Industry executives and experts share their predictions for 2022. Read them in this 14th annual VMblog.com series exclusive.
Data Management, Privacy Laws and AI
By Krishna Tammana, CTO, Talend
Enterprises today have an ever-growing amount of data, especially unstructured data, which amplifies the need for data management solutions and other modern technologies such as AI and ML. When looking at the data management landscape in 2022, organizations that want to get the most out of their data must adopt a coherent data management strategy with a focus on having healthy data to help achieve meaningful business outcomes. Here are my predictions for where the data industry is headed next year, and the technologies that will be most important to enterprise success:
Data management will shift organizations’ focus from the mechanics of moving and storing information to focusing on business outcomes. In the quest to drive business outcomes by being data-driven, businesses will realize the need to go beyond the mechanics of moving data and expect their teams and vendors to drive data health to enable data driven business decisions with confidence. As intelligent automation continues to transform the way businesses operate, organizations are starting to realize that AI/ML is only as good as the data they feed into it. Suppose businesses can ensure healthy data at scale and at the speed of business. In that case, they will be able to truly unlock the power of data analytics and deliver successful business outcomes.
As new data privacy laws continue to emerge and increase compliance complexity, data governance will take a central role in strategic data management models. Using the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) as a model, governments globally and state-by-state in the US will continue to create new regulations to give consumers rights over the data companies collect about them and how they use it. These disparate regulations will increase complexity for organizations trying to comply.
Governance will also continue to increase in complexity as data is democratized by more accessible, lower-cost, self-service data solutions, like cloud data warehouse options that continue to become available to line of business owners (e.g., sales, marketing, product, finance, etc.) and citizen users. As more individuals have access to increasing volumes and sources of data alongside the ability to create their own data lakes and warehouses, data governance practices and teams will be challenged in new ways. Centralizing data governance provides a foundation to manage policies and serves as a glue to keep distributed teams strategically aligned to maximize outcomes that benefit their line of business.
Human involvement becomes increasingly critical as AI drives automation in data management. As data sources, volumes, users, and destinations increase faster than data teams can scale, AI will serve an important role in helping data teams manage the volume with fewer resources. However, we must strike a careful balance between the benefits and costs of automation. Automation is good and even necessary for solving many problems. However, important decisions, especially decisions related to ethics or personal beliefs, require human intervention to eliminate bias. We as humans have biases and machines carry that forward. We have to do everything to ‘check it twice’. Enriching data with context and commentary will help other users in an organization understand what data they can trust and how to use it best to achieve actionable insights.
The new year provides organizations with an opportunity to prioritize the quality and health of their data to ensure confident decision making and fewer biases. Evolving regulations and technologies can create new data challenges, but enterprises can set themselves up for success to drive key business outcomes and see an ROI on their data investments.
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ABOUT THE AUTHOR
Krishna Tammana is the Chief Technology Officer for Talend, a data integration and integrity company. He is responsible for scaling the product and engineering organizations to drive innovation and Talend’s continued market growth. Previously, Krishna spent nearly a decade as VP of Engineering at Splunk where he led global engineering teams and cloud operations during the company’s successful portfolio expansion and transition to the cloud. Prior to Splunk, Krishna held engineering management roles at Dun & Bradstreet, YouSendIt, RIGHT90 INC. and E*Trade.
