Data is the New Oil ?

How do we apply Data Governance in an enterprise ? What all require to make a successfully data governed Organization!

Data — An Asset to the Organization:

Data is the most important asset any company have, without data, companies does not know who the customer is, bank does not know how much is the balance a customer has on his/her savings account, a customer does not know how much is the principal outstanding to clear his/her loans to the bank. So it is not incorrect to say without data no company will survive.

Data — Fuelling for the business growth:

Data consumed by utilizing using the modern tools and technologies, and with the appropriate domain expertise can help the company use its historic data, learn insights from the data, and make the company grow further.

Data — Deserve enough to secure as like a physical asset:

As Data makes the business to function, helps to take decisions and leads the company to grow better, Data needs to be protected always so that outsiders (hackers) won’t be able to seize the data by any means.

Data — Secure, Protect and maintain Privacy:

Regulatory bodies define the rules and regulations so that every company has to comply with these rules. Some examples of regulatory bodies are GDPR, CCPA, SOX, etc.

General Data Protection Regulation — GDPR is the important body that impose legal compliance on collecting data related to people in EU. It focuses on security of data for instance how the Individual’s data is used, and where the his/her data is stored. Also it emphasize on the privacy of using this data.

California Consumer Privacy Act — CCPA define rules when using consumer data, it helps to give more control over the Individual’s personal information that a business enterprise collect about them. Consumer can provide consent on using his/her data for analytical purposes.

Defining Data Governance:

According to DAMA International: Exercise of authority and control over the management of data assets.

According to Gartner: The specification of decision rights and an accountability framework to ensure the appropriate behaviour in the valuation, creation, consumption, and control of data and analytics.

In General, Data Governance is a framework which provides the policies and procedures used in the management of data assets to ensure consistency in data, and to comply with data protection regulations.

Time Vs Value — Do we need to re-build the Organization to achieve data governance ?

One important point to remember, Many of the enterprise would be doing this already in a informal way, In that Case, We may need to formalize them to create a framework & define charter quickly what the company does currently so that data governance can be enforced quickly and formally that can help to improve the process as well than creating them from scratch starting for data governance.

Some strategies and policy initiatives that help to understand the data governance quickly:

1. Data Governance is a framework — Is it really feasible to do health check to know how are we doing.

2. What is our policy on collecting and storing Personally Identifiable Indicator data that mean PII & SPII data?.

3. How do we address data inconsistency issues.

4. How do we address data security breaches.

Why do we need Data Governance:

1. For Instance: CEO is looking for the ARPU information. Finance Team responds to the question — It is $ 5500, whereas Sales Team and Analytics Team comes up with different numbers. Here is the confusion arises on which number is correct, It reduces the confidence to use these numbers, and raising doubt on the accuracy of these numbers. — Primary issue here is the Data Consistency, There may be siloed systems in the company, But the data should be consistent across so that the version of truth is single and justified.

2. We have seen earlier about regulatory bodies defining privacy laws, and data security rules to comply with. In order for the company to survive, these compliance are mandatory or else company would face legal battle, may go for a toss on the brand image.

We have gone through data governance definition, and why do we need this, We will look at who does what under the Data Governance framework.

Various Roles in Data Governance:

Executive Sponsors — Who takes care of sponsorship needed for the Data Governance program.

1. Administrator — Admin is leading the Data Governance program, facilitate meetings, move the program to the next level as per strategies and policies defined by the council.

2. Data Stewards — Business Steward, Systems Steward, etc — Who takes care of the day-to-day work — maintaining the data assets, providing the data access, ensures data security is maintained.

3. Data Gov Council — Council having members from different parts of the enterprise, taking care of defining policies and strategies, KPIs, Review and communicate them to all members of the council, and the stakeholders.

4. Data Consumers — They are the consumers of the data, who can help to measure the effectiveness of the data governance program by responding to surveys, raising requests for new data additions, raising requests for data issues, errors, and breaches.

Data Governance Framework Components:

Pre Implementation steps — Define & Implement Governance Methodology.Charter DocumentRoles & Responsibilities of each of the membersKey Performance IndicatorsPost Implementation steps — How to maintain this health governance going forward.Measure of KPIsCommunication to stake holders — Success stories, Next Steps, and follow up meetings.

Pre Implementation Steps — Data Governance Methodology:

Charter Document:

PurposeDefines the purpose of the programScoping & DescopingThis says what systems (Eg: Sales data, Marketing data, Product Data, Customer Data) need to be considered & scoped for this work, Any Exclusions for this ?

Roles & Responsibilities:

Who is assigned to do what ?How do we know — Are we on the right direction ?We need KPIs to measure the performance of Data Governance Team.

Key Performance Indicators:

1) Connect with consumers and get back the consumer data scores — Higher scores better.

2) Number of new data requests to scope in — More requests are expected and better during initial phase, followed by that number of request should reduce drastically.

3) How sooner is the datasets access request take to provide access — Faster is better.

4) Security checks followed when providing access — Stringent check confirming the authenticity.

5) Data Recency — Is the data closer to real time, or any latency time. 0 Lead time better.

6) Any breaches from the compliance — 0 breach is higher performance.

7) Data Inconsistency instances — 0 instances better.

Post Implementation Steps — Data Governance Methodology:

Documentation at every step helps to maintain the health process followed that include new data scoping to resolving data issues.

Measurement of KPI:

1) Regular meetings to review the list of KPI and update the charter if any new KPI or any changes to the charter and roll out to stakeholders.

2) Also Review the value of measures to the KPIs to find — Are we in improving trend ?

Consumer data scoreIssues getting addressed fasterAccess requests faster in servingData close to real time — data refreshed more frequentlyAudit logs for all the data access transactions.More Automated way of managing requests and issuesSelf-resilience on measuring the breach or issue that is going to happen, predict early before happening and take action to prevent them happening.Use AI tools wherever possible in the journey and take proactive measures.

Communication to stake holders:

Success stories, Next Steps, and follow up meetings.

1) Share success stories showing instance where data is more accurate or more consistent.

2) Any measured improvement done successfully

3) Areas of improvement if any

4) Any new data due to changes in the enterprise Eg: Mergers, Business enhancements

5) To share the status proceedings — who is doing what, next steps, check point meetings, etc.

Cheers!

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