As data continues to grow at astounding rates, most start-up organizations don’t know what data they have, how much it’s costing them, and what risks this data may create for their business. Their data is often scattered in silos or stuck in departmental systems that don’t communicate well with one another, creating a distributed hodge-podge off less valuable or insightful data.
Throughout the creation of a company, busy startup founders and executives usually prioritize the urgent, tactical, day-to-day needs over their long-term strategic initiatives. Consequently, developing a proactive data strategy is usually put on the back burner. What they fail to understand is that developing a data strategy can transform their business, drive revenue, avoid costs, and increase efficiency. Some transformative benefits include (but are not limited to) being able to make data-driven/evidence-based business decisions, understanding your customers’ wants, staying on top of the latest trends relevant to your business, and reducing data risk that can result in massive fines by helping your business stay compliant with data protection regulations.
As a startup becomes more data-dependent, there are a few things they should know to properly store and protect their customer’s information. The first is knowing what data they have. A business needs to know what personal and sensitive data they are collecting, where they are storing that data, who has access to it, and whether it is secure and compliant. Companies often store so much data that managers can’t guarantee compliance and need time to audit their inventory. Conduct a deep data dive and learn what data has been replicated, copied, emailed, and distributed inside or outside the company.
Identifying and classifying data is essential as states pass their own regulatory frameworks protecting consumer data privacy. California’s California Privacy Rights Act (CPRA) being the strictest law so-far, Virginia and Nevada passed their own laws regulating what companies can and can’t do with consumer data. Without a single federal law setting a minimum regulatory standard for states to follow, CEOs and founders are expected to comply with each state’s own rules, which can lead to becoming a compliance and legal nightmare.
The next step is to reduce your data footprint. This will increase your business’s visibility of your data and reduce data risk. Knowing exactly what data and where it’s stored allows data managers to delete the trash data i.e. redundant, obsolete or trivial “ROT” data. With less overall data, companies have more control over access and limit the risk of breaches and leaks – reducing compliance headaches, however some of the biggest impact is with data consolidation comes the reduction of expensive primary data infrastructure and the cascading effect on operations including replication, backup, archive and archive that are dramatically downsized including time, complexity and most importantly all the software licenses, storage servers.
Afterwards, enable your data managers to be the ‘data masters’ with access to the data that can be copied, moved, deleted, and integrated with other applications. Data intelligence can provide managers with even more insight into their data usage. Most importantly, ensure the indexed data can be audited to remove trash data which is unnecessary, to enable data and data infrastructure consolidation for massive reductions in risk and cost.
Understanding the laws that govern data is essential for any well-run startup, especially when preparing to scale for growth. Without clear data management policies, startups are only setting themselves up for wasting precious budget, legal battles and heavy regulatory fines. Looking to the future, startups and other businesses should anticipate more regulatory oversight of the protection of consumer data if the federal government passes its own version. This can be viewed as good pressure for any organization to get their data act together. Today, they should improve their data management workflow to take inventory of what data they’ve acquired, create a plan to process DSAR’s, and reduce their overall data footprint to reduce the risks of a data breach. These steps will empower your data managers to avoid human error in classifying data, secure data on unsecured endpoints like PCs, and prepare the company for stronger data oversight.
Written by Gary Lyng.
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