CxO's Roadmap to an Analytics Journey

In my conversations with senior executives, the common query is always around their organization's analytical journey.

How to build an analytics road map for my organization? What's more important, Prescriptive or Descriptive Analytics? Are we ready for AI or Machine Learning?

My first objective is always to help them understand the economic value of their data .i.e EvD. The next step is to assess the organization's present analytical maturity level and then design the different phases of the adoption cycle.

The following points talk about some important building blocks of the analytic journey's roadmap.

Determine the Economic Value of Data (EvD)

Identify the necessary data points which are required for analysis. If they are not available, develop a solution for data capture and data enrichment. Develop the relationship between different data sets.

It’s the relationships and patterns derived from the data that are valuable.

Business heads understand that monetization value is in the unique customer, product, service, operational, inventory, financial and market insights that are gleaned from the data.

EvD is determined from these insights and helps the organization to the identification of new products, new customers, new markets, new channels, and new services.

Assessment of the Organization's Analytics Maturity Level

Classify your organization in one of the following maturity levels. Based on the assessment of the present analytics maturity level, future phases will be planned as per the criteria shown below.

It's important to assess your analytics maturity level properly as it acts as a foundation and helps in identifying the action items which help the organization to move to the next level. For e.g. without a proper data foundation and historical information, we can't build a strong predictive model.

Choose Appropriate Analytics for a Business Question

Based on your business questions, identify the best applicable type of analytics. Four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive.

With new technologies like AI, NLP, and ML, we have now new addition in the list i.e. "Cognitive Analytics".

Important Note: There is no need to ‘complete’ building out descriptive analytics before moving on to advanced analytics. Also, there is no certainty that higher levels of analytics bring more value.

Build the Right Team for Adoption Cycle

In order to implement an analytics roadmap for an organization, you need to choose the right team. Generally, a data science team comprises of different roles like data analysts, data engineers, data scientists, Analytics Manager and Director of Analytics.

Each one has a crucial role and contribution in an analytics project. My recommendation is always to work initially with the right analytics partners who bring experience, best practices in the process and make sure the foundation of the analytics roadmap is built strongly.

Conclusion:

It's important to understand which parts of an analytic road map are applicable to your business and how we can achieve the best ROI in implementation. While different forms of analytics may provide varying amounts of value to a business, they all have their place.

If you are interested in building an analytics culture and how to use analytics for data-driven decisions then feel free to connect me at [email protected].