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How to Measure the Business Value of Data Governance

Data Governance is vital for many enterprise information capabilities such as master data management, reference data management, data quality, data insights, and more. It requires collaborating within a complex and robust combination of factors such as data stewardship, policy definition & management, and decision rights. With a plethora of factors that are involved in running a data governance program, it is necessary to be able to measure the business value of data governance.

The previous blog on Why do you need to Opt Data Governance for your Enterprise? helps to understand the overall benefits of opting for data governance and transforming your data into insights. The important step after enforcing the data governance policies is to measure the business value of the data governance, by creating metrics in accordance with the business relevance. The metrics must be capable of being measured empirically and must be meaningful to reflect the impact of data governance on the business. The metrics should serve the process of providing insights and how the organization uses the data to support its processes of the business and accomplish its objectives and make business decisions.  For instance, data governance policies regarding data quality must lucidly define the business. When you are initiating data governance in your enterprise it is vital to identify types of metrics that are relevant to your business.

Data Governance is an area that is challenging to measure as it is not only the tool but also the design of the overall process. It is vital to consider multiple types of metrics that can portray the complexity of the data governance programs that deliver value to your company. It is a challenge to understand your goals, issues, and also the impacts of resolving the issues.

Importance of Metric in measuring the value of data governance:

Measure the impact of data governance efforts in business terms

Reduction in the risk scenarios

Reduction regarding the data rectification costs

Improvement in the data quality count

We have to define the metrics by identifying the issues. This will enable us to change and improve accordingly.  Most of them include followingMetrics to be measured:

Data Accuracy

Effort to Comply

Cost Reduction

Audit Frequency

Submission Time

Metrics can be used to measure the performance in the the following key areas:People

Reviewing and prioritizing issues in Data Governance meetings

Training as per new system/process

Data governance adoption rate – via survey

Number of domains vs number of data owners

Process

Identifying the number of data issues

Setting up a system that includes delegating or escalating processes for managing disputes regarding the data issues

Number of issues and the frequency that is being escalated to the Data Governance Committee

Apportionment of time right from issue identification to resolution

The number of implemented policies, standards, and processes approved

Technology

Number of data fields that are traceable from source to the usage

Number of the spreadsheets that are used

Number of the data sources that are consolidated

Number of duplicate products and unique identifiers

How Benefits of Data Governance Creates Wealth of New Opportunities ?

Data is the core of digital transformation and Data Governance is the need of the hour in the most happening Digital Universe today. Data Governance helps unleash the power of data by processing unstructured, non-uniform and duplicate data to deliver business insights. Data Governance is all about managing data as an asset and involves ensuring strategic control around data, its use, structure, content, and safety. For effective Data Governance, we need to understand what type of data exists, its quality, who is using it, for what purpose they are using it, and who is accessing it, and all these use cases are governed, compliant, and secured.

Businesses are embracing new age technologies such as artificial intelligence, machine learning, and advanced analytics, as the world progresses towards digitization with the velocity and variety of data is increasing. Different types of data require different types of control and governance processes with guidelines to ensure that systems can store, manage and analyse the data both effectively and correctly.

From Chaos to Clarity

By orchestrating people, process, and technology processes, Data Governance enables an enterprise to leverage data as a business asset. Data Governance includes understanding and effectively managing data that helps enterprises to derive maximum value from it. Implementing a Data Governance strategy comes with a lot of challenges and its approach should serve the enterprise data needs and requirements.

Data Governance helps to build Data Trust and its initiatives are driven to focus on improving data quality. Data Governance helps increase the value of an enterprise, the greater the enterprise is at governing data, the greater of positive outcomes. Data proves to be very useful when used appropriately.

With Effective Implementation of Data Governance, you can achieve the following:

1. Swift Decision Making which is possible by:

Decreased reporting errors

Easy access to reliable data

Transparent communication and improved standardization

Speedy acquiring and analyzing large sets of data

2. Increase in Revenue which is possible by:

Improving customer experience

Optimizing marketing strategies

Introducing new products

3. Enhanced Data Privacy and Risk Management which is possible by:

Increased efficiency management of financial, operational, and regulatory risk by ensuring data integrity, security, privacy, completeness, and consistency.

4. Improved Capabilities which is possible by:

Increased collaboration, productivity, and efficiency that enables enhanced knowledge management

Transparency can be achieved with holistic business process documentation

By establishing Data Ownership the Data Security can be improved

Confident and consistent business decisions can be based on the data that is trustworthy

Defining and verifying data distribution policies that include the roles and accountabilities

By documenting the lineage for the Data Assets, enterprises can meet regulatory requirements and avoid fines

Addressing data issues by standardizing processes

Top 6 Common Data Quality Issues

Globally enterprises are more data-driven and focused on unlocking the power of data to make right decisions. Impactful decisions are made when the data is accurate. Quality of the data helps to timely react to the new market opportunities and it immensely contributes to the growth of organization. Data Quality refers to the state of a quantitative or qualitative piece of information.

Quality and reliable data helps you stay ahead of the competition, and your business to be agile. It helps you to improve customer relations, data consistency and effective marketing. Data Quality and its effective management helps you to gain a competitive edge, enables and maintains compliance, improves scalability & consistency.

Let us now understand the top 6 common data quality issues:

Duplicate Data: This data quality issue arises when the same data is entered more than once in different ways. Duplicate data is created in the process of extracting  data from the multiple siloed systems and consolidated together in a data warehouse, creating copies of the existing and the same record. Duplication produces incorrect insights when undetected.

Incomplete Information: This data quality issue happens when critical pieces of information are missing due to ETL processes failure to input at the source systems. Fields which are not filled or left blank are the major constraints for the enterprises when using the data for any critical purpose.

Data Inconsistency: Data inconsistency issue arises when storing data in the same field that is either in different units and in different languages. When information such as address and names are duplicated that leads to a compromise in data integrity. Data Inconsistency is caused by redundancy as somebody may change the value in one file and not change in another file.

Inaccurate Data: This is one of the biggest data quality issues that occurs when every value is complete and format is correct but there are mis-spellings  or inaccurate data. Example of data quality related issue–  It is difficult to conduct forecast analysis when sales data entered into your system is incorrect.

Invalid Data: On simple logic or rules, data invalidity scenario arises when your data cant be exact and precise. Invalid data are the values which are generated incorrectly and can be challenging to identify visually but can be explored in analysis and have to be removed from the data set.

Data Imprecision: Lack of precision or data imprecision is when the data has been stored at an outline which is due to ETL process which does not help users to get to the detail which is needed for analysis. Lack of coordination and teamwork between different departments is one of the reasons for Data Imprecision.

Why Is Data Analytics Used in the Retail Industry ?

Data analytics is all about the science of analyzing raw data to make valuable insights. Many of the processes and technicalities are automated into mechanical algorithms that work on raw data for our understanding. Globally, data analytics is applied in all stages of the retail process, by the big retail players. It is used to help enterprises to improve on profit maximisation and discover new sales opportunities. Retailers are considering  leveraging the power of data analytics in this constantly changing world by focusing on new innovative ways.

Let us now understand what are the top 5 ways data analytics can be used in retail

Analysing Customer Behaviour – Data analytics helps to track customer behaviour which help retailers understand what their customers like and how and in what way they are shopping and where they like to receive promotional offers. Using this type of data, retailers optimize their marketing efforts and create offers that suit the customers in the form of personalization and retention that ultimately improves sales and lowers the operational costs.

In-Store Tracking – In regards to brick and mortar and omni-channel retailers, a focus to improve in-store experience is vital. Tracking helps to improve merchandising by using data analytics. To do so, mobile applications, beacons and sensors are used to study the movements of the customer throughout the store. After data is collected it is analyzed for insights in regard to how to focus on customer attention, deliver customer experiences and what makes the consumers come back to the store again and again.

Superior Promotions – In the retail industry, improving conversion rates is vital as online e-commerce continues to be skyrocketing. With targeted promotions and predictive analytics retailers can uncover what type of products and offers are popular. With this strategy of experiment and analyze new ways to capitalize on the customers interest can be explored further

Focusing on Customer Journey – Focusing on customer journey and overall analysis of their entire experience with any type of brand is important. Right from communications to in-store and online store experience, purchases and its related interactions with respective ads, achieving a full view of this enables to increase the chances of closing a sale and reduce the risks of cart abandonment. Furthermore, retailers can understand the best and innovative way to guide customers to complete the buying process.

Successful Data Governance Implementation with the Right Data Insights Platform

Data is the most happening asset that impacts the business operations in any enterprise. Today data is regarded as one of the critical assets and there are a plethora of strategies available to extract the value from these assets to gain a competitive advantage. Insights that are derived from the data are used to solve complex business problems. It is vital to understand where the data came from, how old the data is, the quality of the data, where to find and how best to use it, and how far you can trust the data. You can answer all these types of questions once governance policies and data owners state the usage and also the metrics to understand the data quality. In simple terms, it is all about the orchestration of technology, processes, and people that enables an enterprise to initiate data as an enterprise asset.

The data must be scored, cataloged, and defined so the users can view them as assets, understand what they are all about and make the best use of them.

There are a plethora of benefits of data governance plan risk mitigation, greater consistency quality of data, time efficiency. Some enterprises have a data governance framework that, the data governance process which is critical for success. Some of the internal departments or a concerned set of individuals are responsible and others that have cross-disciplinary teams are responsible for governance.

There is a difference between data ownership and data governance. The data ownership is performed by a team that focuses on the secure storage and integrity of the data. Data Governance is more concerned with how data is shared, maintained, used, and regulated across business processes and functions.

Enterprises are transforming by understanding organizational data by the business users among different departments together with collaborative data and leverage to derive actionable insights. It is vital for the different departments to successfully collaborate on the efforts of data management. The responsibility for preparing and managing data for everyone across an enterprise default to the IT resources. This type of scenario leads to confusion among the businesses that are often left confused when the IT team uses technical languages to describe the data assets in which they attempt to translate high technical vocabulary into business terms.

The data can be treated as valuable as an asset, as the business users can understand the information and transform it into insights. By implementing a detailed enterprise data governance model, it is vital for fostering collaboration between Information Technology and business.

Collaboration starts when we initiate providing clear definitions and business terms in the enterprise through data catalogs, data dictionaries, and business glossaries. Data usage, data lineage, and data source can be clearly defined.

By providing easy-to-use interfaces, simple navigation, effective visualizations, and also collaborative communication between different business departments and IT. The challenge of reliability, flexibility, speed, and the most important is the control.

Experience the collaboration between executives, technical users, and business users with the help of the Amurta Data Insights platform which is an exclusive Data Governance Tool designed for medium to large enterprises with complex data pipelines. Now simplifying the enterprise data governance is made simple.

Let us now understand the 5 steps for implementation of Data Governance

Step 1: Assessment – By understanding who has created, approved, and who is using the data, the purpose, relevance of whom they are using, and who owns the processes? By answering all these questions you can collaborate all the existing reports in a better way. By identifying any scope for improvements that need to be made will enable us to create more effective strategies.

Step 2: Identify Challenges and Risks – Identify the issues and challenges associated with the current sources of data and share them across different departments. Spot the gaps to protect your data regarding compliance and security and make use of the right people, technology, and processes.

Step 3: Data Controls – To optimize your data quality and integrity it is vital to establish relevant metrics, control and develop the report processes. Establishing mechanisms will help to identify, prioritize and resolve the data-related issues. It is important to make sure that the right experts are involved in importing, documenting, and implementing the appropriate data controls.

Step 4. Develop a Data Governance Strategy – After placing the right people effectively and having assessed your current situation, it’s time to create rules around your data and decide regarding the updating technology to make better integration and road map. Data should have clear standards such as how and where it should be stored, managed, enhanced, and leveraged. By employing a data steward and a team that helps in processes and leverage the right Data Insights Platform for your organization.

Data Governance Policy consists of a document with a set of guidelines to ensure that an enterprise information and data assets are managed consistently. The Data Governance Policy contains the practices, principles and standards that the leaders of that particular Enterprise have determined to ensure that they protect and have high quality data.

The policy document clearly defines the structure of the Data Governance for the team of the enterprise that will help in their daily operations. One of the main purposes of the Data Governance Policy is that the management and data processing should ensure the enterprise data is consistent, accessible, accurate and protected.

Why do we need Data Governance Policy?

An Ideal Data Governance Policy includes building a process model for managing the enterprise data. For any successful Data Governance in any enterprise both IT and business departments should collaborate with equal responsibility and create value from the Data Assets and processes. Data Governance Policy brings benefits to an enterprise by

Defining and assessment

Strategizing and Planning

Designing & Implementation

Monitoring.

Data Governance Policy aims and  addresses the structure related to Data Governance, and also includes policies on Data integrity, Data access, Data usage and Data integration.

Why is Data Governance Policy Important ?

Data Governance Policies are guidelines that help your data and information assets be used  efficiently and properly. The guidelines include policies related to security, privacy, quality and access. Data Governance Policy also includes how data will be controlled and managed. Lets focus on common areas covered by Data Governance Policy

Data Availability – For a Successful Data Governance Policy, one has to ensure that Data is available and easy to consume by the Enterprises.

Data Quality – Data is consistent, correct which will not create any problem while usage and analysis.

Data Usability – Data is documented, structured, labeled which also helps in easy retrieval and search and it is very useful with the tools.

Data Integrity – One has to ensure Data should retain its innate quality even if it is a converted, transferred, stored and viewed across different platforms.

Data Security -To Define standard processes and prevent any data leakage and loss.

Data Governance policies are guidelines that help your data and assets be used efficiently and properly. The guidelines include policies related to security, privacy, quality and access.  Just like any other assets, Data is the utmost important asset in the era of Information Technology and it requires consistent and strong management. To avoid data quality issues such as conflicting information, lower quality data etc Data Governance Policy has to be implemented.

Quick Benefits of a Successful Data Governance Policy.

Collaboration

Appropriate usage of the Information

Helps to meet Regulatory Compliance

Sound Decision Making