A Data Strategy is a plan of action that lays out a comprehensive vision across the enterprise and sets a foundation for the company to employ data-related or data-dependent capability. The key is to make it actionable for your specific organization and industry and somewhat evolutionary to adjust to disruptive market forces. A Data Strategy should incorporate some guiding principles to accomplish the data-driven vision of the enterprise and help direct your company to select specific business goals.
Bernard Marr of Forbes in his September 2015 article ‘20 mind boggling facts everyone must read‘ stated that
“Less than 0.5% of all data is ever analyzed and used, just imagine the potential here.”
So how do you tap into this huge potential of data collection and analysis in enterprises?
Whether you belong to the camp that believes data is an asset to the organization or not, you must believe that data that your organization owns is a resource that has economic value. You expect it to provide future benefit just like any other asset. In much the same way you consider your employees as assets and you have employee-focused strategy (attracting and retaining etc.), shouldn’t you treat data the same way?
Research firm Gartner states that “Information is an under-managed, under-utilized asset because it’s not a balance sheet asset.”
We believe that a comprehensive enterprise-wide Data Strategy can give significant competitive advantage in the marketplace. So how does an enterprise get on with such a strategy if it’s not already there?
Step 1: Planning & Discovery
This step is all about identifying business objectives & needs, enlisting sponsors & stakeholders, defining scope & schedule, and discovering technology & data assets that have a role in the Data Strategy.
Identify Business Objectives and Problems that need to be solved with data
Your Data Strategy should align to business objectives and address key business problems / needs, as the primary purpose of Data Strategy is to unlock business value through? leveraging data. One way to accomplish this, is to align with corporate strategic planning process as most organizations have a strategic planning process anyway. Some examples of business objectives / business needs would be:
- Drive customer insights
- Improve product and services efficiently
- Lower business risks & costs
- Drive revenue growth and/or profitability
- Regulatory compliance.
Identify Key stakeholders, team members and sponsors
3 types of people need, at a minimum, to be involved:
- Executive sponsor(s)
- Key technical stakeholders – Explore both internal talent as well as external consultants.
- Business stakeholders – Every project / initiative will have some ‘stakeholders’ who are invested in processes which are likely to need change. Knowing who they are, and their motivations upfront will help you later in the process.
Step 2: Current state assessment
In this step, focus is primarily on current business processes, data sources, data assets, technology assets, capabilities, and policies.
The purpose is to define the gap analysis of the existing state (as-is) and the desired future state (to-be). As an example, if the scope of the data strategy is to get a 360 view of customers and potential customers, the current state assessment would include any business process, data assets including architecture, capabilities (business & IT), and departmental policies that touch customers.
Current state assessment is typically conducted with a series of interviews with employees involved in customer acquisition, retention, and processing.
One critical aspect is to enlist people in the organization that are natural data evangelists. These people truly believe in the power of data in making decisions and may already be using the data and analytics in a powerful way. These evangelists will help drive a ‘data-driven’ culture in the organization.
Step 3: Analysis Prioritization & Roadmap
This phase is probably the most intense and contentious phase and without a doubt will account for the majority of the time when formulating a data strategy. With Big Data and Cloud computing, the analysis has become even more complicated than in the past. With the desired future state in mind, analysis should focus on identifying gaps in data architecture, technology & tools, processes and of course people (skills, training etc.). Big Data brings new data sources into the mix and Cloud computing enables new options for data integration and data storage.
The gap analysis will present multiple strategic options for initiatives and the next task is to prioritize these options with business objectives / needs as the primary criteria. The sponsors and stakeholders will have a key role to play in prioritizing these initiatives. The result of this phase is a roadmap to roll out the prioritized data initiatives. Without going into too many details, some of these data initiatives could be Data Governance, Data Quality, and Master Data Management (MDM).
Step 3: Change Management
Change Management is a crucial step in formulating a Data Strategy. A lack of focus on change management has frequently debilitated the best of Data strategies.
Change management should encompass organizational change, cultural change, technology change, and changes in business processes.
Data Governance, which deals with overall management of availability, usability, integrity, and security of data becomes a crucial component of change management. Appropriate incentives and ongoing metrics should be key parts of any change management program.