A Guide To Analytics Spring Cleaning

Post: Unclutter Your Insights: A Guide to Analytics Spring Cleaning

Unclutter Your Insights

A Guide to Analytics Spring Cleaning

The new year starts out with a bang; year-end reports are created and scrutinized, and then everyone settles into a consistent work schedule. As the days get longer and the trees and flowers bloom, thoughts of spring cleaning take root. The earliest references to spring cleaning come from the Jewish tradition of Passover, where families search for the last pieces of leavened bread before the holiday begins at sundown. In our family, the competition was fierce to find the last crumbs. The winner was rewarded with treats, money, and even a medal ceremony (a different kind of ROI)! It certainly turned these “jobs” into a bit of family fun. A yellow and blue badge with black text Description automatically generated

BI spring cleaning should not merely be a pursuit of tidiness for tidiness’ sake. It’s an exercise in balance, ensuring you’re equipped for the immediate while maintaining an eye on the horizon. Understand which assets have supported decisions through the year and polish them for the journeys ahead.

Set A Culture of Analytics Accountability

Just as you wouldn’t hunt for the last pieces of bread alone, the challenge of spring cleaning your analytics is not a solitary task. It involves the buy-in of all those who operate within and around your BI environment. This is a moment to instill a culture of analytic responsibility where users understand the value of their BI endeavors.

Setting Timelines and Tools

An enterprise-wide spring clean is a significant effort, and like any complex project, it requires clear timelines and the right tools. Invest in data governance tools that allow for the seamless management and monitoring of your analytics repository.

Enact Governance Policies

Governance is often seen as a restrictive measure, but in the context of BI, it liberates by providing the framework for an environment where assets have defined uses, clear ownership, and an actively managed lifecycle.

Taking Stock

The proliferation of self-service analytics tools creates multiplying assets at an unprecedented rate. What once was a clean set of reports and visualizations has burgeoned into a tangled web of dashboards, apps, and reports. While you may not be cleaning physical areas, “digital dust,” a new wave of clutter has emerged. For Business Intelligence teams, it’s time to roll up their sleeves for a different kind of spring cleaning—the one that revitalizes analytics and sets the stage for strategic data navigation.

Review the dashboards, analytics, and objects that are the foundations upon which your business decisions are made. Categorize each asset, discerning between what is still in active use, what is strategic and critical, and what may be redundant or outdated. With the right categorization, assets stop being random and become purposeful entities. Structure enables not just your own efficiency but the ability of your team to tap into the collective insights stored in your analytic arsenal.

Prioritization here is more than just a suggestion; it’s a survival tactic for your BI strategy.

Continuous Maintenance as a Practice

The spring clean should not be a once-a-year event but a practice of continuous maintenance. Set quarterly reviews not only to assess unknown or forgotten assets but also to reflect the dynamic nature of your business and its needs.

The disposal of analytics assets should be a deliberate, well-reasoned process. Data may be timeless, but the DARs we use to interpret it are not. Regular culling of the non-essentials is what will keep your insights sharp and your decision-making agile.

Measuring Success in Streamlined Efficiency

The true measure of a successful BI spring clean is not the volume of assets discarded but the agility with which relevant insights can be surfaced. It is the ‘less is more’ axiom in data form. Efficiency is king, and a well-organized, pruned analytics environment is its kingdom.

With your analytics ecosystem rejuvenated, you are gifting yourself and your team a landscape that is navigable, efficient, and, most importantly, primed for the insights that will drive your business goals. It’s not just about the here and now — it’s about preparing for the future, one cleaned dataset at a time.

A Clean Slate Elevates Your BI

The act of spring-cleaning your analytics is not just a technical task; it’s a statement of intent. It signals your commitment to the integrity and value of the insights that will steer your business forward. In the same vein that a tidy workspace can enhance productivity, a decluttered analytics repository can amplify the quality and velocity of your strategic decisions.

This is a powerful proposition for BI managers. It’s an opportunity to demonstrate leadership not only in data management but also in appreciation of its role in the organization. By parting with the obsolete and organizing the invaluable, you’re setting the scene for analytics success.

The call to action is unambiguous. Begin your analytics spring cleaning today, and as you do, you’ll be setting the stage for a year of clear, insightful, and impactful business intelligence.After all, spring cleaning your digital assets is not just about starting a tradition; it’s about adding value with real ROI to your business..

 

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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.