build your own reports to show the exact data you want. This gives you time back to build more reports to analyze more data. You even have time to build more experimental reports- those you were curious about but didn’t have time to spend making.

The result is that now the business is acting on data insights faster than ever. Your company gains a culture of data consumption- everybody becomes their own detective to solve their own problems.

As you and your team build more and more reports, the possibilities of data comprehension seem endless. But without governance from a BI manager or department, you run the risk of the business reporting off inaccurate data analysis. How much freedom is too much? When users want a report to show a specific result, they can often unintentionally manipulate the data to show exactly what they want. This results in the business acting in intuition versus data.

In order to combat inaccurate data analysis, organizations ought to put data governance in place, so everybody is measuring off of the same set of key performance indicators (KPIs) to determine what is determined as successful for each organization. This can take the form of a designated individual or an entire team of data governance specialists that ensure KPIs are being met.

In the case of the udon shop, having employees dish out proper proportions ensures that there’s enough udon, broth, and topping for everybody. The shop also stays clean, and ensures that patrons maintain a sense of safety while dining. Plus, if any patron had a question, they could ask one of the employees.

The benefits of Self Service BI are not that clear cut. What is the perfect balance for your organization? Should you leave Business Intelligence up to the experts? Or should everybody have a hand in it?

Let us know what your balance is in the comments.

You can also contact us with any questions you may have or request a demo of Motio software.


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As the BI space evolves, organizations must take into account the bottom line of amassing analytics assets.
The more assets you have, the greater the cost to your business. There are the hard costs of keeping redundant assets, i.e., cloud or server capacity. Accumulating multiple versions of the same visualization not only takes up space, but BI vendors are moving to capacity pricing. Companies now pay more if you have more dashboards, apps, and reports. Earlier, we spoke about dependencies. Keeping redundant assets increases the number of dependencies and therefore the complexity. This comes with a price tag.
The implications of asset failures differ, and the business’s repercussions can be minimal or drastic.
Different industries have distinct regulatory requirements to meet. The impact may be minimal if a report for an end-of-year close has a mislabeled column that the sales or marketing department uses, On the other hand, if a healthcare or financial report does not meet the needs of a HIPPA or SOX compliance report, the company and its C-level suite may face severe penalties and reputational damage. Another example is a report that is shared externally. During an update of the report specs, the low-level security was incorrectly applied, which caused people to have access to personal information.
The complexity of assets influences their likelihood of encountering issues.
The last thing a business wants is for a report or app to fail at a crucial moment. If you know the report is complex and has a lot of dependencies, then the probability of failure caused by IT changes is high. That means a change request should be taken into account. Dependency graphs become important. If it is a straightforward sales report that tells notes by salesperson by account, any changes made do not have the same impact on the report, even if it fails. BI operations should treat these reports differently during change.
Not all reports and dashboards fail the same; some reports may lag, definitions might change, or data accuracy and relevance could wane. Understanding these variations aids in better risk anticipation.

Marketing uses several reports for its campaigns – standard analytic assets often delivered through marketing tools. Finance has very complex reports converted from Excel to BI tools while incorporating different consolidation rules. The marketing reports have a different failure mode than the financial reports. They, therefore, need to be managed differently.

It’s time for the company’s monthly business review. The marketing department proceeds to report on leads acquired per salesperson. Unfortunately, half the team has left the organization, and the data fails to load accurately. While this is an inconvenience for the marketing group, it isn’t detrimental to the business. However, a failure in financial reporting for a human resource consulting firm with 1000s contractors that contains critical and complex calculations about sickness, fees, hours, etc, has major implications and needs to be managed differently.

Acknowledging that assets transition through distinct phases allows for effective management decisions at each stage. As new visualizations are released, the information leads to broad use and adoption.
Think back to the start of the pandemic. COVID dashboards were quickly put together and released to the business, showing pertinent information: how the virus spreads, demographics affected the business and risks, etc. At the time, it was relevant and served its purpose. As we moved past the pandemic, COVID-specific information became obsolete, and reporting is integrated into regular HR reporting.
Reports and dashboards are crafted to deliver valuable insights for stakeholders. Over time, though, the worth of assets changes.
When a company opens its first store in a certain area, there are many elements it needs to understand – other stores in the area, traffic patterns, pricing of products, what products to sell, etc. Once the store is operational for some time, specifics are not as important, and it can adopt the standard reporting. The tailor-made analytic assets become irrelevant and no longer add value to the store manager.