In our age of data-driven decision making, the new GDPR laws have once again brought the criticality of data governance to the forefront. Believed to be one of the most extensive revisions to the European data protection and privacy legislation, GDPR and its associated changes have presented businesses with the unique opportunity to organize their […]
In our age of data-driven decision making, the new GDPR laws have once again brought the criticality of data governance to the forefront. Believed to be one of the most extensive revisions to the European data protection and privacy legislation, GDPR and its associated changes have presented businesses with the unique opportunity to organize their data houses.
So, executives should consult with experts familiar with GDPR on its impact on their operations. Businesses need to get used to the idea of handing over control of the data they share with people; only then can they achieve GDPR compliance and establish a better rapport with customers. But how does data governance figure into all this? Find out below:
Shortcomings in Traditional Data Governance
There’s nothing wrong with traditional data governance; in fact, it offers a rigorous and strategic framework for designing outline roles, data standards, and responsibilities, along with procedures and policies for data management throughout the organization. What’s more, without traditional data governance, businesses wouldn’t have been able to increase their efficiency and productivity in the use of core business data resources in data and transactional warehousing environments.
The focus of these methods was on data quality, trust, and protection, and they were great for recognized data sources that had known value. However, the modern industry is full of unstructured or unknown data sources like IoT and big data, and traditional data governance just can’t keep up. With the added features of machine learning and artificial intelligence, the shortcomings of the conventional approach are becoming obvious.
Owing to their rigid structure, conventional data governance procedures and policies hinder the possibilities formed by advanced analytics and data technologies by forcing them to fit the age-old mould for legacy infrastructure and data platforms.
Impact of Emerging Technologies
IoT provides thousands of unrelated data sources a chance to connect on the same platform. IoT gadgets are more than just data source; they are data generators and gatherers. Sensors, wearable devices, and other modern computing technology can accumulate data by the millisecond and stream the same data into a cloud of possible consumers.
Artificial intelligence and machine learning systems analyze the data in real-time to identify relationships and patterns, gain knowledge, and plan a suitable course of action. While these are data-based autonomous actions rather than explicit instruction or programming, they possess the power to find gaps or extra data requirements and send requests back to the IoT gadgets for collecting or generating fresh data.
Traditional data governance makes the onboarding of IoT devices very difficult because of conventional authorization and validation needs. To foster machine learning and artificial intelligence in these initial stages, the data lifecycle must rely on non-conformity with predefined standards and rules. So, governance must allow new data to be incorporated quickly and efficiently, and offer mechanisms to mitigate dangers, maximize value, and encourage exploration.
AI and IoT under the New Data Governance Methods
Concepts like IoT and AI aren’t new but they are still highly competitive markets for businesses. While the two undergo expansion, they tend to hypercharge the growing volume of data, especially unstructured data, to unexpected levels. As a result, the volume, velocity, and variety of data increase in unison. And as the volume rises, so does the speed and velocity at which data need to be processed. In such cases, the types of unstructured data increases as well. To manage all this, businesses have to implement the necessary data governance.
Storage and Retention
Big data has increased the variety and volume of data considerably, which means more data storage is a necessity. Data storage and data integration and provisioning are used interchangeably, but they are very distinct. Governance must address them separately and appropriately. While storage normally means the way data is physically retained by the organization, in conventional data management methods, the data storage technology impacts the storage requirements like size and structural limitations. Along with retention practices and budget limitations, often dependent on compliance, these needs restrict the amount of data stored by the business at a certain time.
Security and Privacy
Security and privacy are the major areas of focus for conventional data governance. But new technologies expand the scope of what needs to be secured and protected, emphasizing the need for additional protection. Even though “privacy” and “security” are thought to be one and the same, they are not.
Security strategies safeguard the integrity, confidentiality, and availability of data created, acquired, and maintained by the company. Security exclusively means protecting data, while privacy is more about protecting entities, like individuals and businesses. Privacy programs make certain that the interests and rights of an individual to control, use, and access their private details are protected and upheld. However, without a successful security strategy, a privacy program is unable to exist. Privacy needs often inform policies in large-scale security operations, but the program itself influences the processes and technology need to implement the necessary controls and protection.
As far as IoT is concerned, security is one of the most crucial aspects. The regular addition of systems and devices constantly leads to new vulnerabilities. Even though business comes first, protection is possible only if they protect and secure the network along with every touch point where data travels. Thanks to IoT, data security isn’t just about permissions and access on a given system. Data protection now incorporates network segmentation, data encryption, data masking, device-to-device authentication, cybersecurity monitoring, and network segmentation. That’s a whole lot more than what traditional governance programs envision.
Escalated Digital Transformation
The changes in digital transformation will be far-reaching. In fact, the new data governance measures will accelerate the process, thereby rewarding organizations that commit to more than just compliance with data governance. Moreover, a stronger foundation in the field of data governance will provide organizations with various benefits, such as increased operational efficiency, decision-making, improved data understanding, greater revenue, and better data quality.
Data-driven businesses have long enjoyed these advantages, using them to dominate and disrupt their respective industries. But it’s not just meant for large businesses. The moment is right, for your company to de-silo data governance and treat like a strategic operation.
Data governance is changing, and you need to work hard to keep up or get left behind in the industry. However, you can follow the tips given below for the best health and ensure your company is prepared for GDPR.
Author - Rahul Sharma
