Refocusing Data Strategy to Value Realization as Generative AI Ramps Up

data strategy
data strategy

In a Gartner survey centered on the adoption of generative AI by the data and analytics functions of enterprises, 40% of the respondents claimed that they’ve either experimented with generative AI, integrated it into some of their processes, or have completely integrated it across their operations. Lingaro Senior Director of Technology Consulting Practice Carlos Navarro shares his insights on the latest developments in AI and machine learning and how they spur the need to modernize their tech stack and create more business value out of it.

What are the most pressing challenges that businesses face today when it comes to data management and strategy?

We see our customers needing to modernize their data platforms. But, at the same time, they have to prove the value of previous and new investments. Generative AI has accelerated their need and increased the visibility of gaps in their data foundations. By gaps, we mean to say challenges like overreliance on technical resources when consuming data and difficulties in data governance, data platform modernization, and data value realization.

Carlos Navarro on data strategy

Data value realization, in particular, is a critical concern, as many companies are realizing that the huge investments done to date do not let them fulfill the new data consumption needs of tools like generative AI. This puts leaders in a pinch as they need to justify their new investments or work on their modernization with limited budget.

As the data and AI partner of our clients, we aim to increase the value of their data, modernize their data platforms to utilize new generation technology and architecture in a cost-effective manner, and enable their organization to scale up data consumption.

How does the evolution of AI and machine learning affect the data strategies of enterprises today?

Generative AI has had a huge impact on businesses, since enterprises can see a fully democratized solution that users will actually use to consume insights from data, as opposed to letting data gather virtual dust in storage. It also helps increase the productivity of traditional data management tasks. For these reasons, the focus now will be in looking for cost-effective ways to employ AI for enabling data self-service, generating of business insights out of data for data consumers, and enhancing the productivity of data management tasks for data producers.

In developing data strategies, are the primary drivers costs and ROI?

When it comes to data strategy in 2024, the critical drivers seem to revolve around costs and a clear understanding of return on investment (ROI). Determining ROI remains a significant challenge for large-scale data projects within enterprises, given that many investments in data management solutions over recent years haven't delivered the expected results. The focus is on adopting approaches that effectively cut costs and provide sustained effectiveness in the long term.

Let me share an example from one of our recent projects to illustrate this point. A company was doubly hurting from the high cost of their existing data platform and low value realization. We proposed a migration to a modern, data lakehouse-based solution that would significantly decrease the total cost of ownership of their platform. Our pilot project proved that the company could reduce 80% of costs by using our solution. This cost optimization allowed our customer to justify investment in migration and ensure the platform was ready for future data demands of tools like generative AI.

data lakehouse-based solution

What holds companies back the most when it comes to realizing business value from their data investments?

Siloed data sources are one of the most common challenges we can see our clients face. That is why we developed our “Data as a Product” framework. Treating data as a product involves assigning data product owners and providing a place to share and consume data products — in short, a data marketplace. With this, companies can better realize the value of their data investments and create a culture of data collaboration.


What other factors can substantially enhance the ROI of data projects in companies?

For sure data governance is one of them. Let me share another real-life example. A customer was suffering from data governance issues but was looking to implement a governance approach that was traditional, rigid, and difficult to implement. We proposed an alternative, modern approach, one that implemented a Data as a Product framework to ensure better data governance and ownership. Not only is it an easier approach to scale, but it ensures that valuable data is reusable, accessible to everyone, and properly managed.


How can organizations maximize ROI from their data investments?

Again, we go back to the key differentiator, which is value realization. At Lingaro, we combine two key strengths in helping companies. The first is our technical expertise. We are up to date with modern data trends and platforms and are fully equipped to help customers modernize their data platforms through cost optimization.

The second is value realization embedded in our technical enablement services. This means that whatever we do, we are always ensuring that our technology solutions create business value for our clients.

How can companies ensure value realization in their data environment?

This not only uses an existing data platform or environment, but also provides easier access to data, thereby accelerating the realization of value.

Lingaro is a client-first data and AI partner. The design of our own data marketplace as a Teams App represents this. We understood the need to create a one-stop shop for data discovery and consumption but didn’t want to add another tool to the already overly saturated tool landscape in organizations. That is why we designed our data marketplace and placed it in the already existing tool which everybody uses most every day: Teams.


What will the future of data platforms and strategies look like in the next decade?

Five to 10 years is too much in advance. Right now, we see significant progress of platforms every two to three years. Last year, we had the lakehouse transformation, which achieved maturity after many years of existence. This year, we are seeing augmented data platforms with generative AI. Data platforms need to advance to be more open for nontechnical users while securing data governance and key data management principles.

We are also seeing a complete transformation in the way that users will consume data and insights. Today, we’re seeing the importance of data literacy, of knowing how to read data. Now, we are focusing on prompt engineering, where users will be taught on how to ask questions to AI to get the most effective insights from it.

It’s all about getting value from your data. With that in mind, it’s no longer enough for data solutions providers to merely create tools according to their clients’ specs and just leave them be. Enterprises must find AI and data partners who’ll help them create value from their data every step of the way.


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