Strategies for building a Data Driven Organization in the Enterprise
Every Organization try to be Data driven and as new role of “Chief Data Officer” becoming popular thanks to cloud scale analytics. One of the most common questions asked by almost all the Customers in my consulting journey is “How to implement a Data driven culture.” In this article I will be covering some of the major points to consider when building a data driven organization.
Establishing a broader Vision
The first step to start on this journey is to set up a vision. The vision helps to aspire and get to the final goal. The vision should highlight the goal and the benefit of a Data driven organization and align all the stakeholders to work towards the vision. There could be bunch of smaller vision that are achievable which can lead up to the larger vision. Some of the common examples of vision can be “Harnessing power of AI and Analytics for deliver faster insights”. “Accelerating Digital transformation through Data driven approach”.
Establishing Drivers for the Vision
Drivers are the objective behind each of the visions and define the purpose and goals for the vision. Drivers are created by asking the right set of questions. These are some of the questions that establish the drivers for the vision.
· Need to establish a data-driven culture within the organization?
· Is there a shift within the organization for digital transformation?
· Are the scenarios (what ifs included) of new or augmented businesses practiced?
· Are there new business cases where proof is needed for their actions?
· Is there a requirement to build a foresight driven organization or business models?
· Is there a need to enhance the current use cases where business users are demanding a better insight into their businesses?
· Is M&A activities driving the need of better data integrations to deliver a quicker / faster insight?
· Is there a need to build a roadmap for the modernized data analytics platform that includes AI, ML, and other tools to be ready to accept the (data-related insights) demands from the business or to be proactive?
· Is there a need to build Data and AI capabilities to further strengthen the use of the data by the organization to fuel growth and net new revenue.
Defining the Strategic Priorities
Considering the nature of the data, the skills and the tools that are in place, the strategic priorities should align with the vision and help fuel the drivers delivering the business value through measurable outcomes. Some of the strategic priorities are.
· Build an enterprise data ecosystem — data at the speed of business.
· Fuel growth and drive efficiency through insights — Revenue, cost savings, new opportunities focused solutions.
· Champion responsible use of data — promote ethics and establish governance.
· Advance enterprise data literacy and data culture — define roles, develop talents and resources across the organization.
· Strong data leadership — evangelize at all levels.
· Develop Open and trusting culture — Removing silos.
Defining the Stakeholders and the Benefits
The Stakeholders need to be identified and highlighted with the benefits that would be realized. It is important to clearly define roles, responsibilities, and their outcomes & benefits as you develop the data-driven process. The stakeholders from business and technology need to be connected, collaborated, and blended into the process. First, build out a cycle of data maturity and create an alignment with the business processes as well as roles within your organization. The focus areas of each of the cycle elements need to be defined and the stakeholders need to be aligned with it.
Define Design Principles
The Design principles are a set of enablement approaches that are aimed to reduce friction in the use of data and drive accountability across the enterprise. Some of the design principles to be considered are.
· User Experience — Minimize manual process, role-based access, fastest insight, highly interactive.
· Establish and empower scenario planning by the business — data readiness, availability, and fastest insight to further validate new business models to gain insights to the foresight planning.
· Align business priorities and processes to the data governance processes and vice versa — global data ownership and its accountability, along with usage.
· Drive business data accountability — like scenario planning, above, but more towards to their use cases for data value creation.
Define the Delivery model.
Once the strategy is built, the delivery model such as technologies, business benefits and technology partners should be selected. There should be specific leaders appointed who play the role of the “Chief Data Officer” and dedicated Data Governance council that consists of business and technical users should be set up. Detailed delivery documents should be created, and the process executed.
