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Rajaneesh Kini Written by Rajaneesh Kini, SVP & Chief Technology Officer
on 04 May 2022

Tons have been written about the importance of data. Aggregating content across the Internet that references the importance of big data would run into a couple of petabytes by itself. However, understanding the importance of getting started on your big data journey is not the challenge. The real challenge lies in successfully operationalizing it and deriving the right insights that allow your organization to make critical business decisions. Todd Davis, Data Technical Consultant, and I recently caught up on a LinkedIn Live to discuss this and more. Here are some takeaways from that conversation.

The data market is poised to top over $100 billion by 2027. This isn’t a surprise, considering the scale and volume of investments that are being made today. Enterprises are often told the success stories of data being leveraged but are generally shielded from the larger picture, i.e., how difficult the journey was.

To simplify this process for you, we’ve narrowed down on the four things organizations need to bear in mind during their data journey.

  1. Intelligence powered by data wisdom: The role of data is to provide intelligent insights. The smorgasbord of information available today can be uniquely transformed to wealth only when it can deliver insights that aid business decisions. The growing prevalence of data mesh has made it possible for organizations to examine their data in a new light with better actionability. The four pillars of any data mesh, 1) domain-oriented de-centralized data ownership and architecture, 2) data as a product, 3) self-serve data infrastructure as a platform, and 4) federated computational governance, will help enterprises go beyond raw data to a ready-made product that can be used at the right time. Within data lies the tremendous potential to deliver transformation at scale. However, today data collected or available is so much that you don’t know how to use it. Real value is derived from it, when the volume, velocity and veracity of it deliver relevant insights at the right moment.
  2. Data governance is not a need rather a necessity: Defining the scope of who has access to data and what level of authority they have, to make decisions and derive actionable insights is paramount in today’s ecosystem. However, governance is more than just a combination of systems and processes. At its roots, data governance begins by ensuring data is providing value to customers and is comprehensive to all their needs. Insights are only valuable when data quality is appropriate to the use case, making the recency and relevance of utmost importance. And from a security perspective, adhering to data laws such as GDPR and CCPA, set the grounding to ensure data is protected at both organizational and customer levels.
  3. AI and automation are using data to move higher up the value chain: Visibility and transparency in data will help make organizations more effective. However, as organizations dive deeper into their data, AI, ML, and automation begin playing a critical role in managing insights and the success of their operationalization. AI and ML are propelling a new era of intelligence with autonomous decisions that are predictive and prescriptive. AI can also parse through volumes of unstructured data and text, delivering deep sentiment analysis. On the other hand, automation is accelerating businesses by creating scale and reducing human intervention. It plays a huge role in helping recover data or proactively mitigating attacks. The ability to predict failure and engage in proactive maintenance is critical to the future of how data will position itself. The explosion of data and IoT will catalyst this seismic shift.
  4. An investment in data also requires a parallel investment in people: The requirement for specialized roles in data has seen a rapid spurt in the last few years. There has been a 650% growth in data science jobs since 2012. An increase in the pool of talent available also means organizations need to invest in their newly hired talent. This begins at a university level, where talent is nurtured and provided with the tools that enable them to rise in the enterprise world rapidly. Additionally, enterprises need to invest in tech advisory boards that guide and mentor new talent. This provides them with a learning ground to harvest new skills and an incubation center to test new ideas.

If you’d like to know more about the power of data and successful data strategy for organizations, you can hear Rajaneesh Kini, SVP and CTO, Cyient and Todd Davis, Data Technical Consultant. They dive into the challenges, considerations, and key success metrics of data strategy during their LinkedIn Live discussion. Watch the whole video here: https://www.linkedin.com/video/live/urn:li:ugcPost:6892496421623255040/

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