Unleashing the competitive edge: 5 reasons enterprises need to scale data productization

If the future of business lies in one area, it’s data. It’s the underpinning of every successful business strategy. But it’s not data alone that powers progress – it’s the ability to use it productively, leveraging it as a tangible, value-creating commodity.

This is the concept of Data-as-a-Product (DaaP). DaaP disrupts traditional data use, generalizing it for streamlined consumption and democratizing data access across organizations by focusing on outcomes and process in equal measure. It is a framework – a cultural shift as much as a technological one – that is focused on enabling data literacy while also providing business value. Implemented effectively, thinking about data as a product might be exactly what the enterprise needs to achieve a competitive advantage in the data economy.

Despite economic uncertainty, according to the Data and Analytics Leadership Annual Executive Survey 2023 from NewVantage Partners, cited in an article on TechTarget, 93.9 per cent of organizations plan to increase data investments this year. The biggest driver of these investments is to discover and realize the business utility of owned data to gain an edge over their competition. With this in mind, here are five reasons why your organization should tap into data productization at scale.

  1. Maximizing Data Utility

Data utility is different from data usability. Utility is measured by how well-built a dataset is, whereas usability is more focused on whether the data fits into a specific business case. The problem with most organizations is that they’re often more focused on getting useful data than they are on maximizing the utility of the data they have. DaaP empowers businesses to unlock the full potential of their data, ensuring that every byte is leveraged to its maximum capacity.

By providing a centralized platform that comprehensively catalogs and organizes all data assets, DaaP enables businesses to transform each individual product into a strategically utilized asset that significantly contributes to their overall bottom line. This approach enables businesses to optimize data utilization, enhance decision-making processes, and drive innovation, ultimately leading to sustainable growth and competitive advantage.

  1. Improving Efficiency

DaaP, at its core, reframes the idea of data in the enterprise: how it’s used, who it’s used by, and how we think about it. By prioritizing the utility, health, and governance of data products, organizations can effectively streamline their operations and enhance productivity. This not only ensures that data assets are well-managed, but also enables businesses to make informed decisions based on reliable and accurate information. With a strong focus on data quality and governance, organizations can maximize the value of their data assets and drive better outcomes. This not only leads to more innovation, but will also reduce spend. A well-organized data environment is a cheaper one.

  1. Increasing Revenues

A successful DaaP strategy not only involves the creation and distribution of valuable data products but also encompasses the implementation of robust data governance frameworks and data-driven decision-making processes. By leveraging these capabilities, organizations can harness the power of their data assets to unlock new opportunities for monetization, revenue growth, and competitive advantage in today’s data-driven economy.

  1. Enhancing IT Cost Predictability

DaaP plays a crucial role in enhancing cost predictability by minimizing over-engineering and maximizing value creation. By prioritizing the relevance and marketability of each data product, companies can better project and manage their expenses. This approach ensures that resources are allocated efficiently and enables organizations to make informed decisions that drive long-term success.

  1. Adapting to Evolving Trends

In today’s rapidly changing landscape, being open to the DaaP paradigm is crucial for organizations to stay ahead of the curve. Embracing this approach enables them to ride on the wave of emerging trends like Data Mesh or Data Fabric, which revolutionize the way data is accessed, shared, and analyzed. By embracing these cutting-edge technologies, organizations can ensure they’re not left behind in the data revolution and unlock new opportunities for innovation and growth.

Key Considerations

Adopting and scaling data productization isn’t a one-size-fits-all solution, and it doesn’t happen overnight. It necessitates a certain technological infrastructure, strategic processes, and, critically, an adaptive and welcoming culture. It’s an evolution, not a revolution. Still, those who are able to successfully navigate this process will be rewarded, reaping the financial and strategic benefits of a productive, efficient, and data-driven enterprise.

Leading data catalog platforms and data monitoring solutions providers are at the forefront of this movement, and are focusing their attention on providing value to all data users across the enterprise, from data engineers to business analysts. With the right technology partner, your organization can increase discoverability, automate data management, monitor data quality, and provide a robust framework for data distribution. It’s important to choose the right one to help you shape your data strategy in a way that delivers the highest value possible.

Thinking of data as a product is not a panacea. It is a necessary adjustment in the tech landscape and a welcome change in a problematic data culture. Reframing the status quo is a consequence of a new economic climate, the emergence of mainstream generative AI, and a decade of underwhelming data strategies at companies big and small. The enterprise is tired of hearing that data is the new oil. They need ironclad strategies for getting the most out of what they have.

The next five years will be marked by significant advancements in data productization because it so effectively harmonizes IT and analytic divisions and supports all business lines equally. Whatever nomenclature takes hold – DaaP, Data Fabric, Data Mesh – doesn’t matter. What does matter is that the companies that streamline data consumption, prioritize process and outcome in equal measure, and increase innovation while reducing spend will wipe the floor with the companies that don’t.

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Jim Love, Chief Content Officer, IT World Canada
Lewis Wynne-Jones
Lewis Wynne-Jones
Lewis Wynne-Jones (VP of Product at ThinkData Works, he/him) has 7+ years experience helping organizations achieve their digital goals, including two years researching and cataloging the world’s largest collection of public data. He is the principal author of white papers on data trusts, active metadata management, and data governance, and was a founding member of the Roche Data Science Coalition and the Ontario Chamber of Commerce Data Working Group. Select speaking engagements include: SDTC Future Cities Cleantech Summit, ORION: THINK Open Conference, CSPS Digital Academy, and Data Driven 2.0.

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