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Diving Into Enterprise Data Strategy With Samsung Research’s Prashanth Rajendran

On a recent earnings call, Microsoft CEO Satya Nadella observed: “Every AI app starts with data and having a comprehensive data and analytics platform is more important than ever.” While much has changed over the past year with the emergence of generative AI, data quality is still a fundamental building block of any enterprise – critical not only to modern product development and customer experience management but also use cases like LLM retrieval augmented generation.

Recently, I caught up with Prashanth Rajendran – a Data Product Leader at Samsung Research – on his experience crafting data strategies for a range of organizations, from industry giants like Samsung to fast-growing startups like Twilio and Cirrus Insight. We explored some high-level ways businesses can think about data strategy and leverage data in a way that fosters growth and success.

How do you think about data strategy at a high level? 

At its core, a data strategy is a foundational plan that defines how an organization will manage its data assets to achieve its business objectives. This spans the people, processes, and technology to effectively capture, store, process, and utilize data.

A good data strategy should be aligned with the overall business strategy and take into account the organization’s current data landscape, future data needs, and potential risks and challenges. When developing a data strategy, I have four key considerations for companies to keep in mind.

The first is defining the vision for data. What do you want to achieve with data? This could include improving decision-making, creating new data or AI-powered products or enhancing customer experiences.

Next is assessing current data assets and identifying gaps. Do you have the right data to achieve your vision? This includes considering the types of data you have. Are there any key data assets that you don’t have? This could include data on customer behavior, market trends, or operational performance.

Then you need to determine the right technology and tools. Do you have the right building blocks in place to collect, store, and analyze your data?

Finally, you need to build a data-driven culture. Do you have the right people and processes in place to leverage data effectively? This could include creating a culture of data-driven decision-making.

Overall, a well-developed data strategy can help organizations make better decisions, improve operational efficiency, and drive growth and innovation. By taking a comprehensive approach to data management, companies can unlock the full potential of their data assets and achieve their business objectives.

How can enterprises create a data moat?

There are several components to consider when it comes to getting the “right data” depending on your context.

The first is identifying the right data source. Not all data sources are created equal. Public data sources like the U.S. Census or economic data may be readily available, but they may not provide a competitive advantage for your app. Anyone can build the same AI app.

On the other hand, first-party data – proprietary or exclusive data to you, like how many people view a home listing from online aggregator sites like Zillow or Redfin – can provide a solid competitive advantage to your product.

To build a data moat, you can focus on first-party data, such as: Mobile/web app usage data, e-commerce transaction data, and Behavioral data like in-store visits and customer feedback.

Another way to build a data moat is through enrichment, which involves supplementing missing or incomplete data with external sources. For example, if you have data on existing customers, you can leverage third-party data sources to enrich the data with the “number of employees,” allowing you to track customers’ employee growth and potentially initiate an upsell conversation.

Another key consideration is prioritizing data sources. This involves considering factors like the cost of acquiring and processing the data, the potential impact on your business, and the effort required to clean and integrate the data.

However, it’s important to note that identifying and prioritizing the most valuable data sources is only the first step. Once you have identified these sources, there is a significant data engineering effort required to clean the data to your needs. This is why successful data leaders often say that 80% of their effort is spent on identifying, modeling, extracting, and processing the data, rather than building ML models.

By focusing on the right data sources and prioritizing your efforts, you can build a competitive advantage that sets your product apart from others in the market.

How are you seeing companies effectively measure the return on investment (ROI) of their data strategy?

There is no one-size-fits-all approach to data initiatives, and companies must tailor their strategies to their specific needs and goals. For example, if a company handles sensitive personal information like health records or location data, it must prioritize security aspects above all else.

My recommendation to data leadership teams:

  1. Run data teams as profit centers: Prioritize profit-generating items like new product development, lowering customer acquisition costs, or increasing conversion rates. These items can be quantifiably measured, and their value can be easily demonstrated to stakeholders.
  2. Build an incremental, iterative roadmap and validate hypotheses at every step: This approach allows companies to prove value using KPIs and analytics before moving on to more advanced machine learning (ML) and generative AI applications.
  3. Do not overspend on infrastructure: Avoid running data teams as pure infrastructure or technical teams, as this can lead to being seen as a cost center. Again follow an incremental approach.,

To effectively calculate the return on investment, companies must take a holistic approach that goes beyond mere financial metrics. Data initiatives require a cultural shift within an organization to utilize data effectively, and this shift must be supported by strong leadership and a clear vision.

By taking a thoughtful and strategic approach to their data initiatives, companies can effectively calculate the ROI of their investments and achieve their business goal.