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.
- It’s not called Decision Support Systems anymore (though it was 20 years ago). 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.
- 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..
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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?
- 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.