Often the most challenging steps in establishing a data and analytics (D&A) strategy and framework are the first steps. Companies across all industries are facing a push to modernize their D&A platforms, and each has a unique set of needs and goals in this transformation. Understanding where challenges can arise is essential to achieving these goals.
There are a common set of questions or observations for risk leaders at the crossroads of a big strategic decision, such as:
- My analytics processes and technology are failing to provide the benefits we expected.
- How do we stablish enterprise-wide scalable and repeatable processes and controls?
- How can we effectively utilize and improve our data that is disparate and unreconciled?
- How do we clarify our governance processes to reflect our evolving capabilities?
To support organizations struggling with these challenges and questions, we developed the Data, Organization, Technology, Strategy (DOTS) framework. The DOTS framework allows organizations to understand, assess and align their D&A strategy and efforts with their business goals. Determining the right balance in data, organization, and technology enables organizations to make collaborative, mutually beneficial decisions that can drive long-term value.
Data, Organization, Technology, Strategy (DOTS) Framework
The DOTS framework allows organizations to understand, assess and align their D&A strategy and efforts with their business goals.
Technology
A consistent technology stack coordinates data collection, integration and management, enables teams to perform high quality real-time customer analytics, and effectively collaborate and consume those insights through integration with business processes
Storage & Integration | Collaboration & Consumption
Organization
A globally aligned operating model coordinates activity to avoid duplication, share learnings, insights and models, and build digital literacy across the regions.
Structure & Roles | Expertise & Fluency
Strategy
The organization shares a vision for advanced analytics, drives a data-driven culture, and prioritizes the work to remain laser focused on the customer, while also executing consistently and at high quality across all regions.
Vision & Culture | Execution & Alignment
Data
Internal and external data required to understand customer needs and issues are collected, made accessible to the right teams, and are maintained at high quality.
Policies & Access | Management & Quality
Insights
Models and analytics are monitored to ensure value delivery, effective business process implementation, and appropriate governance, risk and controls.
Scalability & Controls | Value & Usability
That first step in any D&A strategy is understanding what data are available and what tools, people and processes need access to that data. This initial step focuses on data, because it provides tremendous insight into priorities of the organization, limitations for near-term strategy goals, and opportunities to improve the information culture. Everything else relies on data management. Additionally, an organization needs to consider how that data will be stored, and how to hire / engage the right people to catalog, maintain, and provide that data to the downstream users who require it.
Next the technology architecture needs to be defined. Once an organization knows the data strategy, it is capable of designing a technology architecture to enable success. It is critical to consider existing internal capabilities, as well as new tools and external support that may be required to get the architecture off the ground.
Once the data and technology requirements have been determined, the organization needs to begin designing and building their team. A new D&A strategy requires specialized skills that should be defined before they can be turned into job descriptions, head count assessments, organizational charts and recruiting efforts. It is also important to consider how existing team members may be re-skilled to take advantage of the new ways of working in a data-driven environment.
After the data, team, and technology are in place is time for the organization to focus on execution at scale. Proofs of concept (POCs) are a great way to start, but the organization should also remain focused on how to achieve enterprise scale for their D&A initiatives. Collaboration is essential at this point. POCs run the risk of becoming established standards. Critical eyes from all parties are needed to determine how benefits are realized for all stakeholders and what else can be done to improve the POC into a production-ready solution.
While D&A modernization may appear overwhelming, with the right guidance and the DOTS framework in place, the journey can lead to lasting enterprise-wide value in the form of cost take-out, increased efficiency, and process optimization.
