Accurate data provides correct information for
proper decision making and implementing smart strategies for
growth of the organizations.
Today, data is changing the face of our World. In general, data may simply be another word for information; but in business and computing, it refers to information which is machine-readable. It is one of the critical resources in digital world and all the organizations depend on it on an ongoing basis for daily activity as well as for business growth. Maintaining quality data is a matter of great concern and necessary safeguards must be implemented to leverage it. Data Quality reflects mainly on three aspects like Completeness, Accuracy and Timeliness. Hence Data governance within an organization to use and control data assets is the need of the hour.
Sources of Data:
Data sources can be different for different organizations. The data can be internal data generated from millions of transactions along with employee data and customer data. Otherwise it may be data received from external sources like social media platforms, credit bureaus, external reports and publications and sometimes feedback from customers.
4 V’s of Data:
Volume: Normally the organizations use huge amount of data which is very voluminous.
Variety: Data can be unstructured, partly structured or structured with various sources. There could be multiple data inflows from internal and external sources.
Velocity: The speed and movement of data generation is called velocity. Data is continuously created, modified, deleted and moved. Business units need to capture data on real time basis to make quicker and efficient decisions.
Veracity: Organizations must ensure purity, reliability, integrity and accuracy of data before its use. It is necessary to deal carefully with unpredictable data.
Benefits of maintaining Quality Data:
Futuristic units must focus on data literacy and data culture at all levels. Organizations with pure and quality data have the following advantages:
Operational efficiency: Proper data can be used to customize specific products for the requisite group of customers without wasting unnecessary time. This will help in increasing customer satisfaction and thereby reducing customer complaints.
Business Leads: Accurate data helps in identifying one individual or unit as a potential buyer of a product or service. Business can get access to such leads through advertising, trade shows, direct mailing and other marketing effort.
Risk Management: Pure data helps in managing the risk of the units by knowing their customers and suppliers. The reports which were taking lot of time earlier can be prepared with accuracy in minutes. This saves employee time and organization money. By consistently tracking and monitoring costs, prices and other useful information, the financial health of the units can be improved.
Proper Decision Making: One must ensure that they are having right kind of data which provides relevant, accurate and complete information. Accurate data provides correct information for proper decision making and implementing smart strategies for growth of the organizations.
Compliance: Quality data is a must for error free Regulatory compliance. Organizations can be more efficient and effective in regulatory reporting processes by proper data risk management. This is helpful in heavily regulated financial services and the technology industry, where business models are built around consumer data.
Fraud Monitoring: Proper data can allow management to recognize signs of fraudulent activity. Employees become aware that they are being monitored which results in increasing their accountability and ethical behavior. Preventive measures can also be initiated by identifying and mitigating threats.
Data Analytics: Analytics is the application of mathematical and statistical techniques on data to make models that predict and enable fact-based decision making or planning within the organization. Or simply it derives insight from information and uses it for the benefit of the organization.
Whenever corporate data crosses a physical and logical boundary, the primary goal is to govern this data, while sharing data among business units internally or publishing data to customers, partners, auditors and regulatory bodies externally. Corporate Governance too demands stricter oversight for data usage, quality, privacy and security. Data governance monitors the People, Processes and technology required to properly handle data across the enterprise. Considering the importance and key role benefits arising, data governance gained a new dimension.
Objectives of data governance:
The goal of data governance is to establish the methods, set of responsibilities, and processes to standardize, integrate, protect, and store corporate data and minimize risks. The objectives of data governance can be better enumerated as follows:
- Establish internal rules for data use.
- Improve internal and external communication.
- Increase the value of data and facilitate its administration.
- Reduce costs
- Compliance with relevant legislations, regulatory requirements, policies & procedures and standards.
- Protection of the data through documented policies and procedures and ongoing communication, awareness and monitoring.
- Integrating data for greater accuracy, timeliness and quality of information to make decisions.
Key areas of Data Governance:
Data Owner: Business Units (BUs) are the data owner while IT remains the data custodian. Accountability for accuracy, integrity and availability of data lies with the data owner.
Data Classification: All information on data assets must be classified based on their business value and impact on business operations. It should be classified as Internal, Public, Confidential and Sensitive based on its importance to business operations.
Data Protection: There should be consistency in protecting data records from loss, unauthorized access and unauthorized sharing throughout the life cycle right from its origination to destruction.
Data Quality: Each data asset should be ‘fit for purpose’ by maintaining quality and timeliness. It should be ensured that data quality is maintained at the point of creation which can be referred as “First time Right”. Units should identify gaps in critical and sensitive data at each level to initiate remedial steps.
Data Sharing and Access: Data breach and data leakage are the greatest concerns for an organization posing serious threats like significant reputational damage including regulatory scrutiny and potential financial damages in the form of penalties, customer claims etc.
Data Leakage Prevention: During normal course of business lot of internal confidential data are generated which includes sensitive information of customers as well as employees which are private and confidential. Data leakage, either by breach of systems or unsecure mode of sharing, poses serious threat to the organizations.
Data governance framework: Data governance means proper approach to collecting, managing, securing, and storing data in an organization. Management of data can better be described as a wheel, with data governance as the hub from which the following 10 data management knowledge areas radiate:
- Data architecture: Data Governance architecture has a pivotal role in “enabling” oversight for key aspects of Corporate Governance. It is important to note that data governance procedures facilitate Corporate Governance in terms of management of data, but they are not the end point of implementation.
- Data modeling and design:Analysis, design, building, testing, and maintenance can be defined in this head.
- Data storage and operations: This is storage, deployment and management of structured physical data assets.
- Data security:Ensuring privacy, confidentiality, and appropriate access is primarily required for data security.
- Data integration:when two or more databases are required to merge for a common purpose for more than one department, integration of the data becomes necessary.
- Documents and content:Storing, protecting and indexing unstructured data sources and making this data available for integration with structured data.
- Reference and master data:shared data needs to be managed to reduce redundancy and better data quality through standardized definition and use of data values.
- Data warehousing and business intelligence (BI):This can be referred as managing analytical data processing and enabling access to decision support data for reporting and analysis.
- Metadata:Data management to ensure that information is integrated, accessed, shared, linked, analyzed and maintained to best effect across an organization.
- Data quality:Monitoring and maintaining data integrity with improving data quality by way of accuracy, consistency and completeness.
Disposal of Data: Data records and media containing data records should be disposed of securely when no longer required as per the laid down e-Waste policy and IS policy of the organizations. Secure disposal of data should be established in order to reduce the risk of confidential information leakage to unauthorized persons.
New data technology like artificial intelligence and cloud-based storage has been adopted by nearly every industry. While businesses continue on the path of digitalization and become more data driven, the concerns around privacy and ethical use of data have become top of mind. So being transparent over the company’s data assets like how they are acquired, stored, shared, and used is becoming a top priority. So thorough understanding of data related risks are critical in managing trust with data, both from within the organization and outside.