Drive tangible business outcomes with data products

Data products are transforming how modern businesses operate, make decisions, and innovate. From predictive analytics that forecast market trends to dashboards that visualize performance metrics in real-time, data products make it possible for businesses to turn their raw data into actionable insights that everyone across the industry can use to drive business outcomes. And their impact is profound. Using data products, businesses can unlock hidden value in their data, drive greater efficiency, and open new opportunities for growth and innovation.

But you may be wondering, what are data products? And how can they help me achieve my business goals?

Data products defined

According to Gartner’s Hype Cycle for Data Management, 2023, a data product is a curated and self-contained combination of data, metadata, semantics, and templates. It includes access and implementation logic certified for tackling specific business scenarios and reuse. A data product must be consumption-ready (trusted by consumers), kept up to date (by engineering teams), and approved for use (governed). Data products enable various data and analytics (D&A) use cases, such as data sharing, data monetization, domain analytics, and application integration.

Said differently, data products transform raw data into valuable, actionable, trustworthy insights that everyone across the organization can use to make better decisions. Through analytics, algorithms, and visualizations, data products refine data to deliver clear, meaningful outcomes. They reduce complexity when it comes to data analysis, allowing users to easily identify trends, make informed decisions, and solve problems.

In essence, data products help organizations realize the true strategic value of their data by packaging it in ways that are accessible, understandable, and directly tied to business objectives.

Characteristics of good data products include:

• User-centric: The best data products are tailored to meet the needs of specific groups of users, from business analysts to executive-level decision-makers
• Automated: Data products employ AI and machine learning to automate the collection, processing, and analysis of data to reduce time and avoid errors
• Scalable: Data products are built to handle large volumes of dynamic data, which makes it easy for them to adapt and scale as data grows and evolves
• Interactive: Data products allow users to interact with the data through intuitive dashboards, predictive models, and visualizations
• Integrated: Data products integrate data from disparate, siloed data sources to ensure they represent the best version of the data
• Secure: Properly-developed data products adhere to an organization’s data privacy and security standards

Examples of data products

Data products can take shape in a number of different formats ranging from dashboards and visualizations to predictive models and recommendation engines. Each type of data product serves a distinct purpose and together, they enable organizations to harness the full potential of their data to drive smarter decisions, optimize processes, and create more personalized experiences.

Common examples of data products include:

• Business intelligence (BI) dashboards: Business intelligence dashboards, like those provided by Tableau, Qlik, and PowerBI, enable users to track metrics and KPIs in real time. Their user-centric design allows users to explore the data and easily refresh it as new or updated data becomes available.
• Recommendation engines: Recommendation engines use algorithms to deliver personalized suggestions for products or content based on user behavior and preferences. As a consumer, recommendation engines should be familiar to you. Good examples are Netflix and Amazon.
• Data APIs: Data APIs provide access to datasets for integration into apps or platforms, helping developers facilitate the smooth integration of data from disparate systems for real-time use. The Google Maps API and LinkedIn profiles are examples of data APIs.
• Predictive models: Predictive models utilize AI and machine learning to analyze large volumes of data to predict future trends, anticipate customer behavior, and generate forecasts that project sales outcomes. Companies like FICO, LinkedIn, and Zillow use predictive models to develop predictions and forecasts.
• Social media analytics tools: Social media analytics tools gather and analyze social media data to assess engagement, trends, and overall sentiment. Hootsuite and Sprout Social are tools social media professionals use to analyze information and glean insights that will help them optimize their social media campaigns and posts.

The key benefits of data products for businesses

Data products hold the potential to transform how organizations operate. By surfacing insights in a consumable way, everyone across the business can take advantage of real-time, trustworthy information that enables them to make better, more informed decisions that lead to better outcomes. Moreover, because data products integrate data from various sources, they also provide a holistic view of information from across the business, which helps organizations uncover hidden patterns, spot trends, and reveal new opportunities for growth.

In addition, adopting data products gives organizations a competitive edge. Recent data estimates that the world is creating 402.74 million terabytes of data each day, including newly generated, captured, copied, or consumed data. And as data volumes continue to grow – and the complexity of data continues to intensify – data products become even more critical in a company’s data ecosystem.

Because data products are designed to scale, they can manage large, increasingly complex volumes of data. As more data becomes available, data products can integrate new data into their existing framework and make it immediately available for consumption by data users across the organization.

Aligning data products with business goals

To drive meaningful outcomes and ensure that data-related initiatives deliver tangible value for the organization, it’s imperative that organizations align data products with business goals. When data products support key business objectives, teams can feel confident using them to make critical business decisions. This alignment also ensures that the insights are relevant, actionable, and tailored to the unique needs of the business which helps to foster innovation, enhance operational efficiency, deliver exceptional customer experiences, and maintain competitive edge.

To create data products that align with business goals, organizations must foster a collaborative environment where teams from across the organization can weigh in on the data and attributes that matter to them. This cross-functional collaboration is crucial. Not only will it help to preserve the integrity of the data product, but it will also ensure that the data product meets the needs of various stakeholders across the business.

Conclusion

To ensure the successful adoption of data products, it’s important to keep the organization in the loop as you develop data products. Similar to agile development, a best practice is to deliver a minimum viable product, ask for feedback, and iterate based on user suggestions. Not only will you demonstrate value and show momentum, but you will also secure their buy-in from the start.

As businesses continue to embrace digital transformation, the importance of data products will only increase. By investing in the right solutions, modern companies can transform their data into a strategic asset, empowering them to not only adapt to today’s dynamic business environment but to thrive in it.

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