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Where Do You Stand In Data & Analytics Maturity Level?

Where Do You Stand In Data & Analytics Maturity Level?

Renukanandan Aurangabadkar (Nandan)
Data Analytics Maturity Level analytics india magazine

“Data is a natural resource.”

It is quite common these days to find CEOs, CMOs and analysts quoting ‘data’ as the next big thing in the industry. But the real question is how do we leverage this newly-discovered resource through a business model that is capable of assessing data capabilities? The fact is that the power that we seek from data has, in fact, already found us. 

CMOs and CTOs of the world are right now thinking on how to use their own data and leverage its power for business growth. As we speak – thousands of websites, digital platforms, mobile applications are capturing and processing data in real-time, to mine meaningful consumer insights. To illustrate this phenomenon let’s take a step back to see how digital marketing strategy is evolving.

When it comes to conversions, brands today have turned to data, to give a highly targeted personalised experience for customers through a host of analytics tools. But to truly realise the potential, how can we optimise our processes from a business point of view? Can we align organisational verticals to reflect this data-driven approach to solving problems? How can we collate data from multiple sources? More importantly, how does a business assess their own standing (among the competition) in terms of the maturity of their data collection and usage? What are the key access points to evaluate data and analytics capabilities?

The answer is not a radical change, rather a slight realignment of processes that look somewhat like this…

Analytics Maturity Model

 The general purpose of this maturity model is to introduce a comprehensive framework, which makes it easy to assess the most vital parts while setting up a solid data and analytics foundation. It’s a road map that provides a guideline for the necessary means to ensure that infrastructure and processes enable frequent, regular optimization across media vehicles, digital, CRM and brand measures on basis of the business objectives.

Sample score of an organisation for the Analytics Maturity model

The maturity model is designed to evaluate the mandatory requirements for a highly actionable data and analytics foundation.

It can be used to evaluate the ability to deliver advanced digital marketing techniques that are at par with the evolving content consumption pattern. And in the process, assess the underlying factors such as the technical requirements, operating models, roles and responsibilities to further augment capabilities. Keeping this in mind, we have identified key areas that need to be appraised by organizations.  


The key question to answer is, why are we capturing data and what are we trying to achieve with it?

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A clearly defined strategy is the backbone of all advanced digital marketing activities and heavily influences whether or not the change towards a data-driven marketing culture is successful. Usually, the strategy is defined by the marketing executives, or at least empowered by them and communicated throughout the organization. The strategy provides the criteria to select business objectives and how to leverage data in order to achieve the anticipated goals. The key points of focus while framing strategy are Vision, Objectives and how are we going to use the data for turning it to insights. 

In short, the most favourable situation for an organization would be to have a clearly defined and well-communicated strategy, which comes along with business objectives and related KPIs. 


The key questions to answer is, how do we organize the resource and skillsets for data maturity?

Another major part regarding the overall maturity of data and analytics utilisation is the expertise of the relevant stakeholders. Providing a well thought out framework is essential for coordinating resources, empowering knowledge sharing and planning for individual skill development. The first step towards this framework is to analyze our organization’s operation model is. 

Is there proper coordination between different departments? For example, do the Social, CRM, E-commerce Marketing/Sales align together? Do they meet and plan campaigns? Are the roles and responsibility for each stakeholder clearly defined? How are they leveraging the collected data? Are they well aware of their respective roles in marketing activities? 

Thus, the highest level of capability maturity is achieved, when there is complete transparency of the individual capabilities, how they can be combined and what the concrete steps are to increase them over time. 


Key Question to answer is, what is the role of technology and how does it enable our goals?

Advanced digital marketing is largely dependent on the utilized tools and their capabilities. Whereas the concrete technology stacks can differ from client to client, they always are based on the same functionalities. For instance, you can expect a CRM system to be in place, as well as a web tracking tool, to capture user interactions and preferences but are these tools sharing the data with each other and does our team use the data in an effective way? In case if organizations have vendors, is the optimal value generated from vendor partnerships?  What are our competitor’s doing? Do we get training and support for our technology platform and tools used?

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Thus a close partnership with vendors, alongside a good understanding of their future developments and clearly defined training and support requests form the basis for a high level of maturity.


Key Question to answer is, how do we affect the culture so that data-driven decisions are adopted?

Having talked about capability and technology before, the next aspect is concerned about the needed processes to make the most use out of both. Vital parts are the data collection itself, how to request analytics services going beyond just mere Excel spreadsheets and how to guarantee reliable data quality all the time. We need to have a governance and operating model defined for our data collection. 

A highly mature process setup is built on automated, less error-prone data collection and refinement, accompanied by standardized ways to request and serve information requests.


Key Question to answer is how do we ensure data is turned into insight and ultimately action?

Finally, the last aspect of the maturity model is evaluating how data is turned into meaningful information outputs, to what extent relevant insights are generated and how those are operationalized. All the above mentioned are forming the most tangible and directly visible outcome of a mature data and analytics foundation.  In order to evaluate the organization maturity in this regard, it is investigated if there are managed expectations regarding the insight generation, how frequently they are derived and how valuable they are.

The insight maturity is expected to be on a high level, when information is not created to solely document performance but is actively incorporated into day to day thinking. Recurring information needs are served in a rather effortless and efficient way.

The analytics continuum

One can judge themselves on these aspects to know where they stand compared to maturity level!

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