Analytics function

Why is now the time to revisit your analytics function?

28 November 2018Matt Berry & Sam Miller

We all know that data is an asset. The world's most valuable resource according to the Economist in 2017. 

But while most large organisations now have an analytics function and probably some pretty smart tech that drives the numbers, many are still lagging behind when it comes to designing the organisation around this function. In short, while the data is not in short supply, the know-how and process around how to make it actionable often is.

Attention on both of these areas is borne out at Cognifide, where we already help some of the biggest brands in the world develop the capabilities needed to deliver a truly-effective analytics function. We know businesses are already using data to drive better decision-making, better experiences, better performance and better share price. But we also see that they are not doing enough to truly reach their data-led potential?

Just because a business has an analytics function, does not guarantee they are gathering, tracking and measuring the right data. Let alone using it and enriching it to maximise its potential and value to the business. The explanation for this can often be found in their organisational structure & ways of working.

Combining these two competencies enables a business to evolve and enhance the use of its data while enriching it and maximizing its value to the business and other stakeholders.

This has been articulated in a previous blog, about how to make analytics actionable: “People are just as important as the technology in creating actionable insight. Finding the right talent to turn data into insight is hard”.

This spurred us into thinking about how a lot of business value has still been left on the table. Previously we have discussed the 4 imperative steps for redesigning your business and operating model to realise benefit, as well as how to empower your business with analytics. Now we have a methodology and holistic framework to design a business intelligence operating model. So, how do you design a business to make the most of its data? 

The role of competencies versus capabilities

Organisation is a means of helping to deliver strategy and this is crucial. With an analytics lens, this means thinking about how data should flow to and from an analytics technology stack into different areas of the organisation. It also focuses on the capabilities needed to deliver the right data and actionable insight to support data-led business activities, such as data science, optimisation and personalisation. We need to recognise that people have different skills and the use of these skills must be optimised.

It is commonplace for organisations to be looking for people to be a ‘do-it-all’ under a general ‘data person or analyst’ title, but this approach doesn’t maximise the use of peoples’ specialist skills. Instead it asks them to engage in activities at which they are not expert and reduces the time they can spend on their primary specialism which is what really unlocks business value.

This brings us to our model:

How does it work?

The aim of this model is to remove the operational dependency on IT and empower other business areas with their own data & analytics capability. This can be achieved by uncoupling the analytics-focused capabilities from the underlying development of the analytics platform. This approach aligns the employees' competencies to where they are best served, so you can have an effective and efficient analytics function.


We see the data, insights & intelligence management activities needed for an effective, efficient and mature analytics function as detailed in the visualisation above.

How will this create value?

  • Allow analysts, to do just that - analyse, to create business value - rather than being sidetracked on the development of the platform
  • Facilitate the spread of insight through the business with activities focused on gathering requirements from and managing the outputs to the wider business
  • Encourage adoption and enablement of the analytics platform by making these activities accountable to a role
  • Management of the platform as a service approach to allow evolution and innovation of the underlying analytics platform technology alongside analysis
  • Move the business through the analytics maturity stages by creating a blueprint for bringing in talent with different skills
  • Creates momentum by formalising top-down and bottom-up processes needed to create a groundswell of data-led decision making across the business

If you are keen to understand more about your analytics operating model and how it could be improved to deliver more value, look out for our white paper coming in 2019 or reach out now for a chat.

Author: Matt Berry & Sam Miller
Published: 28 November 2018
analyticscustomer insightdataoperating modelsuccess measurement

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