{"id":91126,"date":"2025-09-11T10:57:48","date_gmt":"2025-09-11T07:57:48","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=91126"},"modified":"2025-11-12T16:18:01","modified_gmt":"2025-11-12T14:18:01","slug":"data-capability-modelling","status":"publish","type":"blog","link":"https:\/\/intellias.com\/data-capability-modelling\/","title":{"rendered":"Why the World Is Now Investing in Data Capabilities \u2013 It\u2019s not Just About AI"},"content":{"rendered":"

Universally across sectors, customers are prioritizing investments to build modern data teams and capabilities. This is nothing new; central data architecture has been a topic for 10 years or more, but it\u2019s only now that we see it driving the board agenda as a fundamental competitive advantage.<\/p>\n

In a recent AWS report on market competitiveness<\/a>, 79% of organizations reported increased pressure for AI functionality from their end-customers. The hype generated by AI has re-shaped customer expectations; automation, product insight, and AI functionality are considered essential USPs and the key to effective competitiveness. And yet, the enterprise data capability required for these features is often not there. According to the Project Management Institute<\/a>, 70-80% of organizations that start developing AI applications will fail with poor-quality data as the main reason why. Successful AI initiatives must use high-quality data for model training, throughout deployment, and during operation.<\/p>\n

Yet, many companies are only now realizing the need to prioritize the health of their data. They have operated in data silos, with different systems in different departments, each with unique data requirements. Until recently, this was a manageable problem. Now, customers are demanding better insight from products, tailored services, and yes, AI features and functionality. Now it is time for many to move up the data maturity scale.<\/p>\n

\"A<\/p>\n

Most companies have collected and stored a lot of data<\/a> and built capabilities in specific areas, advanced performance\/SEO analytics, for example, or a data science team experimenting with AI use cases. This makes progression up the maturity scale at an organizational level challenging, as pockets of capability exist in silos.<\/p>\n

\"Why<\/p>\n

Tackling silos requires organizational change, which in turn takes time. As is now often a widely quoted fact<\/a>, most large-scale transformations fail. Intellias believes that incremental steps, each winning investment for the next phase of work by demonstrating value to the wider business, is the best way to avoid the risk of large-scale transformation.<\/p>\n

\"A<\/p>\n

But how do we ensure we demonstrate this value to the wider business, and how do we build a program of work around it?<\/p>\n

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Discover how prepared your organization is to leverage AI and where to focus next.<\/p>\n

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The backbone use case<\/h2>\n

To underpin this, we need a use case that drives the most commercial value \u2013 quite simply, this is the use case that will make money, save money, or mitigate risk, potentially all three at once. Many customers will already have an idea of what this is, buried in the vision for a data-driven and holistic organization; it will typically represent a core business process or scenario that is critical to the client\u2019s operations and is used to validate assumptions, test proposed models, and ensure consistency in solution design. By clearly defining this backbone use case early in the program design, we can expedite decision-making, maintain focus on delivering value, and provide a north star to aim toward.<\/p>\n

Data capability modelling<\/h2>\n

The Intellias method for translating vision into the Backbone use case and embedding into a roadmap of data initiatives is Data Capability Modelling (DCM). A small team, working closely with executive stakeholders over a 4-week assessment, will:<\/p>\n

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  1. Understand the strategy, processes, data & AI landscape and prioritize the Backbone use case for in-depth scoping<\/li>\n
  2. Evaluate the AS-IS, define the TO-BE state, and the requirements for delving into the Backbone use case<\/li>\n
  3. Define an execution plan, roadmap, supporting business case figures, and a backlog for the first value release.<\/li>\n<\/ol>\n

    \"A<\/p>\n

    Three streams make up the analysis: Process & Strategy, Data, and AI\/ML, providing a deep dive into the components that will ultimately drive the vision for data enablement. At the end of these four weeks together, we provide the following deliverables.<\/p>\n

    Deliverables<\/h2>\n