Data management as a discipline is playing vital role in digital and data driven organizations to help achieve its organizational goals. While data needs in these organizations growing rapidly across diverse set of channels, it is important to maximize the value of these data assets through better adoption across organization-wide business functions, meeting compliance and regulatory requirements, providing integrated view and access at the enterprise level, and enabling innovation through emerging business technology. These data assets with derived value will help achieve the company mission (e.g.: Becoming customer centric, building digital organization, building an AI organization, etc.). Setting up a data management function requires a good data strategy with set of data management practices, frameworks and best practices followed by ongoing plan to execute this strategy

1.    Data Strategy – Planning

1.1 Data management practices

Technologies around advanced data management practices should be built on proper data management capabilities foundation. Following these data management practices and proper hierarchy will lead to better organizational alignment, less risk, huge efficiencies, higher cost savings, and better standards. Data management strategy consists of several foundational practices or capabilities such as data governance, Data quality, Data platform/Architecture, Data operations. Advanced data management practices such as CRM, Data warehousing, MDM, BI, Big data and Analytics, and data integration should be built on core data management foundation/strategy. Since value of data is immutable and data is key driver for business decisions, it requires strong discipline to manage this as strategic asset. Governance is critical to ensure that data value is consistently maintained across various use cases within organization. Data governance focuses on improving master data, policies, and processes to manage data assets, enabling enterprise access to the data, defining ownership and access, setting up regulatory and compliance boundaries (e.g.: PHI, HIPAA, etc.)

A data strategy aligns with business priorities and objectives to plan and prioritize key data and analytics initiatives and activities. These efforts results in right data & analytics capabilities, business solutions, and technology assets while delivering constant business value for short-term and long-term business needs. Data strategy in life sciences helps with enabling customer centric vision, governing compliance, and regulatory requirements, promoting product performance, and driving innovation to take competitive advantage. Data strategy needs to engage various business functions such as commercial, medical, R&D, clinical, and manufacturing while aligning with organization-wide mission, strategy, innovation, and performance.

1.2 Planning and Value Drivers

Data management should be part of business function managed as program discipline vs. treating it as IT project. Data strategy considers organization-wide business needs, existing capabilities, and strategic data imperatives as its core drivers.

• Business needs captures company-wide mission, strategy and objectives for various business functions, structure of the business units, compliance protocols, performance measures and drive for innovation.
• Current state captures organizational readiness, maturity, business processes, data management practices, frameworks, data assets, technology assets.
• Strategic data imperatives consider business value targets, capability targets, tactics, data strategy vision.

Data strategy initiative requires building strong collaboration and partnerships with business stakeholders, IT, and external data and technology partners. While data is immensely powerful, but often it is not managed effectively to utilize its full potential. Data should be part of the organizational assets managed by central business function where IT is strong partner in this program.

Success for these programs needs to balance between value for immediate needs and capabilities to support future business events and innovation drivers to gain competitive advantage. Business case to convince senior executives focuses on why and what part of strategy, but not much on how. That is why some of the long-term value drivers needs to be justified. It is important to make this more actionable and relevant to business priorities. Set of quantitative metrics and qualitative measures needs to be defined to measure the success of this program

2.    Data Strategy – Execution

2.1 Road-map and Execution

Data strategy is a program executed as a dedicated project owned by business and strongly partnered with IT to leverage all assets. Change management should be integral part of the execution to drive adoption, organizational readiness for innovation. Data strategy projects are iterative in nature with constant evolution through multiple versions aligned with business roadmap and priorities. Lengths of the data strategy process depends on the strategic planning maturity of the organization

Data strategy execution involves various process steps as below

1. pre-planning to gather inputs around business objectives, identifying key stakeholders, participants, groups, and other roles, creating charter, and creating communications plan
2. alignment with organization strategy, interviews and assessments, prioritizing components
3. development of initial strategy, stakeholder reviews, and socialize plan
4. finalizing strategy and budget for execution, and communication of strategy
5. iterate through versions by taking next set of priorities considering the learning from previous iterations. This is program implemented as project in multiple cycles.

Key deliverable for data strategy can be a business presentation with following components, 1) Background and objectives, 2) Vision, business case and benefits, 3) Goals, objectives, strategies, and initiatives, 4) Implementation road-map and priorities, 5) Risks and success enablers, 6) Budget estimates for people, technology, project, etc., 7) KPIs and Metrics

Components in data strategy road-map is prioritized across several dimensions, 1) value based on business goals & objectives, 2) leveraging existing capabilities and assets, 3) new data capabilities your organization needs, 4) other strategic priorities and innovation initiatives for long-term benefits and to gain competitive advantage.

2.2 Key Success Factors

1. Dedicated owner with role such as “Enterprise Data Executive” or “Data Strategy Lead” need to be assigned to lead this enterprise wide data management program. Lacking key leadership and competency will lead to inefficiencies and failures
2. Complete alignment and periodic engagement of key program sponsors and senior executives is extremely critical for the success of this program
3. Business should own the program by involving senior leads from each of the business units along with IT. All the key roles including program sponsors, core team, review team, decision makers, budget approver, etc. Program should follow the strict engagement model to ensure 100% participation from each of these roles
4. Communication strategy is extremely critical to ensure you get constant alignment from every single function to avoid any surprises related to data assets, capabilities, and impact and perceived value from this program. Setting right expectations and timing (state or versions) is a key to success.
5. Companies should not rush towards building solutions straight out of the project based requirements, but they should consider programmatic approach of integrating the 3 key pillars including organization-wide business needs, existing capabilities, and strategic data imperatives to build a data strategy
6. Culture is the biggest driver to a shift in organizational thinking about data. Organizational change is not complete without any of these components including vision, competency, incentive, resources, and action plan. If any of the above components are missing, this may lead to one or more of these impacts such as ambiguity, nervousness, delays, mistrust, misalignment
7. Data sharing must be done at the program level. This saves a lot of IT and processing overhead and costs
8. Data strategy should not be one-time massive effort, but it is iterative program implemented as projects. Road-map should be designed as per the prioritization of the business goals and should be clearly aligned and agreed with all business functions through periodic governance meetings
9. All business and IT teams should contribute to this program and collaborate effectively. Lack of cross functional collaboration is often one of the key reasons for failure