Data Governance: Definition, Challenges, and Best Practices

what is data governance

What is data governance?

The process and practice of managing, protecting and utilizing the data is Data Governance. Data governance is done by the organizations in order to protect their data from all possible threats and cybercrimes in order to maintain data security against any misuse. To execute these processes there are specific policies and regulations in the organization itself which are to be followed in order to govern the data that they have access to.

It is becoming important day by day that their data is subjected to privacy. Organizations might encounter data privacy regulations and count on data analytics that will help in decision-making since breaches in data have become common lately. To protect the citizen’s and customers’ data, these organizations require a proper data governance program.

A full-fledged data governance team is formed along with a driving committee of governing body and data stewards. They work closely together to form the policies, rules and regulations, and set standards for implementation and enforce the policies of data governance.

Data governance is a vital part of the data management strategy and is responsible for desired business outcomes. Major goals like:

  • Risk minimization
  • Forming rules for usage of data
  • Compliance requirements to be implemented
  • Improvement of communication (both internal as well as external)
  • Create value out of the data
  • Cost reduction
  • Ensures that the company is long lasted by using risk management solution and optimization

What is the importance of Data Governance?

  • A data governance process is significant for any business to ensure that the data they are using is stored and protected.
  • Data governance ensures your protection against damaging and high in cost breach of data and cyberattacks.
  • Cost reduction in data management and improved Return on investment of data analytics.
  • Data governance reduces a load of data management from the IT team and spreads the organization’s burden.
  • Standards set for data help in improved communication and cross-functional decision making.
  • Compliance audits and compliance standards are managed and maintained easily.
  • Data drives business intelligence for mergers and acquisitions as well as for planning.
  • Data governance checks data growth which keeps it organized.

What is Data Governance Framework?

  • A data governance framework comprises the guidelines and definition of how the enterprises set up and enforce the data governance. It is a multidisciplinary approach to collect, store, manage, secure and use data regardless of how big it is.
  • BARC had recommended a few key points for implementation of data governance:
  • Defining goals and understanding their benefits
  • Analyzing current state and thorough data analysis
  • Roadmap derivation
  • Convincing stakeholders and budgeting projects
  • Planning and Development of data governance program
  • Implementing the data governance program
  • Controlling and monitoring

Four pillars of data governance are:

  • Data Stewardship
  • Data Quality
  • Master Data Management
  • Use cases

Data Governance Framework

After a careful consideration, these four principles were stated as data governance principles:

  • Data should always be recognized as a valuable enterprise asset.
  • Accountability must clearly be defined by the data
  • Data should be managed in such a way that it follows all the rules internally as well as externally.
  • Data quality must be retained and defined at all times all throughout the life cycle of data.

Data Governance Best Practices :

Data governance can be interpreted as some Data Police because of its strict guidelines and policies. Data governance handles data in a very uncompromising manner, which can be a concern in organizations. To promote user buy-in, avoiding resistance to government policies, experts in the field of data governance suggest that the programs must be business-driven with the involvement of data owners and the data governance committee making business-related decisions on data governance standards, guidelines and policies.

Another essential component of data governance is creating awareness about it by training and educating the users and data analysts about data use rules, data privacy, and their responsibilities in maintaining consistency in data sets. An open communication channel amongst managers, corporate executives and users regarding the data governance program is necessary via emails, webinars, reports etc.

There is a lot to learn from people who have been practicing data governance. But every enterprise is not the same, and you need to initiate the unaware maturity phase of data governance and rise it until it reaches the effective maturity phase.

Given below are some of the best practices of data governance:

  • Focusing on organizational outcomes and business values.
  • There must be an agreement at internal level regarding the accountability of data and decision right.
  • A governance model must be trust based which can be relied on data curation and data lineage.
  • A clear and explicit decision making that boils down to the ethics and principles.
  • Core governance components comprise of data security and risk management.
  • To ensure broad participation, there must be collaborative governance and cultural processes.
  • Starting small is an unsaid rule. Don’t try to reach the far end when you are starting out instead focus on short term goals and rise up gradually.
  • Setting particular, trackable and short-term goals. You can only control what you can measure. And start celebrating small wins as a reward for your efforts and move on to next goal.
  • Ownership must be defined at the very start as without that a data governance framework may fail.
  • Teamwork is what makes data governance successful, and everyone should be assigned their said roles and responsibilities.
  • Stakeholder should be educated whenever and wherever possible and required. Use of business-related terminology and interpreting theoretical bit of data governance into meaningful content should be done.
  • The data governance framework must be adapted in such a way that it weaves with your business methods. Execution of framework operations must be focused upon.
  • Mapping of tools, infrastructure and architecture should be done. The framework must be a pragmatic part of your organization’s tools required, infrastructure and architecture.
  • Developing definitions for data. It’s very important to balance the best out of centralization and localization.
  • Data domains should be identified. Data domain having the best proportion of impact as well as effort must be initiated with to identify increasing maturity.
  • Data elements of critical nature must be identified. Focus should be on the most critical data elements.
  • Defining control measurements in different business-related tasks, IT operations as well as reporting wherever required.
  • A business case must be designed. Merits of improving data governance maturity should be identified with regard to savings in cost, risk and compliance, business growth etc.
  • Leveraging the use of metrics by focusing on certain KPIs of data quality which can then be identified with genera performance KPIs in the organization.
  • Communication should be regular and frequent. Experts believe that communication is the key to data governance.

After all, it’s not a project, it’s a full-blown practice.

Goals of Data Governance

Data governance is required to make sound, confident and consistent business-related decisions by relying on accurate and true data in accordance with all other purposes for data usage throughout the company.

Some of the goals of Data Governance are:

  • Meeting all the requirements on regular basis and avoiding any fines.
  • Improving security of data by implementing data ownership and other responsibilities related to it.
  • Definition and verification of policies of data distribution involving the responsibilities of all the staff internal as well as external.
  • Value can be derived from data in the form of profits as well. Monetizing data is a goal everyone wants to achieve and to do so data should be collected and stored and should be available in such a way that it increases profits.
  • Data quality must be checked and the responsibility for keeping it in check must be assigned to a team member in order to track its KPIs with the general KPIs in the organization.
  • Better planning to avoid cleansing and structing data every single time.
  • Leaving no scope of re-work by obtaining trusted, secured and multi-purpose data assets.
  • Increasing efficiency of the staff by providing information that is of value to meet the threshold of data quality.
  • Increasing the maturity level of data governance by evaluating and improving the overall performance in each quarter.
  • Acknowledging gains and building on forward momentum to have a long-lasting commitment as well as an organization wide support.

Data Governance Benefits

There are many benefits of data governance due to which more and more businesses are nowadays implementing some sort of data governance be it master data governance, big data governance or enterprise data governance. Some major benefits of Data Governance are:

  • It helps in decision making.
  • Improved quality of data.
  • Data accuracy.
  • Using data to its full potential to arrive at sound business decisions.
  • Financial performance is enhanced by data governance.
  • Data becomes consistent.
  • It helps to increase the profits.

Challenges in Data Governance :

  • Apart from the benefits, there are also some challenges while establishing data governance rules and policies such as:
  • Data governance is a tiny chunk of the IT governance policies, both these initiatives have to in correspondence with each other to be fully successful.
  • Any sort of change or modification is difficult but making employees aware of the data governance policy and its importance is also not less than a task. They will require correct motivation to adhere to the new data governance policies and follow all the guidelines.
  • Data governance policies are supposed to be flexible according to the needs of the employees and for users. If the efforts are impeded, it may not be able to achieve desired business-related targets or goals.
  • For the data governance policies to be implemented correctly, there must be a mandatory rule for everyone in the organization to follow all the protocols for it to be successful.
  • A lot of focus is required in data governance. Departments are made to take out time for data management, so ensuring that these efforts are not going in vain is the business owner’s responsibility.
  • The lack of resources and limited funds and increasing competition is also a major challenge in data governance.
  • Sometimes there are many high-level, detail-oriented and very complicated data flows.
  • Third-party data and other data that the company is not responsible for or is not in control of can be a little challenging. Giving partial access to data as well.
  • A lot of the IT and operations teams are siloed or fragmented.
  • Many people do not understand the concept of data governance and misinterpret what it exactly is.
  • Lack of correct data governance tools or data governance software.
  • Having no or less knowledge of enterprise data governance or big data governance.
  • To have a weak data governance model.

Conclusion

The process of managing, protecting, and utilizing the data by the organizations to protect their data from all the threats and prevent any misuse of data is called data governance. A framework is required for all these procedures to get implemented. For data governance to be successful, the organizations must follow some of the best data governance practices to increase their maturity. Improvement in data quality, better decision-making, and data accuracy are some of the benefits of governance. Even though it has its merits and demerits, one cannot deny that every enterprise needs a proper functioning data governance strategy to ensure better business operations and make sure that the data is trusted and secure.

What are data governance tools?

In order to implement the policies of data governance, there are a lot of functions to be performed such as fixing data issues, data quality improvement etc. and to execute these functions you require specific tools or software.

What is a data governance analyst?

An analyst’s job is to manage the data flow while making sure that the integrity of the data, its efficiency, accuracy and accessibility is maintained. They can also help in monitoring data and providing guidance with data management.

What is data governance strategy?

A data governance strategy is the how of data governance practices. How the data is stored, collected, named and processed. A well-defined data strategy makes sure that the data is used in an efficient manner in the company.

What is the purpose of data governance maturity model?

A maturity model in data governance is a tool that lets its users keep track of the performance of people or a group of people and also helps in finding out what else can be done in order to improve their caliber and performance.

WRITTEN BY

Himanshu Mishra

Technology Head
Himanshu is an entrepreneur with 17+ years of overall experience in strategic and advisory roles in Senior Management, IT program management, Quality Assurance, and ERP implementations. He has invested in companies focused on Digital Technologies and Healthcare industries. He has previously worked in domains like: Technology, Finance, Marketing… Read more

0

Leave a Reply

Your email address will not be published. Required fields are marked *