The diagnostic analytics gap

Why this gap is relevant and how to close it

Showing what’s happening in dashboards is informative, not insightful. It’s useful to know if a key business metric is going up or down, but it’s not actionable. Only the “why” behind these changes can drive recommendations and actions.

However, I often hear how teams look at dashboards in weekly/monthly reviews and comment that metrics went up or down, without clear answers to root causes or actionable insights. This leads to missed opportunities to drive business impact through data and analytics, hindering the agility needed to stay ahead of the competition.

Why is this relevant?

Analytics is still primarily descriptive in most organizations (i.e., WHAT’s happening).

Few data and business teams have nailed diagnostic analytics (i.e., WHY this happened).

As external events affect the business and stakeholders take action, metrics change as a reflection. Businesses need to understand what drives KPIs and why they are changing to make better decisions. This can only be achieved by nailing diagnostic analytics: providing comprehensive insights into metrics changes at the speed of business.

But oftentimes, stakeholders and data practitioners come up with hypotheses on why metrics changed based on their domain expertise, intuition, and experience. These reasons are typically high-level and just scratching the surface by testing 2 or 3 usual suspects, leading to missed opportunities.

The analytics landscape

On a high-level, diagnostic analytics is often overlooked as it lies between data and the business. It’s helpful to compare it against the other types of analytics types.

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● Descriptive analytics is self-served through dashboards. Data teams manage governance and infrastructure and enable business teams. There are clear processes, ownership, and tools.

● Diagnostic analytics lies between the business and data. It’s nearly impossible to self-serve diagnostic analytics through common dashboards (although many data teams fall into this fallacy). There aren’t clear processes and the data culture varies significantly.

● Predictive and prescriptive analytics are managed by the data teams with clear data products (e.g., recommendation systems, and forecasting models) used by the business folks. They are technically complex, but very clear concerning processes, people, and culture.

What’s causing this gap

As data grows in volume and complexity and businesses change faster than ever, the gap between business needs and delivery keeps widening. Most organizations overlook this gap for various reasons. A deep understanding of the gap requires a holistic approach based on the 4 key elements: people, culture, tools, and processes.

Let’s go deeper into each one of the 4 key elements.

 

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How to close this gap?

Ideally, business and data teams would proactively and regularly analyze metrics changes. Even if the change is minor, there are typically opposing effects. Identifying them is crucial so that teams can quickly differentiate what’s working and what’s not working and take actions to maximize business impact.

Where to focus depends on the starting point. It requires a comprehensive overview of the 4 elements described before. Based on conversations with dozens of data and business leaders, I often see three scenarios:

The 4 states of diagnostic analytics

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State 1- Stuck in the “what”

Companies lack the data culture to drill down into the why. Teams monitor metrics on dashboards, but rarely identify data-driven reasons as to why they changed.

Actions: Foster data culture and show the value of diagnostic analytics to business stakeholders

How:

• Pick 2 or 3 use cases where you see significant potential to deliver business value. Proactively investigate changes in these business metrics and share your findings in a structured and impact-driven manner
• Continue until you achieve a couple of success stories. This should be the base to further expand into additional use cases and start the change management process

State 2 - The usual suspects

Teams end up only scratching the surface of WHY metrics change and only test a couple of usual suspects regularly.

This is the tricky one because it lacks symptoms. Business stakeholders just test their high-level hypotheses, leading to missed opportunities. Data teams believe that they are doing a great self-service analytics job.

Actions: Raise awareness that not drilling down to the why is leading to missed opportunities

How:

•  Pick 2 or 3 critical use cases where you know that business teams are only looking at the usual suspects. Proactively drill down to the why and share your findings by comparing comprehensive analysis vs previous analyses that were only scratching the surface
  
  
• Build out success stories and expand into more use cases

State 3 - Need for speed

Business is aware of the value of drilling down to the why but struggles with the speed-comprehensiveness trade-off.

In this case, data culture is already advanced and teams are only missing the last mile of the marathon. Most teams are aware there is business value to be unlocked, but struggle to accelerate diagnostic analytics. Performing comprehensive root-cause analysis with existing workflows is too complex and time-consuming. Teams struggle to deliver insights at the speed of business.

Action: Augment current workflows to accelerate speed to insight and eliminate the speed - comprehensiveness trade-off.

How:

• Consider augmented analytics platforms to accelerate speed to insight and eliminate the trade-off. These tools leverage ML to automate hypothesis testing and driver scoring. This way teams can get insights at the speed of business and you can scale analytics without throwing more analysts at the problem.
  
  
• Review current diagnostic analytics procedures and ensure a proper way to conduct these analyses, share insights and organize them.
  
  
• Increase the collaboration between data analysts and the business for the most important use cases to develop domain expertise

State 4 - Full Force

Teams proactively diagnose metrics changes for constant improvement. By augmenting traditional workflows teams eliminate the speed- comprehensiveness trade-off, share actionable insights proactively and capture a wide range of opportunities.

This state can be showcased as a best practice. The teams work collaboratively and proactively, processes are well-defined and clear, and the right set of tools are implemented to drill down to the why and find actionable insights. As a result, the ROI of data analytics is clear and there is strong trust in the data culture.

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Conclusion:

Showing what’s happening in dashboards is not enough. Drilling down to the why comprehensively and quickly is the only way to deliver actionable insights that increase the value of analytics. This is the way to conquer the last mile of the data journey.

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Thoughts? Reach out toJoão Sousa. Joao is the Director of Growth at Kausa.