Keys to scaling your data products: Prepare your data for the era of generative AI

As companies increasingly rely on data to innovate and grow, ineffective data product practices have positioned themselves as one of the top strategic issues.

Companies that have a clear roadmap for developing a well-thought-out data product program can identify high-value cases for quick wins, as well as lay the foundation for continued value generation over time.

We unpack the keys to designing a data architecture that accelerates data readiness for generative AI and enables unparalleled productivity for data teams.

As McKinsey explains, most executives’ focus and energy are focused on one or two specific use cases, as this allows leaders to show activity and celebrate impact. In other cases, CIOs are forced to work in multiple directions with requests to create specific data products without an effective way to evaluate their costs and benefits to the enterprise as a whole.

But leaders need to have a clear vision of where the greatest value to the business lies. This starts with the discipline to analyze the value potential of each use case in the enterprise program and then group those that are based on similar types of data. If there are no other relevant use cases, there is no need to develop a data product.

However, if multiple high-value use cases are based on similar data sets, this makes a good case for developing a data product. In fact, the more use cases a data product can address, the greater the value it can generate.

Fragmented data stacks, pressure on productivity, and lack of data readiness for generative AI are driving enterprises to evaluate new data strategies. Products like Data Fabric are designed to harness the power of AI and optimize the integration, curation, governance, and delivery of high-quality data for analytics and artificial intelligence.

The next-generation data fabric is hybrid and can run anywhere, either on-premises or in a cloud environment, as well as integrate into hybrid data planes, enabling any style of data integration.

The key features of this data architecture are:

• Augmented Knowledge Graph: This is an abstraction layer that provides a common business understanding of data processing and automation to act on insights.
• Intelligent integration: a range of integration styles to extract, ingest, stream, virtualize, and transform unstructured data, driven by data policies to maximize performance and minimize storage and costs.
• Self-service data usage: a marketplace that supports self-service consumption, enabling users to find, collaborate, and access high-quality data.
• Unified lifecycle: end-to-end lifecycle management to compose, build, test, optimize, and deploy various capabilities of a data fabric architecture.
• Multimodal governance: unified policy definition and enforcement, governance, security, and data management for a business-ready portfolio.
• AI and hybrid cloud: an AI-infused composable architecture designed for hybrid cloud environments.

All in all, a data framework is essential for enterprise AI, which requires reliable data built on the right data foundation. Whether to simplify the day-to-day work of data producers or to provide self-service data access to data engineers, data scientists, and business users, a data framework prepares and delivers the information needed to gain better insights and make better decisions.

One of the main keys to successful data scaling is to identify the data strengths that set us on the path to success, whether through infrastructure optimization or process optimization.

But in these keys is also understanding the underlying principles that drive successful scaling. One of these is the concept of data elasticity, which refers to the ability of data to stretch and expand as the organization grows. This can be translated into designing a data infrastructure in a way that allows for easy scalability, ensuring that it can handle growing data volumes effortlessly.

At this point, it will be very important to assess the maturity of your data, because you will be able to identify strengths and weaknesses, which paves the way for effective scaling strategies. Several factors, such as data quality, data governance, and data integration, should be considered here.

Another aspect to consider is understanding the role of data analytics. By leveraging advanced analytics techniques, such as ML and predictive modeling, valuable information can be extracted to drive business growth and innovation.

But one of the most important keys is to internalize that data scaling is not a one-off, but an ongoing process that requires constant monitoring and optimization. As companies evolve and new technologies emerge, strategies must also be adapted accordingly.

Therefore, successful outcomes require adopting data elasticity principles, leveraging advanced analytics techniques, and remaining agile in an ever-evolving data landscape.

Creating multiple data products using an industrialized approach is much more efficient than creating them manually from scratch. It is therefore critical for decision makers to understand and appreciate the economies of scale that come from reusing elements and templates across different data products.

By adopting common processes and principles, the initial development costs of the first data product can be amortized over subsequent products, reducing costs across the program, especially as teams grow and share expertise. This is why it is so important to understand that data products are constantly evolving objects, and must be easy to maintain and update to optimize their value over time.

It is also crucial to create data products that bring us long-term value, which means maximizing the reusability of the technical components of data products. Focusing on data engineering and establishing standards and templates may seem like a laborious task. Still, it ensures that programs can scale and generate a positive flywheel effect, with continued momentum building over time.

In addition to being used to drive AI programs, data products can benefit from the adoption of generative AI in their creation and ongoing support. Therefore, organizations should break down the different steps of data product creation and understand where generative AI offers advantages in terms of consistency, speed, and efficiency. This includes the preparation and implementation phases of data products, such as pipeline creation, data quality monitoring, and testing and publishing.

Generative AI should be incorporated directly into data product workflows, consistently across teams, to optimize processes and leverage its benefits, creating better and more usable data products that meet user needs.

At Plain Concepts, we help you formalize the strategy that best suits you and its subsequent technological implementation. Our advanced analytics services will help you unlock the full potential of your data and turn it into actionable information, identifying patterns and trends that can inform your decisions and boost your business.

Our goal is to approach the challenge of digital and data strategy from a business prism with which you will be able to profit, using a structured framework according to your needs.

With this approach, we define the necessary digital and data strategy through a process of immersion, maturity, and consolidation, working on the generation of short-term benefits that give credibility to this strategy:

• We assess the company’s data maturity level.
• We identify critical data to manage, control, and exploit.
• We establish objective use cases, focused on generating medium-term benefits, and design the initiatives to implement them.
• We generate interest and commitment in your team through training on the importance and potential of data-driven management.

If you want to start converting your data into actionable information with the latest data architecture, storage, and processing technologies, contact our experts and start your transformation now!