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’s only now that we see it driving the board agenda as a fundamental competitive advantage.
In a recent AWS report on market competitiveness, 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, 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.
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.
Most companies have collected and stored a lot of data 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.
Tackling silos requires organizational change, which in turn takes time. As is now often a widely quoted fact, 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.
But how do we ensure we demonstrate this value to the wider business, and how do we build a program of work around it?
The backbone use case
To underpin this, we need a use case that drives the most commercial value – 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’s 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.
Data capability modelling
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:
- Understand the strategy, processes, data & AI landscape and prioritize the Backbone use case for in-depth scoping
- Evaluate the AS-IS, define the TO-BE state, and the requirements for delving into the Backbone use case
- Define an execution plan, roadmap, supporting business case figures, and a backlog for the first value release.
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.
Deliverables
- Backbone use case vision and scope: requirements, architecture vision, implementation effort and costs, roadmap, and team composition
- Architecture vision and scope for a Unified Data Platform: including use cases, flows, architecture blueprint, data governance, and suggested technology stack.
- Data governance recommendations: data sharing, monitoring, and security trade-offs analysis.
- Operational integrations considerations: for Structured Data and Unstructured Data capabilities.
- Roadmap execution plan: with milestones and dependencies, adoption approach.
- Cost model: Include estimates for Foundation/Implementation Phase and first Value Initiative to form the foundation for a cost/commercial model in the business case.
Getting started
Over hundreds of conversations with new and existing customers alike, we’ve found that a simple and effective first step is often defining a problem statement together that articulates the barriers facing the business and captures a vision statement to drive toward. Solutions like the AWS Data Strategy Diagnostic (DSD), for which Intellias is a delivery partner, can help provide quantitative data for internal stakeholder dialogue, too. We believe in co-created engagements and will work together with you to compose a scope that fits the budget and timelines.
Contact one of our data capability engineers for a full assessment of your data.
Intellias data capability modelling
Let’s build a data-driven business together to:
- TAKE THE COMPETITIVE EDGE with your customer and product data.
- ENSURE THE FUTURE-READINESS OF YOUR DATA TECHNOLOGY STACK for the increasing AI feature demands of customers.
- ELIMINATE BUSINESS AND PRODUCT SILOS, re-focus your teams on innovation and customer value-add activities.
