How to define a Big Data Strategy

In this post, we will discuss a practical approach to formulate a Big Data strategy. As discussed in one of our previous articles, a comprehensive enterprise-wide Big Data strategy can provide enterprises with a significant competitive advantage in the marketplace. A Big Data strategy, however, cannot be seen as something separate from the organizational strategy, and should be firmly embedded. When we are discussing a Big Data strategy, this effectively means a business strategy that includes Big Data.

A Big Data strategy defines and lays out a comprehensive vision across the enterprise and sets a foundation for the organization to employ data-related or data-dependent capabilities. A well-defined and comprehensive Big Data strategy makes the benefits or Big Data actionable for the organization. It sets out the steps that an organization should execute in order to become a “Data Driven Enterprise”. The Big Data strategy incorporates some guiding principles to accomplish the data-driven vision, directs the organization to select specific business goals and is the starting point for data driven planning across the enterprise.

Besides the gains of realizing a competitive advantage, enterprises require a Big Data strategy because it transcends organizational boundaries. Without a Big Data strategy, enterprises will be forced to deal with a variety of data related activities that will most likely be initiated by different business units. Various departments are likely to start up their own analytics, Business Intelligence or data management programs, without taking into account the overall long-term strategic objectives.

The driving force behind the formulation of an enterprise Big Data strategy should be the combination of either the CEO/CIO (when Big Data defines the enterprise) or the COO/CIO (when Big Data optimizes the enterprise). This recognizes that the data is not only an IT asset, but also an organization wide corporate asset.

A well-defined enterprise Big Data strategy should be actionable for the organizations. In order to achieve this, organizations can follow the following 5-step approach to formulate their Big Data strategy:

1. Define business objectives
2. Execute a current state assessment
3. Identify and prioritize Use Cases
4. Formulate a Big Data Roadmap
5. Embed through Change Management

Each of the steps to formulate a Big Data strategy is explained in further detail below:

Figure 1: Formulating a Big Data Strategy

Step 1: Define business objectives

In order to leverage Big Data in any organization, it is first necessary to fully understand the corporate business objectives of the enterprise. What makes an organization successful? Revenues and profits are often the result of meeting or exceeding business Key Performance Indicators (KPIs). Start with understanding how an organization is successful, before exploring how Big Data technologies and solution might enhance the future performance.

The Big Data strategy should align to the corporate business objectives and address key business problems, as the primary purpose of Big Data is to capture value by leveraging data. One way to accomplish this to align with the enterprise strategic planning process, as most organizations already have this process in place.

Examples of frequently occurring business objectives from a recent survey have been listed in the figure below:

Figure 2: Examples of Big Data objectives, source: IDA

In order to identify business objectives, involvement of key business stakeholders is of paramount importance. Ensure that these stakeholders are involved right from the start and provide key input on a continuous basis. Key stakeholders to consider in this first step are:

• Executive sponsors. The importance of finding and aligning with executive sponsors cannot be underestimated. Their support is essential throughout the ups and downs of formulating the Data Strategy and implementing it.
• Right talent on the team. Involving people with the right talent and skill sets is essential in determining the right business objectives. Explore both internal talent as well as external consultants.
• Potential trouble makers. Every project or initiative will have some ‘stakeholders’ who either deliberately or unintentionally are opposed to change. Knowing who they are, and their motivations upfront will help later in the process.

Step 2: Execute a current state assessment

In this step, the primary focus is to assess the current business processes, data sources, data assets, technology assets, capabilities, and policies or the enterprise. The purpose of this exercise is to help with gap analysis of existing state and the desired future state.

As an example, if the scope of the data strategy is to get a 360 degree view of customers and potential customers, the current state assessment would include any business process, data assets including architecture, capabilities (business and 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.

In this stage, it is also important to identify and nurture some 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. By involving these people, asking for their input, it becomes easier to formulate the roadmap in a later stage.

Step 3: Identify and prioritize Use Cases

In step 3, envision how predictive analytics, prescriptive analytics and ultimately cognitive  analytics (further discussed in chapter 8) can help the organization to accelerate, optimize and continuously learn, by developing Use Cases that align with the business objectives from step 1. Document each of the Use Cases to understand how Big Data can realize the business objective, as per the figure below:

Figure 3: Development of Use Cases

Well-defined Use Cases provide a clear and effective way to define how Big Data technologies and solutions can realize business goals. After the Use Cases have been developed, the next step is to prioritize all of the Use Cases based on their business impact, budget and resource requirements. By conducting this exercise, enterprises can identify which Big Data initiatives provide most business value.

One of the most effective ways to prioritize Use Cases is by using a Prioritization Matrix. Prioritization Matrix facilitates the discussion and debate between the Business and IT stakeholders in identifying the “right” Use Cases to start a Big Data initiative ― those Use Cases with both meaningful business value (from the business stakeholders’ perspectives) and reasonable feasibility of successful implementation.

The Prioritization Matrix in the figure below is an excellent management tool for driving organizational alignment and commitment around the organization’s top priority Use Cases.

Figure 3: Example of a prioritisation matrix

Step 4: Formulate a Big Data Roadmap

The next step is probably the most intense and contentious phase and without a doubt will account for majority of the time in formulating data strategy. Based on the current capability state assessment (step 2) and the identified and prioritized Big Data Use Cases (step 3), the Roadmap can be developed. The Big Data Roadmap outlines which projects (or Use Cases) will be executed first and what capabilities (knowledge, tools and data) will be increased in the next 3-5 years.

With the desired future state in mind, the Roadmap should focus on identifying gaps in data architecture, technology and tools, processes and of course people (skills, training , etc.). The current state assessment and Use Cases will present multiple strategic options for initiatives and the next task is to prioritize these options based on complexity, budget and potential benefits.

The sponsors and stakeholders will have a key role to play in prioritizing these initiatives. The end result of this phase is a roadmap to roll out the prioritized Big Data initiatives.

Step 5: Embed through Change Management

Although technically not a part of the Big Data Strategy formulation, Change Management (involving the hearts and minds of people) will have a profound impact on the success or failure of a Big Data strategy.

Change management should encompass organizational change, cultural change, technological change, and changes in business processes. Data Governance, which deals with the 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 part of any change management program.

Learn more about the Big Data Framework

This article is an excerpt out the Enterprise Big Data Professional guide. If you want to learn more about the Big Data Framework, you can download a copy of the guide for free on this page.