According to a World Economic Forum (WEF) estimate, around 463 exabytes of data enough to need 200 million DVDs per day – is being created worldwide every day.
What is Data Governance? A McKinsey study points to 15 to 25 percent growth and increase in EBITDA in companies that use data-driven B2B sales-growth engines.
While this is good news, what is of greater importance is to know that data collection is only Step #1. Managing and organizing data is essential to reap the expected benefits, and the larger the volume of data, the greater the need for data organization and management.
And, there is more. Data management and organization are just the tip of the iceberg. Businesses need to set in place policies and processes around making the data available and usable while ensuring its integrity and security. This is where data governance plays a crucial role. It ensures data consistency and trustworthiness while protecting data from being breached and misused. They need data governance.
Data Governance refers to the corporate view on a variety of aspects related to data engineering services, including:
● The collection processes
● Roles and responsibilities of the employees accessing the data
● Policies and standards
● Metrics to measure data usage and ensure its effectiveness and efficiency in achieving goals
A data governance program is designed by bringing together a steering committee consisting of the executives, IT,, and data management teams. They create the policies and standards that govern data collection and usage processes, while data stewards implement and enforce the procedures.
Data governance is mandatory and guided by regulatory requirements to protect data privacy and security. However, having an effective and efficiency data governance practice is a complex process. It requires a common understanding of key data entities among the different stakeholders, requiring common data definitions and formats.
Second and key challenge is in determining the business value, which can make it difficult to get the required approvals, budgetary allocation, and support of the key stakeholders.
To demonstrate the business value of data governance on an ongoing basis, quantifiable metrics need to be identified, established, and communicated to the rest of the organization.
One of the advantages of cloud-based data access and modern technologies is that data is now available to the business users for self-service BI and analytics. This has added to the complexity of data governance challenges of ensuring data is not misused or breach data privacy and security requirements. Streaming data in real-time analytics is another compilation where accuracy, privacy, and security are not impacted.
Big data analytics solutions with the amalgamation of structured, semi-structured, and unstructured data adds another layer of complexity to governance, which traditionally dealt only with structured data.
Siloed data, lack of resources, poor quality of data are among some of the other challenges that make data governance complex.
To ensure effective governance and overcome the challenges, businesses need a structured approach. This includes:
1. Identifying Distinct Use Cases: Understand the benefits, costs, and risks of governance to be able to make a business case and allocate the necessary resources. By identifying the use cases, engaging the stakeholders becomes easier and ensures a more comprehensive data governance framework that addresses meaningful issues.
2. Quantifying Value: Assign a quantifiable value to the key performance indicators and monitor the KPIs helps to:
– Assess the effectiveness of data governance framework
– Strengthen the need to align processes with the frameworks where it proves to be effective
– Identify areas of improvement to improve it further
3. Improve Scalable Data Capabilities: Clearly outline the capabilities users require to improve the value and usage of data based on their specific needs. Empower the users with the required technology and processes such as intuitive and searchable catalog that helps discover data assets, enhanced data security, accurate date, and understand data origin, classification, content, and use to:
● Collaborate across the functions for sharing data assets
● Improve internal and external regulatory compliance
You might be interested in: Why Data Fabric is the key to next-gen Data Management
An effective data governance strategy provides many benefits to an organization, including:
● Data Consistency: Provides a consistent view of data with common terminology to different business units while enabling flexibles use based on need
● Data Quality: Provides access to accurate, complete, and consistent data
● Data Mapping: Makes it easy to location all data associated with key entities for faster access.
● Single Version of Truth: Often data siloes fragment the view of data, impacting business outcomes. Data governance enables unifying data for providing a holistic view of business operations to improve decision-making.
● Improved Compliance: Meet the requirements of regulations and standards such as the US HIPAA (Health Insurance Portability and Accountability Act), the EU General Data Protection Regulation (GDPR), and industry standards such as PCI DSS (Payment Card Industry Data Security Standards).
Indium Software is a digital engineering expert with specialized expertise in data science services, data engineering services, and data lifecycle management services. We help businesses establish a data governance framework by understanding their business needs, identifying their data sources, and creating a centralized data repository to improve data management, organization, and stewardship while ensuring compliance, privacy, and security.
By Ankit Kumar Ojha
By Uma Raj
Indium Software is a leading digital engineering company that provides Application Engineering, Cloud Engineering, Data and Analytics, DevOps, Digital Assurance, and Gaming services. We assist companies in their digital transformation journey at every stage of digital adoption, allowing them to become market leaders.