Treating data as a product a method to grow analytics use

By neub9
4 Min Read

Empowering Employees with Data as a Product

Many enterprises are searching for innovative ways to expand access to analytics tools for a wider group of employees. One approach that is gaining traction involves treating data as a product rather than just information. Studies conducted over the last two decades have found that roughly a quarter of potential users within organizations are utilizing data analytics tools. These tools are often complex and require coding knowledge or extensive data literacy training. While some vendors have developed capabilities to address these challenges, such as natural language processing (NLP) and low-code/no-code features, organizations continue to face adoption roadblocks. According to a NewVantage Partners survey, only 24% of Fortune 500 companies consider themselves to be data-driven, with just 21% reporting the development of a data culture within their organization.

However, with recent advancements in technology, particularly the rise of generative AI large language models (LLMs), analytics tools are becoming simpler to use, potentially enabling organizations to expand their data-informed decision-making processes. Larger vocabularies and intent understanding have made true conversational interactions possible, reducing the need for extensive coding skills. Additionally, embedded analytics, delivering data within normal workflows, is also growing in popularity to facilitate easier adoption of analytics tools. To maximize analytics usage, both a philosophical shift and technological advancements are necessary. Data, as well as data assets like dashboards, reports, and models, need to be treated differently – making it easier to find and demonstrating its usefulness to potential consumers. In essence, data should be treated as a product rather than mere information, and technology should align with this philosophical ideal.

Treating Data as a Product

Even though data assets are often referred to as “data products,” this notion doesn’t necessarily equate to treating data as a product. Data assets are usually isolated within organizations, with different departments using different tools, leading to disconnected data systems. This disconnect can result in data duplication and a lack of data lineage, leading to data quality problems. To address this disconnect, a switch in focus is needed, treating data assets as retailers treat their products – making them appealing and easy to find. Data assets should be easily discoverable and usable, akin to the way medication is made available on a pharmacy shelf. To achieve this, enterprises should focus on packaging and storing data assets for maximum usability and reusability.

Furthermore, technology should be deployed strategically to enable this shift. Organizations should borrow successful strategies developed by e-commerce vendors, such as personalized recommendations and real-time delivery. Data catalogs are a valuable tool that can make data assets easy to find, encourage collaboration, build confidence, and deliver recommendations. These catalogs provide an inventory of an organization’s data products and enable administrators to govern data access layers, ensuring the proper use of data while enhancing collaboration and trust.

By adopting a data as a product approach and leveraging appropriate technology, organizations can create an environment where data is easily accessible, useful, and reusable, ultimately empowering employees to maximize the potential of their data assets.

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