As a consultancy firm dedicated to helping enterprises navigate the modern technology stack and get more value out of their data, one of Hakkoda’s first key objectives was to develop a clear and actionable roadmap to data maturity.
To that end, we pioneered a model we call the Data Innovation Journey, which assesses organizations’ data practices and measures their data maturity across four stages: Chaos, Order, Insight, and Innovation.
An organization in the Chaos stage, to give an example, has likely only just begun its data innovation journey, and will need to overcome the silos, maintenance costs, and manual inefficiencies of outmoded legacy platforms.
On the other extreme, an organization in the Innovation stage has already reached a relative state of data maturity and is now ready to tackle more advanced data projects that unlock new revenue streams and integrate AI to drive further efficiencies.
In this article, we’ll discuss the four stages of the Data Innovation Journey and define how we distinguish each step in the data maturity process. We will also draw on findings from our 2024 State of Data report to identify key themes at each stage.
Building Practical Data Roadmaps with Data Innovation Journey Calculator
The Data Innovation Journey calculator is an interactive tool that allows users to provide information about their organization’s data practices and receive immediate feedback about where they fall on the data maturity continuum.
Depending on where an organization falls on this roadmap, the Data Innovation Journey provides businesses with both a diagnostic framework and concrete data strategies that can help them decide on a practical course of action.
The calculator also acts as a living document that can be updated to reflect organizational and technological changes, ensuring that companies are able to track their progress at every stage of your journey.
Stage One: Chaos
The first stage in the Data Innovation Journey is Chaos. As the name implies, one of the key features of an organization in Chaos is scattered data that lives in a host of different places across the business.
Siloing data across several platforms makes the data harder to access and therefore harder to use, both for everyday purposes like reporting and analysis and as the foundation for Gen AI and other advanced data capabilities that require clean, quality data to succeed. Siloed, poorly governed data also makes it more costly for businesses to use the information they have, requiring more manual intervention and leaving you beholden to third party developers that can delay projects and bury critical insights.
A reliance on several BI tools often goes hand in hand with this scattered data approach, which makes processes slower and reduces trust in data security.
Stage Two: Order
The second stage of the Data Innovation Journey is Order, in which an organization’s data stack becomes more centralized, often following a major cloud migration.
Data now lives within a centralized data cloud platform in which tools are consolidated and interaccessible. Legacy technologies are beginning to be phased out to streamline processes, and data quality tools are being deployed to sort out useful data from the noise.
A key feature of the Order stage is that data can now be managed in a single location, with changes being reflected across the various tools that make up the organization’s data platform.
This is sometimes referred to as a “single source of truth” for the organization’s data, and has myriad downstream benefits including increased trust, improved collaboration between business units, and shortened time to insight.
Stage Three: Insight
The third stage—Insight—is achieved when a data stack begins to work for the organization and not the other way around.
With an increasingly efficient data stack in place, Insight organizations have the agility and resources to begin experimenting with their data approach, identifying potential areas where truly innovative practices might be possible.
Data-driven insights are built into operational processes. They are not only accessible—they are seamlessly integrated into the organization’s workflow, driving decision-making at every level.
Because the data stack has been engineered for efficiency, the burden of maintenance is drastically reduced, requiring less manpower from internal data teams.
Stage Four: Innovation
The fourth and final stage is Innovation, which is defined by a wholly unified data platform that has been engineered to fully realize the value of organizational data. In the Innovation stage, the organization also has the power to generate new streams of revenue through data monetization and advanced Gen AI use cases.
Meanwhile, the effort required to manage the organization’s data stack has been reduced to the point that the majority of data functions are self-sufficient.
This frees up the organizational data team to focus their efforts building new capabilities that set their company apart from the competition—this being the true spirit of data innovation.
The work doesn’t end here, but the real possibilities do begin here, as a truly mature data stack gives your organization the agility and insight needed to dream big.
Data Innovation by the Numbers
Hakkoda’s 2024 State of Data report takes these stages of data maturity and applies them to some of the top companies across data-driven industries.
In the report, our research reveals the specific practices, tools, and philosophies that distinguish the most data-mature organizations from their competition. The results were clear: our Data Innovation Journey framework paints a powerful and accurate picture of the steps that lead to data maturity.
Innovation organizations achieved 37% more of their data goals in 2023 than Chaos organizations, reporting a 164% ROI on their data tech investments, compared to the 73% reported by Chaos organizations.
Overall, Innovation organizations were far more likely to be using centralized data cloud platforms, Gen AI, and other sophisticated data functions like machine learning and LLMs. Consequently, they were also more likely to be successfully monetizing their data.
Data Innovation Journeys Start with Hakkōda
It’s no accident that Innovation organizations are relying on external support to help them modernize their data stack.
These companies have a sense of urgency about realizing the full value of their data, and they know having the right tools and capabilities is the only way to do so. Instead of overburdening their internal data teams, they turn to outside experts who can keep them agile and protect their bottom line, all while delivering the highest possible level of data support.
That’s where Hakkoda comes in. Our data experts know what it takes to bring a data stack from chaos to innovation, and our collaborative approach to modernization means we work with our clients to build the data stack that works for them.
If you’re ready to take charge of your Data Innovation Journey, let’s talk today about how Hakkoda can help your organization achieve true data maturity.
