I recently finished a consulting project with one of the biggest health insurance companies in the US. I was part of the team that was engaged in defining and implementing the client’s data governance strategy from scratch as they didn’t have any data governance policy in place.
There is an abundance of articles and literature on data governance but most of these articles are created by technology provider companies, and their main focus is not to help people to understand data governance from basics but to market their solution and services. Such articles and webinars are usually not created to explain data governance in a layman’s language.
Let me take you on a journey of data governance by introducing the concept, things that you should consider and the success criteria. I am going to share a real-world experience that I gained while putting together a data governance strategy for my client. The primary objective of this blog post is to provide simple and easy to understand explanation on data governance, but you can also leverage it for your future data governance projects.
Let me first talk about what is data governance by providing some examples which you can relate to on a daily basis?
What is Data Governance?
Let us explore concepts of data governance in managing your personal finances. You will be surprised to know that you actively use data governance concept on a daily basis. First, you generate your funds either through salary or income from your own business. Then, you would create your budget for efficient and safe spending (investments, daily habits, transportation expenses, etc.) and at the end of the month, you will look at the spending trends or other kinds of reports to ensure that you are on track with the budget and exceeding it. If you notice more expenditure, then you cut down some expenses to make sure that you are back on track.
You are governing your finances. How?
Data governance is about acquiring (generating income), storing (investments, bank), and using (spending) data securely and efficiently so that it can be utilized for creating insights (spending trends) when making business decisions.
The diagram below indicates what data governance includes.
Data Governance Framework
(*Source — Data Incorporated)
Ownership — Who does the data belong to, so a business knows where to get data from?
Accessibility — How easily and quickly could a business access the required data?
Security — As the name suggests, ensure that no one could steal the data.
Quality — The data must be complete, accurate and usable to be suitable for generating insights.
Knowledge — I would replace this term with ‘data culture.’ Knowledge of data governance can be acquired from various sources, however you need to be motivated to learn and gain knowledge. Organizations must promote data governance to motivate everyone in the company to be excited to learn and adopt data governance.
Let us now talk about success criteria which you can use to plan your strategy.
Think beyond the data
You might say that data governance is only about managing and having the right quality of data. Well, technically, that is true but think about how, when and who generates data in the company? The answer is almost everyone and all the time!
How is the execution of business processes (e.g., filling out a credit card application- includes personal data, demographic data, income data), when-almost every second, who- everyone in the company (e.g., website, bank representative, etc.). So, when you decide to start a project around data governance, you need to think about how data governance would be extended to business processes (e.g., elimination of manual data entry, and validation of missing values), the systems in place (e.g., storage of data, and security of data), the employees (e.g., difficulties in getting data back, time taken to service a customer due to lack of information, etc.), customers (e.g., less wait time for representative, get the right details of product, focus on right messaging part of marketing, etc.) and everything where data is being produced.
Talk to people to understand their requirements and challenges
Data governance is a strategic initiative that occurs at the enterprise level, however it doesn’t mean that you don’t have to speak with tactical (managers) or at the execution level (operational staff). Keep one thing in mind: the reason that you need to govern data is because you would like to have the right data at the right time to analyze and gain insight that will help you make timely and well-informed business decisions. People at the operational/execution level produce data (e.g., customer care representatives produce complaints data, satisfaction data, etc.). That data is being stored by IT (through architecture and infrastructure) and then transformed into insights by building statistical models and those insights are then used by managers and leaders to make appropriate business decisions. Therefore, it is very important to talk to everyone in the company to first understand the need for data governance, requirements and challenges (operational staff and managers) that are aligned with overall business needs (executives).
Don’t create a governance structure/operating model based on the recommendation given by big companies
There are thousands of data governance operating models recommended by consulting companies; however, don’t adopt it just because it comes recommended. Do not forget that they don’t just recommend a model without practically implementing it at some client. To build out the model, they do all types of resource assessment, processes, technology, and data. There is a lot of groundwork that goes into it before they recommend it, and for that, they often get hourly rates ranging from $ 500–1000. Also, the operating model is not as important as the focus around how data will be protected, used, and managed throughout the enterprise, by streamlining business processes, securing systems, reducing the challenges of stakeholders in the company (customers/employees/external vendors, etc.). Then, take that recommended model as a reference or guideline and think along the lines of which operating model would be the most beneficial.
This is how I envision a complete data governance structure.
Engage business analysts
The role of business analyst is critical for data governance’s success. The first step is to understand business requirements, challenges and the need for data governance and that’s something business analyst could do by speaking with the staff at execution level to discuss their challenges, strengths, weaknesses, and requirements that would ease their day to day activities. A business analyst could collect this information and communicate it to tactical level staff and executives to let them define priorities for data governance, and structure of operating model. The business analyst plays the critical role of assigning resources to data governance operating model through organization assessment. Don’t just utilize them for documentation and preparing power point decks. Take advantage of their intellectual power.
Consider the maturity level of data governance. You can’t conquer the world in one single day. You need to identify the functional area that produces lots of data, its impact on the organization (e.g., sales, marketing), understand challenges and then implement the governance model within that particular function as a pilot. This will enable you to understand the success areas and challenges that could be addressed before it is rolled out to multiple functions and eventually, enterprise-wide.
Involve everyone within the organization
Stewards ensure that there are policies and processes in place for data governance, monitor the performance, address challenges of stakeholders, but ultimately it is everyone’s responsibility to follow policies, processes, and rules for data governance to be successful. If you are producing data, then you are responsible. Avoid a bureaucratic set up, as data governance is not a government. Include everyone in the company by organizing workshops to train people, have business analysts to work with an individual to address challenges, promote data governance by launching marketing campaigns and create incentives’ structure as part of employee appreciation.