Maximize Analytics Value Across the Enterprise

4 min readApr 5, 2022

Unlocking enterprise value of data is akin to sharing baby clothes.

My partner and I live in NYC with our daughter. We have good friends who live in Canada whom we often speak to, as they have a daughter 6 months older than ours. One of the great things about this relationship is not only do we great great advice and anecdotes about the fun stages upon us, but we get donated clothes! What’s key in making this work is that our friends know enough about us to do this effectively; they know the age of our daughter and therefore the approximate size of coats, sweaters, hats etc. They know the what clothing is going to be most relevant for the time of year, they know the gender of our child and therefore perhaps the right color, and finally, our family lifestyle and activities — we love spending time outdoors so we get lots of warm jackets and outdoor play clothes.

This context allows our friends to share clothes and other items with us effectively so that they know what will be helpful and when.

Imagine if we could do this with data across teams inside our organisations? If we knew what insights and models would add most value, to the right team at the right time. This sort of effective collaboration will unlock huge value in our data.

In a December 2016 report on the state of data analytics McKinsey concluded that slow progress was being made despite large investments in technology because companies “have failed to make the organizational changes required to make the most” of big data. Despite a strong desire for success these expensive big data investments are not producing meaningful results”

Unlocking value of analytics

I was recently the Chairperson at the Chief Analytics Officers Fall event and the subject of unlocking business value came up again and again. How can we leverage the collective intelligence of our organization and embedded analytics teams? How can we maximise the utilisation of analytic tools and skills across the organisation and break silos of best practice?

The conversation came back constantly to people — business and analytics interactions and an effective data culture.

Here are some things to consider build a data culture in your organisation.

Translators provide business context

Embedded analytics teams was a popular model and provoked much discussion and the sharing of great success examples. What is critical here, is that analytics team members (what we refer to as translators) obtain the right business context. “The worst thing that could happen is that it is seen as a request to the analytics department” was a comment that was well received.

Analytics translators require a facilitation skill here. Clarifying the business challenge at the start of the project and reviewing throughout in an iterative way — driving for clarity for both business and analytics teams members and achieving context is key.

Ways Of Working

We’re starting to realise that the promise of Business Intelligence tool providers is falling short — specifically with self-serve analytics. “Strapping a pretty dashboard on the end of a poor process is not good enough”. Instead, translators need to support business teams to be hypothesis led in there thinking and list and challenge around the supporting assumptions. This is a step-change away from asking for a report towards effective collaboration and ways of working between these groups. Or as an analytics leader said to me recently ‘moving away from being stick-fetchers for the business”.

Asking Better Questions

The skill for business and analytics teams will be to quickly and effectively identify their gaps in understanding. Herein lies the source and route to better business questions! As a former British Commando Intelligence Officer we often used the phrase “Be the first to Understand, first to Decide and the first to Act”. Here is where data drives better decisions in business teams. The translator here facilitates a discussion by applying techniques such as Key Assumptions Check or What If? analysis to act as a handrail for the discussion in meetings.

Promote a culture of sharing

When translators across your organization are working effectively with embedded analytics teams, you will see them prompted to ask “how can we effectively share and re use this insight?” There needs to be role modelling and support from senior leadership is key here. “Information is power” cannot work in an effective data culture.

Culture is where the power of data and the organization is unlocked.

If you’d like to understand how you can implement a data culture in your organization, contact us at see6 today.




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