No board room agenda today is complete without a mention of Data centricity, yet no one has yet been able to put a finger on what exactly data centricity is all about. Most of the organizations, even those that are in the business for the past 100 years,
are suddenly woken up to the idea of data centricity. Focus on data has not been a recent phenomenon, the social and machine data has led to the data explosion. Organizations were adept at mining data even before data explosion and what better example than
an annual report could we cite of Data centricity?

Despite all the buzz around data centricity, except for the e-commerce industry, no other industry has yet been able to exploit social data effectively. Question is how much of data is getting mined and is even useful for analysis. With-out real use cases,
Business justification, any program around Data will remain a pipedream. While everyone talks about Data Centricity, no real attempt however has been made to put a logical sequence to achieve it. Ask any organization that embarked on Data modernisation journey
in the past decade or so, will admit behind closed doors that nothing really has changed on the ground. In the name of data centricity, while technical debt has increased by implementing Data platforms, lakes, marts and vaults, business see them as the shiny
new toys of technology. Organizations struggle to leverage data platforms as no one thought about establishing business context and taking business along as a result the very users who were the intended beneficiaries dumped it.

Data centricity does not demand a technology solution rather screams for business ownership, impact, and outcomes. Getting into the nitty-gritties is often painful and that’s what defines the success and failure of a Data centricity vision.

Let’s look at the pyramid of Data centricity below and understand how various layers need to be carefully put together to infuse life into hackneyed concept of Data Centricity. In this blog, I will touch upon the first two layers i.e. Pillars and Cardinal
Principles as organisations often struggle to put first foot right.

*Will delve deeper into foundational capabilities and Data controls in my next blog along with Pillars and Cardinal principles.

Pillars of Data Centricity:

• Cost and Value: What’s an asset without any intrinsic value and importance? Since Data is recognized as an asset, it’s important for organisations to arrive at the value of Data and put required controls in place. It’s neither practical
  nor advisable to focus on all the data assets, identification and prioritization of the most critical Data assets is highly recommended.
• Literacy: Knowledge about the business context of the data besides its type, size and usage is important for defining and measuring key Metrics and KPIs like Customer centricity, Compliance, Revenue.
• Democratization: Unless data is freely available in the hands of people who are required to mine it to generate insights, an asset remains notional devoid of any real value. Availability of trusted data on time is key to the success of Self-service
  enablement.
• Residency: How data gets collected, shared and consumed is driven by the law of the land. Organisations operate in a multi geography landscape and are bound by the laws of the respective countries for data protection and privacy. Data
  Sharing and access hence is critical for achieving the vision of Data centricity.
• Culture: No level of strategy or technology investment can bring in Data centricity unless the people at the grassroot level start appreciating the importance and consequences of data handling.

Cardinal principles of Data centricity

• Ownership: Ownership is key for establishing accountability and making sure that Data domains are properly defined, and Data products are delivered as per the business demand. Ownership must be seen with the criticality of the Data.  A generic
  ownership matrix cannot consider the complexities and realties of an organisation. Each Op model needs contextualization to reflect the business reality.
• Harmonization: Remove multiple definitions, establishing common standards, definitions and policies go a long way in data harmonization. Marketing, Compliance, Servicing teams cannot have different definitions of customer. 
• Traceability: Regulations have placed increased focus on auditability and traceability. It’s important to understand the e2e processes and map the data flows to the underline processes. Understanding of Data lifecycle will provide necessary
  insights.
• Fit for purpose: What use data is if cannot be used without confidence. Quality of Insights is as good as the quality of input Data. Hence rigorous Data governance and Data Management are essentially to ensure Data quality. Data Quality
  needs a holistic approach covering both Business and Technology concepts. There can be nothing farther from truth that by putting DQ tools, Data quality has been achieved and all that needs to be explored now is AI/ML.  
• Secured: Data security is no longer a compulsion but critical for the very existence of business. Security cannot be achieved by just defining a security policy and procuring sophisticated tools. Data security needs arise at all the touch
  points from collection, processing, usage, Access, storage to sharing internally and externally. Aligning Data Security with Privacy and Governance will help close the loop.

Once organisation figure out Pillars and Principles, putting capabilities and controls become lot easier. Let’s discuss that in out next blog.