Getting Your Data Management Priorities Right in 2022

As organizations brace for the next wave in the data boom, governance policies need urgent reassessment. During the pandemic, the world generated more data than ever before, both in structured and unstructured formats. With so many processes moving to the cloud, data exposure in transit is exceptionally high. In 2021, 4 in 10 servers suffered at least one outage, interrupting business processes and risking data integrity. That is why it is vital to get your priorities in order before we begin the next phase of digital consolidation.

Understanding the Discrete Tenets of Data Governance

When we talk about data management priorities, we are discussing seven things:

1. Collection — Governance begins at the data collection stage. Organizations must connect to different data sources, extract information as per a framework, and centralize the data repository to build a sole source of truth. Increasingly, data is also collected and processed at the edge.
2. Storage — Organizations must choose the most suitable data storage strategy for them. Hybrid or multi-cloud environments are the most common, providing flexibility and risk fragmentation. In that case, an enterprise data platform is critical to maintaining visibility.
3. Availability — Maximum availability and uptime ensure that data is authorized to the correct use by being mobilized/monetized as envisioned. Availability depends on many things, from the strength of the underlying infrastructure to failover mechanisms.
4. Access — Managed data access is central for both security and business continuity. Therefore, varied data tiers must have associated access privileges, mediating how machines and human users fetch information hosted in the enterprise environment.
5. Security — Data loss prevention, data retention, and endpoint protection are a minimum of the policies you need to govern the security aspect of data. Data security and compliance go hand in hand, which means that organizations need frameworks and guidelines for GDPR, CCPA, Indian data protection laws, etc.
6. Master Data Management — The MDM tool offers the capacity to secure data by creating regulations and policies. MDM carries features which include using user passwords to keep vital confidential information safe and away from third-party intrusion. In addition, it assists in protecting sensitive customer and staff data by using encrypted data to generate security-based rules. MDM solution needs to implementation in conjunction with security as it plays an essential role in access rights. To properly coordinate security measures when implementing MDMs, add a data program that regulates data production, maintenance, storage, and destruction, besides how changes are approved and audited.

7. Fragmented Data — Data fragmentation indirectly distorts the knowledge of an enterprise. Especially companies where we have multiple systems to manage their customers, products, or their ecosystem end up having a proliferation of data from various sources, fragmented and unstructured data multiplies. It causes companies to take slower decisions, and more money is spent on managing sensitive data, limiting their vision. Moreover, it can sometimes escape data governance and security strategies, increasing exposure to data breaches. However, data fragmentation can be avoided. Part of the solution is to adopt SaaS platforms around pivots like the enterprise-wide customer data, wherein platforms like Gainsight or SmartKarrot can be beneficial.

A Multi-Disciplinary Approach

In 2022, data management must be a multi-disciplinary function, not limited to IT alone. Today, every department, business function, team, and process generates enormous data volumes, which feed back into the business in a closed-loop model to generate value. Therefore, governance and data management must be multi-disciplinary capabilities — co-owned by tech, legal, HR, and the executive team. Increasingly, organizations are designating a Chief Data Officer alongside a Chief Technology Officer to underscore the importance of the function and maintain transparency.

Some of the best practices to follow would include:

● Mitigate data exposure risk in the supply chain through third-party risk assessments and DevSecOps.

● Conduct regular vulnerability testing to prevent exploits like Log4Shell.

● Regularly revisit your data retention rules in line with current law and changes in consent.

● Avoid creating a “data swamp” that is comprehensive but unusable due to low quality.

● Invest in metadata management to further mine the value of information and improve data retrieval efficacy.

● Build a strong data culture and strong data leadership as companies with these qualities are likely to succeed in digital transformation.

Organizations must create a cohesive data mesh that weaves together the disparate information buckets in an enterprise.

To know more, drop a line at Arvind@AM-PMAssociates.com.