David R. Longnecker

Over the last twenty years, the analytics landscape has evolved dramatically but one fundamental truth remains: organizations still struggle to translate data into meaningful action. In today’s AI-augmented environment, the challenge isn’t accessing data-—it’s determining which insights actually matter and how to implement them effectively within your organization.

The Data Paradox of Modern Business

The average enterprise now manages over 10 petabytes of data, yet according to recent research from Forrester, only 29% of organizations report successfully creating actionable business value from their analytics investments. Despite massive spending on analytics platforms and data lakes, companies continue to see a fundamental disconnect between data collection and value creation.

  Why? Because many organizations still approach analytics backwards. They start with tools and technologies rather than clearly defined business problems.

Five Essential Questions for Your Analytics Strategy

1. Does your organization effectively use existing data and insights?

The goal isn’t to collect more data; it’s to better leverage what you already have. Before investing in more sophisticated analytics capabilities, assess how well you’re using what you already have. According to McKinsey’s 2024 Analytics Excellence survey, organizations that maximize existing data sources before scaling up new initiatives show 3.2× greater ROI on their analytics investments.

The reality: Most organizations use less than 30% of the data they already collect. Before expanding your data collection, focus on extracting value from existing sources by:

• Conducting a data utilization audit to identify unused data assets
• Creating a centralized data catalog that makes existing assets discoverable
• Establishing clear ownership and accountability for existing analytics
• Creating regular review cycles to assess the impact of current insights
• Cross-referencing existing datasets to uncover hidden relationships and insights
• Consolidating redundant or fragmented data sources before adding new ones
• Holding “data showcases” where teams present insights from existing sources
• Developing a formal process for sunsetting unused or low-value data
• Creating a “data impact scorecard” that quantifies business value from existing analytics

2. What is the organization doing with the insights it has already produced?

Data without action is just noise. In my experience with product teams, the gap between insight and action is where most analytics programs fail. Case in point: a client who tracked over 400 product metrics but couldn’t identify the five that actually drove their business decisions or their business success.

Effective organizations create closed-loop systems where insights trigger specific actions. This requires:

• Explicit decision frameworks that connect metrics to actions
• Regular reviews of which insights have led to actual business changes
• Documented success stories that demonstrate the value of data-driven decisions
• Defined “insight ownership” that assigns accountability for acting on each key insight
• Integration of insights directly into workflow tools rather than separate dashboards
• Automated alerting when metrics cross thresholds that require immediate action
• Continuous improvement processes that track how insights translated to business outcomes
• Executive sponsorship for critical insights to ensure organizational follow-through

The vast majority of analytics implementations focus almost exclusively on the “left side” of the analytics value chain: data collection, processing, and visualization. But true value emerges only when insights flow seamlessly into decisions and actions.

According to Gartner research, only 20% of analytics insights deliver actual business outcomes, highlighting the critical importance of the action pathway. This explains why they predicted that more than one-third of large organizations will have analysts practicing decision intelligence, which specifically focuses on designing the decision process rather than just delivering data.

McKinsey’s research on high-performing analytics organizations reinforces this, finding that the most successful companies are those that have transitioned from using analytics for “decision support” to actively driving “decision making”.

  Make the shift from “How can we analyze this data?” to “What decision would we make differently if we knew this information?”. If you can’t answer the second question clearly, stop and re-evaluate.

3. What specific business problems would the solution solve today?

Analytics without purpose is just expensive vanity. I’m still amazed at how many organizations implement analytics programs without clearly articulating the specific business problems they’re trying to solve.

We now understand the importance of problem framing more than ever. According to Accenture’s report on analytical maturity, organizations that frame specific business problems before implementing analytics solutions are 4.7× more likely to report significant business impact from their investments.

This aligns with Gartner’s research, which shows that leading organizations are embracing “decision intelligence” - focusing first on the specific decisions they need to improve rather than the data they want to collect. In fact, a McKinsey study found that high-performing analytics organizations are 1.5 times more likely to report double-digit revenue growth when they start with clearly defined business problems.

Before investing in new analytics capabilities, document:

• The 3-5 most pressing business decisions that require better data
• The economic value of improving each decision
• The current data gaps preventing better decisions
• Success metrics that will determine if the analytics solution is performing
• Critical stakeholders who will use the insights to drive action
• Potential organizational barriers to implementing the resulting decisions
• An iterative roadmap that delivers incremental value rather than a “big bang” approach

This problem-first approach has been embraced by high-performing organizations. For example, Capital One doesn’t build analytics capabilities in the abstract—they identify specific customer pain points that data could address, then build targeted solutions.

Gartner is aligned and advocates that organizations whose CDOs focus on value-stream-based collaboration will significantly outperform peers by focusing on specific business problems rather than general analytics capabilities.

  Remember: The goal isn’t to have the most sophisticated analytics capability; it’s to make better business decisions that drive measurable value.

4. Do you know how the analytics program will connect to operational processes?

This is where many analytics programs fail. A 2024 Deloitte survey found that 76% of analytics initiatives never connect to operational processes in a systematic way.

In contrast, successful programs, those at the Managed and Optimizing level, create structured pathways from insight to action by:

• Mapping analytics insights to specific decision points in existing workflows
• Establishing clear roles and responsibilities for acting on insights
• Creating feedback mechanisms to measure the impact of data-driven decisions
• Creating cross-functional teams that include both analytics and operations personnel
• Implementing “insight councils” to review and prioritize actions from analytics findings
• Building automated alerting systems that trigger when metrics cross predefined thresholds
• Establishing governance structures that enforce data-driven decision protocols
• Developing training programs that build operational teams’ data literacy
• Creating recognition systems that reward employees who successfully act on insights

According to Gartner, 95% of decisions that currently use data will be at least partially automated, indicating that leading organizations are moving beyond manual insight-to-action processes to build systematic connections between analytics and operations.

McKinsey’s research on high-performing organizations shows that companies with successful analytics implementations create what they call “insight cockpits” - integrated environments where insights are automatically delivered to decision-makers within their workflow, saving time and increasing adoption rates by up to 70%.

The most mature organizations implement “augmented analytics” - solutions that not only deliver insights but recommend specific actions, making it easier for operational staff to translate data into decisions. This approach can reduce decision latency by up to 60% and improve decision quality by 40%, according to industry research.

5. Who is going to use the solution, and are they equipped to do so?

The human element remains the most overlooked aspect of analytics initiatives. According to LinkedIn’s 2024 Workplace Learning Report, 67% of data and analytics implementations fail due to inadequate user training and adoption—not technology limitations.

MIT Sloan and NewVantage Partners research indicates that while 94% of companies plan to increase their data investments, only 24% actually describe themselves as data-driven, and a mere 2% consider data literacy an investment priority. This explains the massive implementation gap in analytics adoption.

The most significant barriers aren’t technical but human. MIT Sloan’s further research reveals that “the adoption barriers organizations face most are managerial and cultural rather than related to data and technology,” with almost 40% of respondents citing “lack of understanding of how to use analytics” as the primary obstacle.

Modern analytics strategies must include:

• Skills assessment for all analytics consumers
• Role-specific training plans with practical applications
• Embedded analytics that fit into existing workflows
• Analytics champions who can translate insights for their teams
• Continuous reinforcement of data-driven decision making
• Clear metrics to measure the impact of data literacy programs
• Executive sponsorship that models data-driven behavior
• Collaborative environments where data experts and domain experts work together

When you land a solid strategy, your organization can win big, especially in high-touch industries like eCommerce and transactional SaaS services. BigCommerce reports, “every $1 invested in UX [for analytics tools] brings $100 in return—a 9,900% ROI.” This dramatic impact occurs because better UX dramatically improves adoption rates and ensures insights actually reach decision-makers.

The role of “data translators” has emerged as particularly crucial. These individuals bridge the gap between technical data teams and business users, translating complex findings into actionable insights. Organizations with dedicated translator functions report 2-3× higher analytics adoption rates, according to MIT research.

Building an Analytics Practice vs. Implementing a Tool

The most effective analytics initiatives I’ve seen follow an incremental, problem-focused approach–“outcome-driven analytics.”

This approach flips the traditional implementation model:

1. Start with specific, high-value business decisions (not data collection)
2. Map the decision process and identify information gaps
3. Design targeted analytics solutions for those specific gaps
4. Implement and measure impact in short cycles (4-6 weeks)
5. Scale successful patterns across the organization

Research from Gartner and McKinsey reveals stark differences in outcomes between traditional and decision-first approaches. Traditional analytics initiatives typically require 18-24 months for full implementation and achieve only 29% user adoption on average. In contrast, decision-first implementations work in 4-6 week cycles and achieve adoption rates of 84% — primarily because they deliver immediately usable insights that directly support specific decisions.

Scenario: Turning a Dashboard Disaster into a Decision Engine

Consider this common scenario: A mid-sized financial services company invests $2.7M in a sophisticated data platform but sees minimal adoption and impact after 18 months.

The problem isn’t technical–it’s strategic. Organizations often build dashboards without understanding the specific decisions they need to influence.

For companies facing this challenge, pivoting to a decision-first approach often yields better results:

1. Identify 3-5 critical business decisions that drive the majority of profitability
2. Map the information needs for those specific decisions
3. Build focused analytics that deliver exactly what decision-makers need
4. Integrate insights directly into workflow tools, not separate dashboards
5. Measure impact through decision quality and business outcomes

When implemented properly, this approach can yield significant improvements. One financial institution documented a 68% reduction in decision cycle times, 23% improvement in loan approval accuracy, and $4.2M in incremental profit—all while using less than 30% of the data they had originally planned to collect.

This pattern of success has been documented across industries in research by both Gartner and McKinsey.

The Paradox of Modern Analytics: Less Data, More Value

Here’s the counterintuitive truth about today’s analytics economy : the organizations creating the most value are often analyzing less data, not more.

According to MIT Sloan’s research on data-driven transformation, high-performing analytics organizations focus on “decision density” (maximizing the impact of data on key decisions) rather than “data density” (maximizing the amount of data collected).

The most successful analytics programs I’ve seen share these characteristics:

1. Decision orientation: Starting with decisions, not data
2. Minimum viable analysis: Using just enough data to improve a specific decision
3. Embedded delivery: Putting insights directly in workflows, not in separate tools
4. Closed feedback loops: Measuring how analytics change decisions and outcomes
5. Continuous evolution: Starting small and expanding based on proven value

Key Metrics for Measuring Analytics Success

How do you know if your analytics strategy is working? Traditional metrics like “dashboard usage” or “reports generated” tell you nothing about business impact. I’ll say that again. Usaging analytics tell you nothing about actual business impact.

Instead, track these outcome-focused metrics:

1. Decision velocity: How quickly can you move from question to decision?
2. Insight-to-action ratio: What percentage of insights lead to concrete actions?
3. Decision quality: Has the accuracy of key decisions improved?
4. Business outcomes: Have KPIs tied to these decisions improved?
5. Value density: Business value created per dollar of analytics spend

Starting Small: The Implementation Roadmap

If you’re just beginning your analytics journey or rethinking your approach, here’s a practical roadmap:

1. Month 1: Identify 2-3 high-value business decisions that need better data
2. Month 2: Build minimum viable analytics focused on those specific decisions
3. Month 3: Implement within existing workflows and measure impact
4. Month 4-6: Scale successful patterns to adjacent decisions
5. Ongoing: Build analytics capabilities incrementally based on proven value

The key is to maintain a relentless focus on business outcomes rather than technological sophistication. As Harvard Business Review’s research shows, the analytics programs that create the most value are often the least technically complex—-they just focus on the right problems.

The Future of Analytics Success

Despite continued evolution, the core principles of a successful analytics strategy and implementation remain constant: start with decisions, not data; focus on specific business problems; build direct connections between insights and actions; and make the human element central to your strategy.

By asking the right questions before implementing solutions, you’ll avoid the expensive analytics failures that continue to plague even the most sophisticated organizations. In a world awash with data, the competitive advantage isn’t who can collect the most—it’s who can extract meaningful action from it.

As you evaluate your own analytics strategy, remember that the goal isn’t to have more data—it’s to make better decisions that drive business value. Everything else is just expensive noise.