5 questions companies must seek about their data and analytics maturity level

5 questions companies must seek about their data and analytics maturity level - CIO&Leader

“Data is a natural resource.”

Businesses today are aware that ‘data’ is the next big thing in the industry. However, the real question is how to leverage this data through a business model that is capable of assessing data capabilities? Thousands of websites, digital platforms, mobile applications, etc. are capturing and processing data in real time, to mine meaningful consumer insights.

In fact, when it comes to conversions, brands today have turned to data, to give a highly targeted personalized experience for customers through a host of analytics tools. But to truly realize the potential, how can businesses optimize their processes to achieve desired results? Can they align their verticals to reflect this data-driven approach to solving problems? How can they collate data from multiple sources? More importantly, how does a business assess their own standing (among competition) in terms of 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. 

Fig 1: Analytics Maturity Model

The purpose of the Analytics 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.
Itis designed to evaluate the mandatory requirements for a highly actionable data and analytics foundation.
Fig 2: Sample score of organization for Analytics Maturity model
The Maturity model 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 technology requirements, operating models, roles and responsibilities to further augment capabilities. Keeping this in mind, key areas that need to be appraised by organizations include:- 
1. Strategy:
Why are we capturing data and what are we trying to achieve with it?
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. It helps provide 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 to use the data to capture insights. 
2. Capability:
How do we organize the resource and skill sets for data maturity?
Another major part regarding the overall maturity of data and analytics utilization, 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 the organization’s operation model. 
Is there a 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? 
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. 
3. Technology 
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 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?
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.
4. Process
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 required 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.
5. Insights
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. 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
These aspects will clearly help companies identify where they stand compared to their competitor, in data and analytics maturity level. 
The author is Lead Data Analyst, DigitasLBi


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