Data management is an administrative and governance process for acquiring, validating, storing, protecting, and processing organizational data. With the growth of Big Data, enterprises of all sizes are generating and consuming vast quantities of data to create business insights into trends, customer behavior, and new opportunities. Data management strategies help companies avoid many of the pitfalls associated with data handling, including duplicate or missing data, poorly documented data sources, and low business value, resource-intensive processes. An enterprise data management strategy can help organizations perform better within the markets they serve.
Although the strategy drives the organization, there should also be tactics, projects, and operational goals established based on the strategic plan. Data itself get transformed into information, information into knowledge, and knowledge in decisions by and for the organization. It is important to remember these stages of transformation as organizations adopt technology, practices, and processes and enable people to support the organization and its customers. Data management as an organizational capability is a business team effort and should not be siloed into just an IT function.
Data management strategy and master data management strategy should follow data science best practices. These practices can be seen in and underpin data engineering, data analytics, machine learning, deep learning, and artificial intelligence disciplines.
Data management strategy is enabled by people, processes, technology, and partners and is supported by the business strategy goals and IT strategy goals. The strategy itself is a plan or roadmap with an overall budget that supports the organization’s other strategic projects.
Data Management Strategy Approach
Data management goals and objectives examples could be to enable better decision-making, reduce operational friction, protect data stakeholders, and have a best practice common approach to data issues. These goals and objectives are allowed with a data management strategy. Approaching any strategic initiative can be challenging, especially data management.
The following are steps for defining a good strategy approach:
- Identify business objectives by performing a business assessment to understand business strategy, business technological direction, operating model, and policies and procedures
- Decide how IT supports business objectives with data strategy by doing a strategic assessment
- Understand the demand for data management
- Understand strengths, weaknesses, opportunities, and strengths
- Define the market spaces a data management strategy is needed for
- Identify any strategic industry factors
- Identify all organizational gaps in capability and resources for success
- Generate the strategy
- Establish priorities
- Establish goals
- Establish objectives
- Form a position
- Craft a strategic plan
- Decide measurements and critical success factors
- Define expected return on investment (ROI) and total cost of ownership (TCO)
- Execute strategy
- Create strategic plan
- Create risk assessment
- Get business buy-in, alignments, and integrations to the plan
- Deploy IT assets – people, technology, process, partners
- Decide and define governance and compliance capability
- Hire Chief Data Officer to be accountable for strategy execution
- Create standards, policies, and procedures
- Support execution of plan across organizational, functional units
- Monitor strategy and evaluate for continuous improvement
- Obtain feedback from data management architecture and design
- Find the right technology that supports the budget and objectives
- Establish data management practice
- Establish data governance
- Design and transition strategic and tactical plan for operations
- Prepare and execute organizational change management (OCM) for data management
- Train and educate employees and other stakeholders
- Get feedback
- Deliver and operate data management strategy with feedback
Following these steps will help ensure the success of your data management strategy for the organization. The strategic plan is followed by plans, projects, and then operational implementation. The strategy supports operations, and operations should support the strategy of the organization’s data management initiatives.
Data Management Strategy Benefits
The benefits of a data management strategy are many. Data is a critical asset within all organizations as a capability for decision support and as a resource for all organizational activities. The organization’s data as an asset strategy is synonymous with a data management strategy. Assets are all capabilities and resources for an organization, and data is critical in each category.
Some benefits of having a data strategy:
- Overall better decision-making across the organization.
- A better understanding of organizational strengths, weaknesses, opportunities, and strengths in all business areas.
- Reduce Bad data – Inconsistent, incompatible, duplicate, missing, etc., data for decision support across the organization.
- Improved value chains between teams, projects, and practices in the organization with organizational data.
- Better contribution to business outcomes, IT outcomes, and all supporting outcomes focused on the organization and its customers: overall productivity and higher performance of the business.
- Cost management and efficiency. Enablement of the business to spend its budget better on needed capabilities and resources to support customer outcomes.
- Better data governance, compliance, and master data management strategy. Critical data is managed better.
- Improvement in running the business, innovation of the business, customer fixes, and wishes.
These benefits can overall increase business value to the business and its customers. All data management strategies and projects should focus on benefits and value to the organization.
Data Management Strategy Capabilities
To support the data management strategy, the organization should be aware of critical needs. Some of the essential requirements and capabilities for a data management strategy are:
- Data management platform supports ease of use, technology integrations, performance needs, availability needs, capacity needs, and security needs. Price supports value for the organization.
- People with specialized capabilities, including leadership of a Chief Data Officer. Organization change management initiative for people training, awareness, and compliance to data management policies, practices, and processes. The overall organizational culture and tolerance for data management have to be addressed.
- Reliable partners to support all needs relative to organizational gaps for success with data management strategy and initiatives.
- Effective, efficient practices, processes, procedures, and work instructions to support people’s behavior. These include governance, risk, and compliance needs for the organization.
Within the data management platform, there should also be a metadata strategy. Metadata is data that describes or gives information about other data. Deciding this strategy can help with organization and usage of data. Decisions on how metadata would be used, governance, structured, and traced should be made as a strategy to support effective long-term usage of data.
Organizations should also review and adopt best practices for data management. The Data Management Framework (DAMA-DMBOK2) is a comprehensive body of data management practices and standards for data governance. This framework can help create an overall data management strategy framework.
The DAMA-DMBOK2 reference data strategy practice includes the following information on 11 functions of data management:
- Data governance
- Data modeling and design
- Data storage and operations
- Data security
- Data integration and interoperability
- Document and content management
- Reference and master data
- Data warehousing and Business Intelligence
- Metadata
- Data quality
- Data architecture
Data governance leads the other ten functional areas. Data governance helps define the strategy for the execution of data management as an organizational capability. Data governance includes people or governs, standards, policies, and a plan. Data governance target operating model overalls underpins the functional areas of the DAMA practice.
Potential Challenges
There can be many challenges relative to having and executing a data management strategy. Some of the biggest challenges are:
- Alignment with business needs is not understood.
- Weak strategy and goal setting – The strategy has to support business outcomes and be followed by data management mission, goals, and objectives for success, including measurements.
- Organizational change – People adoption is key to success, and if people don’t follow the strategy, including plans, procedures, and work instructions, the strategy will not work. Training is essential, and organizations should formally do this and not expect that their people are “smart” and will just pick up all the needed skills.
- Lack of resources – The organization’s budget has to support strategy, and the organization has to enable all resources and capabilities.
- Ineffective communication – Communication plans have to be created and executed. People have to clearly understand what the strategy and plans are.
- Lack of follow-through – Metric has to be reviewed, and continuous improvement plans must be implemented. Metrics have to be attainable. Have to be able to track progress and roadblocks.
Data management strategies, when executed effectively, will outweigh the challenges. There are always challenges with any business initiative, but staying focused on the outcomes and the overall benefits is the key.
Conclusion
Data management strategies help with data governance, metadata management, data quality, data integration, data security, and many other data challenges and issues. Data management has to be a strategic capability of all organizations today. With digital transformation initiatives, machine learning, artificial intelligence, and other emerging technologies and practices, mastery of organization data is a must. Data has to be controlled strategically and not as an afterthought in all the engagements with customers and technologies that we use today.
