What’s new in data strategy and why it matters now

Welcome to the first edition of our Data & Analytics Newsletter

Data and analytics have become central to how organizations plan, operate, and make decisions. At the same time, the landscape keeps getting more crowded. New platforms, new features, new governance concerns, and new expectations around AI and automation are arriving faster than most teams can reasonably keep up with.

That is the reason for this newsletter.

Our goal is not to cover everything. It is to focus on what is worth paying attention to and what has practical relevance for business leaders and data teams. Each issue will highlight a few meaningful developments, point to a signal or trend in the data, and share one practical idea that organizations can apply in their own environment.

Here is what you will find in each issue:

• IN THE DATA - A timely trend shown through public data and a simple visual, along with a short perspective on why it matters to businesses across industries.
• IN DATA - A roundup of notable developments in the data and analytics landscape, including product updates, platform shifts, governance issues, and broader business-relevant news.
• ACTIONABLE INSIGHT - A practical lesson, framework, or example designed to help organizations improve the way they report, govern, analyze, or act on data.

The intent is straightforward. We want to make data and analytics easier to interpret, easier to connect to business decisions, and easier to put to work in a useful way.

Thanks for reading.

Clean energy is moving from niche story to operating reality

Wind and solar reached a new milestone in 2025, generating a record 17% of U.S. electricity on a utility-scale basis. When small-scale solar is included, the share rises to 19%. Looking forward, 2026 is on track to deliver another record year, as early data for January 2026 indicates that this percentage will continue to climb. That matters not only as an energy headline, but as a broader signal that the U.S. power mix is continuing to change in visible, measurable ways.

For business leaders, the more interesting question is not whether renewable energy is growing. It is what that growth means for planning assumptions. Electricity has become a more strategic topic for many organizations as power demand rises, data center expansion accelerates, and resilience, cost, and capacity enter more boardroom conversations. A record share for wind and solar does not eliminate short-term volatility or infrastructure constraints, but it does suggest that the underlying system is evolving faster than many legacy planning models assume.

This is the kind of annual trend worth watching because it sits at the intersection of operations, infrastructure, sustainability, and long-term investment. It is not a one-month fluctuation. It is a structural shift that may shape decisions about location strategy, energy sourcing, cost forecasting, and the broader environment in which companies operate.

Source: https://www.eia.gov/todayinenergy/detail.php?id=67367

Supporting Images:

Power BI keeps moving beyond static reporting

Microsoft’s March 2026 Power BI update is a useful snapshot of where business intelligence tools are heading. At the feature level, the release includes items such as Translytical task flows reaching general availability, Direct Lake in OneLake reaching general availability, and TMDL View in web modeling entering preview, alongside updates across Copilot, reporting, modeling, and connectivity. On the surface, that sounds like a standard product release. In practice, it reflects a broader shift in what organizations now expect from BI platforms.

For many years, BI was treated primarily as a reporting layer. Teams assembled data, built dashboards, and distributed information to stakeholders who used it to look backward at performance. That model still exists, but it is no longer enough. The direction of the market suggests that organizations increasingly want BI platforms to do more than present charts. They want them to support shared definitions, reduce friction between data and action, and sit closer to everyday operating decisions.

That is what makes this Power BI release interesting beyond the technical details. The theme is not simply that Microsoft shipped more features. It is that the platform continues to move toward a more active role in the business: more connected to enterprise data environments, more structured around semantic consistency, and more useful as part of workflow rather than as an isolated reporting destination. For readers who are further from the technical side of analytics development, the takeaway is straightforward: the next generation of BI is not just about seeing what happened. It is about helping teams work from a more governed, more actionable version of the truth, and moving beyond just looking at charts and data.

Source: https://powerbi.microsoft.com/en-us/blog/power-bi-march-2026-feature-summary/

The data platform is becoming a broader business conversation

Microsoft’s March 2026 Fabric update broadens that same story. The release spans governance, data engineering, real-time intelligence, data science, extensibility, and AI, which signals that the market continues to move toward more integrated data environments rather than disconnected point solutions. While many readers will not need to know the product details, the larger implication is important: data, analytics, engineering, and AI are increasingly being discussed as parts of one operating ecosystem rather than separate technical domains.

That shift matters because fragmented data environments create familiar business problems. Teams spend too much time reconciling definitions, moving data between tools, managing handoffs, and trying to maintain visibility across systems that were never designed to work together cleanly. As platforms like Fabric expand, the promise is not simply consolidation for its own sake. It is the possibility of reducing friction across the lifecycle of data: from ingestion and modeling to reporting, real-time analysis, and AI-enabled use cases.

For leaders outside the data team, this is less about choosing a specific platform and more about understanding the market direction. The conversation is shifting from “Which separate tools do we need?” toward “How integrated does our environment need to be for speed, trust, and scale?” That is a business question as much as a technology one. The more organizations depend on timely insight and cross-functional coordination, the more the shape of the underlying data platform starts to influence execution, not just architecture.

Source: https://blog.fabric.microsoft.com/en-us/blog/fabric-march-2026-feature-summary/

A cyber incident showed how quickly IT disruption becomes operational disruption

Stryker disclosed in an SEC filing that on March 11, 2026 it identified a cybersecurity incident affecting certain information technology systems, resulting in a global disruption to the company’s Microsoft environment. The company said the incident affected order-related processes and caused disruption across manufacturing and shipping. Even before the full impact is known, the business lesson is already clear.

You might be asking yourself “why is this in this newsletter? What does it have to do with data and analytics?”. We chose to include this event because it illustrates how digital and data dependency now works in practice. Core business systems are deeply embedded in how organizations process orders, manage production, coordinate logistics, and maintain visibility into operations. When that digital backbone is disrupted, the impact does not stay inside the IT function. It moves quickly into the operating model. Fulfillment slows, manufacturing is affected, reporting visibility can degrade, and leadership has to manage through uncertainty with fewer reliable signals.

The takeaway is broader than cybersecurity alone. As companies digitize more workflows and rely more heavily on shared enterprise platforms, resilience becomes a cross-functional issue. It is about security, but it is also about continuity, recovery, transparency, and the ability to maintain business control when routine systems are interrupted. In that sense, incidents like this are reminders that digital strategy and operational strategy are no longer separate conversations.

Source: https://www.sec.gov/Archives/edgar/data/310764/000119312526102460/d76279d8k.htm

Reduce KPI overload before it reduces decision quality

Many executive dashboards start with a clear purpose and lose it over time. A team builds a reporting view to help leaders monitor performance, then more metrics are added, more stakeholders request visibility, and more edge cases are accommodated. The result is familiar: a dashboard that looks comprehensive but is harder to use than the conversations it was meant to improve.

This happens because organizations often confuse more information with better guidance. In practice, when leaders are shown too many metrics at once, the first layer of reporting stops functioning as a decision tool. Instead of identifying the few signals that matter most, people spend time scanning, interpreting, or debating which number deserves attention. The dashboard becomes a summary of everything the organization tracks rather than a focused view of what leadership should act on.

A more effective approach is to organize metrics into three layers.

Outcome metrics are the few top-level business results leadership ultimately cares about. These may include revenue growth, margin, churn, conversion rate, service level, utilization, or another result directly tied to enterprise performance.

Driver metrics are the small number of measures most likely to explain changes in those outcomes. Depending on the business, these could include pipeline coverage, retention rate, resolution time, backlog, forecast accuracy, average selling price, or lead quality.

Diagnostic metrics are the supporting measures teams use to investigate why something moved. They are important, but they usually do not belong in the first view of an executive dashboard.

The common mistake is trying to show all three levels at once. That creates density, but not clarity. A better rule is that the primary executive view should contain five to seven metrics maximum, and each one should pass three tests:

• It has a clear owner.
• It supports a real decision.
• A material change in the metric would plausibly trigger action.

If a metric is regularly reviewed but does not influence a decision, it probably belongs in supporting analysis instead of the top layer. If two similar metrics appear because teams have not aligned on a definition, the problem is not reporting capacity. It is governance. If a dashboard keeps expanding because every stakeholder wants their number represented, the issue is not visibility. It is prioritization.

A practical way to apply this is to review one executive dashboard and ask four questions about every metric:

What decision is this supposed to support?

If there is no clear answer, the metric may be informative but not actionable.

Who owns the outcome connected to this metric?

Metrics without ownership tend to generate commentary rather than response.

Is this a result, a driver, or a diagnostic?

If it is diagnostic, it likely belongs in a secondary drill-down view.

What would actually change if this moved materially next week or next month?

If nothing would change, it is probably not a first-layer KPI.

This framework does not mean leaders should see less information overall. It means they should see information in the right sequence. The first layer should tell them where to focus. The second layer should help them investigate. The third layer should support detailed analysis and follow-through.

In practice, many organizations discover that they can reduce a dashboard from 20 or 25 measures down to six or seven meaningful ones without losing insight. In most cases, they gain it. The signal becomes clearer, the conversation becomes faster, and the reporting environment becomes more aligned to decisions rather than observation.

A practical place to start: choose one executive dashboard, classify every metric as an outcome, driver, or diagnostic, and then decide which five to seven metrics truly belong in the first view. Move the rest into supporting layers. That single exercise often reveals whether the real issue is too much data, unclear ownership, or lack of agreement on what matters most.