Published on Mon 17 Oct, 22


JoΓ£o Sousa

Sales Manager

For many companies data is not delivering on its promise (and ROI). Because the data journey is like a marathon and business value and impact start coming in at the last mile.

What is referred to as the last mile of the analytics marathon comprises data analysis, insight communication, and taking action. However, most companies are falling out of the race before tackling these last three steps and, as a result, fail to achieve the expected outcome. brent dykes analytics marathon.png (image by Brent Dykes)

Not delivering value can quickly start damaging the data culture and could lead business teams to overlook the efforts spent in the earlier stages of the data marathon. Data practitioners that want to create a strong data culture and have a seat at the decision table should carefully evaluate whether these habits are hindering their teams

What’s holding data teams back?

While there isn’t a straightforward answer to this question, I have identified six bad analytics habits based on many conversations with data & analytics leaders and practitioners. This list comprises topics that lead to costly decisions and missed opportunities to deliver business impact and are related to the four key elements: people, processes, tools, and culture, which are:

The six main bad analytics habits

  • Treating data/analytics/BI teams like a dashboard factory
  • Analyzing only a portion of the available data
  • All descriptive without diagnostic analytics
  • Waiting too long to act
  • Making it impossible to ask follow-up questions
  • Failing to standardize and streamline insights stories

The following image summarizes the relation between each bad habit and the corresponding root causes across the four key elements:

Main reasons bad analytics habits.svg

1. Treating data/analytics/BI teams like a dashboard factory

These teams do not have established processes or frameworks to structure workstreams and gather context and information. Hence, data teams receive many ad-hoc requests and are asked to create one dashboard after another without proper context. Often they jump into analyses without asking for clarification or understanding of the ultimate goal (i.e., what is the business trying to achieve). These communication gaps between data and business teams lead to working based on assumptions and data analysts answering the wrong questions. The culture of asking "why" is also often missing.

Impact: Wasted time and delay in critical decisions.

How to break it:

  • Involve data teams earlier in the process and align on expected business outcomes and what actionable insights look like
  • Foster a strong collaboration between data and business teams
  • Develop processes to make people reflect on the why.
  • Consider embedding your analysts into the business teams.


2. Analyzing only a portion of the available data

Most companies only analyze a small fraction of the data they collect. Teams often just look into the top drivers and the usual suspects (i.e., the usual slice and dice), which results in testing very high-level hypotheses. There are also data silos that prevent teams from leveraging all the available data (e.g., the marketing team only looks into marketing data without combining it with orders or product data).

Impact: Missed insights and opportunities

How to break it:

  • Go beyond the available slices and dices on dashboards and start analyzing all actionable dimensions in your data
  • Go deeper by looking at combinations of multiple factors and leveraging different types of data (e.g., customer, product, and marketing data)


3. All descriptive without diagnostic analytics

Showing what is happening in dashboards is informative but not insightful. Only the β€œwhy” behind these changes can drive recommendations and actions. However, teams often look at dashboards in weekly/monthly reviews and comment that metrics went up or down, without clear answers to root causes or actionable insights.

Impact: Lacking actionability and missed business opportunities

How to break it:


4. Waiting too long to act

Teams spend so much time analyzing that they fail to act on time. As businesses change faster than ever, the speed to actionable insight is getting more relevant. Business stakeholders need quick and actionable insights to make informed decisions before it is too late. In most cases, teams need insights on the same day, and waiting a couple of days or weeks is too long for the business to take action.

Impact: Reactive teams, missed opportunities, undermined data culture

How to break it:

  • Leverage technology to augment current workflows and accelerate speed to actionable insight
  • Spend most of the time on uniquely human value-added-tasks like insights communication and decision-making


5. Getting stuck in a loop of follow-up questions

When diagnostic analytics are performed using dashboards, follow-ups are inevitable. To understand why things are changing and how to act on them more granular insights (e.g., combining 2 or 3 different dimensions) are needed. Manual analysi could require multiple iterations and creates follow-up loops as initial answers are only scratching the surface. However, it is often hard for business leaders to answer their questions without the support of the data team.

Impact: Overwhelmed data teams, unanswered questions, analyses that only scratch the surface, and missed opportunities.

How to break it:

  • Invest in tools & training that empower business leaders to answer their questions
  • Consider decision intelligence platforms that enable stakeholders to drill down to the why


6. Failing to standardize and streamline insights stories

Reports and analyses are often distributed across various platforms and lack a consistent structure: Slack messages pointing to dashboards, Excel sheets with ad-hoc computation, PowerPoint slides, Notion pages, tickets, etc,... Even if insights are uncovered, they do not make their way to the decision-makers, or their relation to business priorities is not well-explained.

Impact: Difficulty in interpreting results and drawing actionable insights, missed opportunities.

How to break it:

  • Establish common formats for reporting. Leverage standard methods like waterfall charts to summarize the key metrics changes.
  • Develop and promote best practices for data storytelling


Bottom line: focus on the last mile

Starting with the way teams communicate and collaborate and extending to the tools used to uncover insights and the process to distribute them, these bad analytics habits are hindering teams from conquering the last mile of analytics and leading to missed opportunities…

Where to begin?

  • Carry out a comprehensive assessment of your current situation to see if you fall into these patterns
  • Focus on what is holding you back the most
  • Pay attention to what is breaking your value chain earlier in the process and causing a domino effect on the next steps

Habits are hard to break. Take your time to assess, prioritize and keep introducing incremental changes.


References: Brent Dykes, Data Analytics Marathon: Why Your Organization Must Focus On The Finish (2022),

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About the author and Kausa

JoΓ£o Sousa is the Director of Growth at Kausa. Stay tuned for more posts on how to increase the value of analytics.

Kausa accelerates data exploration, delivering actionable insights in seconds by testing all hypotheses comprehensively and continuously.