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10 Things The C-Suite Needs To Know About Analytics

C-Suite Analytics

10 Things the C-Suite Needs to Know about Analytics

If you haven’t traveled much lately, here’s an executive summary of developments in the field of analytics that you might have missed in the airline seatback magazine.

 

  1. It’s not called Decision Support Systems anymore (though it was 20 years ago). C-Suite Analytics Top 10                                                                                                             Not reporting (15 years), Business Intelligence (10 years), or even Analytics (5 years).  It’s Augmented Analytics.  Or, Analytics embedded with AI.  Cutting edge Analytics now takes advantage of machine learning and assists in making decisions from the data.  So, in a sense, we’re back to where we started – decision support.
  2. Dashboards.  Progressive companies are moving away from dashboards.  Dashboards were born out of the management by objectives movement of the 1990s.  Dashboards typically show Key Performance Indicators and track progress toward specific goals.  Dashboards are being replaced by augmented analytics. Instead of a static dashboard, or even one with drill-through to detail, AI infused analytics alert you to what is important in real time.  In a sense, this is also a return to management by well-defined KPIs, but with a twist – the AI brain watches the metrics for you..
  3. Standard tools.  Most organizations no longer have a single enterprise standard BI tool.  Many organizations have 3 to 5 Analytics, BI and reporting tools available.  Multiple tools allows the data users within an organization to leverage better the strengths of the individual tools.  For example, the preferred tool in your organization for ad hoc analytics will never excel at pixel-perfect reports that government and regulatory agencies require.
  4. The Cloud.  All leading organizations are in the cloud today.  Many have moved initial data or applications to the cloud and are in transition.  Hybrid models will support organizations in the near term as they seek to capitalize on the power, cost and efficiency of data analytics in the cloud.   Cautious organizations are diversifying and hedging their bets by leveraging multiple cloud vendors. 
  5. Master data management.  The old challenges are new again.  Having a single source of data to analyze is more important than ever.  With ad hoc analytic tools, tools from multiple vendors, and unmanaged shadow IT, it’s critical to have a single version of the truth.
  6. Remote workforce is here to stay.  The 2020-2021 pandemic pushed many organizations to develop support for remote collaboration, access to data and analytic applications.  This trend shows no signs of abating.  Geography is becoming more of an artificial barrier and workers are adapting to working on dispersed teams with only virtual face-to-face interaction.  The cloud is one supporting technology for this trend.
  7. Data Science for the masses.  AI in analytics will reduce the threshold to Data Science as a role within an organization.  There will still be a need for technical data scientists who specialize in coding and machine learning, but AI may partly bridge the skill-gap for analysts with business knowledge.  
  8. Monetization of data.  There are multiple paths where this is taking place.  Organizations that are able to make smarter decisions quicker will always tend to have a marketplace advantage.  On a second front, we’re seeing in the evolution of Web 3.0, the attempt to track data and make online more scarce (and therefore more valuable) by using blockchain systems.  These systems fingerprint digital assets making them unique, traceable and tradable.
  9. Governance.  With the recent external as well as internal disruptive factors, it is an important time to re-evaluate existing analytic/data policies, processes and procedures in light of new technologies.  Do best practices need to be re-defined now that there are multiple tools?  Do procedures to comply with regulatory requirements or audits need to be examined?
  10. Vision.  The organization relies on management to make the plans and set the course.  In turbulent and uncertain times it is important to convey a clear vision.  The rest of the organization should be aligning to the direction set by leadership.  An agile organization will re-evaluate often in a changing environment and course-correct, if necessary.
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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.