Data Virtualization Drives Successful Data Governance
Without a holistic view of an organization’s data, most data management and governance practices end up being ineffective.
Every day, we create over 2.5 quintillion bytes of data. Data is so important to business and the economy that in 2019, the Singapore Government added ‘digital defense’ as the sixth pillar of national defense together with military, civil, economic, social, psychological.
Aside from the rise in cyberthreat incidents, the proliferation and adoption of newer data-driven technologies like artificial intelligence (AI) and Internet of Things (IoT) in the enterprise is also making it necessary for organizations to make sure that they are managing their data well. Increasingly, enterprises have to ensure that they have the right protocols in place over access to their data, that they are observing regulatory compliance and also mitigating data privacy risks.
In fact, it is now necessary for enterprises to incorporate data governance into organizational planning from the very start. Effective data governance, as opposed to simple data management, maintains the proper balance between protecting data and making it accessible to the people who need it – by defining who can take what actions, upon what data, in which situations, and using which methods.
Here we will explain how data virtualization can play an integral role in ensuring effective data governance.
Enterprise Data Governance
Data governance requires buy-in from all levels of the organization. Modern data governance initiatives move beyond the evaluation of data assets and requirements at a departmental level. IT departments cannot operate a successful, holistic data-optimization project without the full approval and engagement of the board. After that, the plan needs to be reviewed by departmental directors and managers.
Each step needs to be mapped out, and the necessary resources in each individual department need to be determined. Project planners need to assess which data is of the most value, which is most critical, and how it will tie in with data from other departments as part of a holistic enterprise data-optimization program.
A thorough plan maps out the organization’s current infrastructure and specifies the departments that would be affected. It also thoroughly examines what data may be needed, why it might be required, and where that data would come from. However, individual departments may have their own sets of guidelines and rules around the use of data, and these guidelines could have been drawn up over time. All these factors can diminish the effectiveness of an enterprise-wide data governance solution.
A Complete View of All Data from All Angles
There is a huge influx of structured and unstructured data in the modern enterprise — from customer information to sales records, from social media interactions to marketing reports, and so on. Advanced, digitally mature organizations use that data to enhance their business operations and gain deep insights into many fundamental operations, while most organizations aspire to this – but they have barely started on their journeys.
What differentiates the former from the latter? A holistic view of all data in the enterprise. To operate effectively, a modern organization needs intelligence about the different ways data connects – who owns it, the lineage of the data, its relationship to other data points, and so forth. It is crucial that organizations gain a complete, 360-degree view of the data’s history, such as the relationships between it and other data points, and transactions between that data point and other systems, to provide the proper governance of data.
Data can inform an organization about its history with a particular customer, the customer’s purchase history, whether the customer has been considering other lines of products, and other information. That data may also enable the company to “connect the dots” and determine the optimal place to display advertising to the customer across multiple channels.
However, making these connections requires that the metadata layer – data about the data itself – be actionable within the data governance solution. A master data management (MDM) platform, working in conjunction with a data virtualization platform, can offer a complete view of the data and its lineage. This enables the organization to establish links between departments and disparate data sources. It also enables the organization to easily sort out all data and present it, in real-time, across departments, systems, and geographic boundaries. Data virtualization uses data-access metadata to provide seamless, real-time access to data sources. By centrally storing this same metadata, data virtualization enables organizations not only to determine the data lineage of every data set but also to implement data governance protocols from a single point of control across the entire organization.
Strengthening Enterprise Data Governance
The adage “you are what you eat” can also be applied to an organization and its data. If your company ingests quality data that has been cleaned and maximized for the tasks it is required to perform, the result will be a high-quality data analysis and useful business intelligence. To derive real business value from data, organizations need a view into all relationships across multiple systems. Only then can organizations turn their data into information and gain enhanced efficiency and business opportunities.
The most effective solution, in conjunction with a strong data governance plan, is to implement a data virtualization platform at the active metadata layer. This enables organizations to centrally sort out all data and present it, in real-time, across departments, systems, and geographical boundaries.
A data governance plan that is bolstered by data virtualization can properly organize an organization’s data and present the most current and valid information to users, regardless of their department or specific requirements.
Ed. Ravi Shankar is a Senior Vice President at Denodo.