Enterprise Big Data strategy refers to a company’s comprehensive vision when it comes to utilizing data-related and data-dependent resources and capabilities. Organizations that gather or generate loads of data cannot escape the need to come up with a sensible systematic plan of action to harness and better handle Big Data.
A study on the implications of big data published in the journal Procedia Computer Science highlights the practical applications and benefits of collecting and analyzing vast amounts of information. “Big Data analytics offers a multitude of opportunities to enhance business value and productivity. One of the main applications of Big Data analytics is for business intelligence to improve decision-making capabilities, faster decision making, understanding of customer needs, developing strategies for launching new products and services, exploring new markets, improving inventory turnovers, reducing customer complaints, and enhancing staff productivity and efficiency,” the study notes.
How do companies achieve these benefits? How can they come up with an effective strategy to take advantage of Big Data?
Big Data strategy framework
Enterprise Big Data Framework, a certification program developed by the Big Data Framework Alliance to establish a vendor-neutral standard for big data analytics, prescribes a five-step process for defining a big data strategy. Here’s a summary of the process.
Identification of business objectives
For the Big Data strategy to work, its use has to be based on what an organization aims to achieve and how it works to accomplish its goals. “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,” according to the Enterprise Big Data Framework.
Examples of business objectives in which Big Data can be applicable are as follows:
- Prediction of customer behavior
- Prediction of sales
- Analysis of data from devices such as POS systems and factory sensors
- Prediction of financial risks
- Detection of fraudulent activities or records
- Analysis of social network comments, customer engagement, and online marketing metrics
- Search for correlations across multiple disparate data sources
Assessment of an organization’s current state
The next step is about evaluating the state of a company’s data assets, processes, sources, as well as IT resources, capabilities, and policies. This is crucial to formulate the right strategy in line with what a company strives to be in the future and what it is at present.
This assessment is usually undertaken by having a series of consultations or interviews with the relevant employees including those involved in customer targeting and retention, marketing, and IT functions.
In the case of customer outreach, for example, it would be necessary to come up with ways to achieve a 360-degree view of current and prospective customers. This is possible by reviewing business processes, data assets and architecture, data collection capabilities, as well as organizational policies that impact customer attraction and retention.
The same goes for optimizing data security in an enterprise. There has to be a thorough and comprehensive examination of existing data processes, assets, infrastructure, and policies to come up with the right strategy.
Determination and prioritization of use cases
After examining business objectives and the current state of the organization, it becomes easier to identify and prioritize use cases for Big Data analytics. Does the company collect and store expansive amounts of data that can aid marketing, sales, inventory, or other related efforts? How can these be useful? What methods or tactics will leverage these data to help achieve business objectives?
For example, if the specific business objective being targeted is the expansion to new markets, Big Data analytics sees applicability in the compilation, processing, analysis, and reporting of customer behavior in the target market, profitability of the products being considered, costs, price competitiveness, and the most appropriate promotional strategies. Data on risks also have to be taken into account.
Creation of a Big Data roadmap
The formulation of the Big Data roadmap is “the most intense and contentious phase,” according to the Enterprise Big Data Framework. It is largely based on the business objectives identified, This roadmap provides the outline by which projects and activities are executed and how funding, resources, or technologies will be allocated.
The roadmap, according to the framework, “should focus on identifying gaps in data architecture, technology and tools, processes and of course people.” It indicates a specific duration or timeline for the use of Big Data resources and information.
Integration of the framework through change management
Lastly, it is essential to embed the Big Data strategy with the existing setup of a company. This is usually undertaken through effective change management. A change manager may be selected to carefully embed the Big Data framework into the existing policies and plans of a company.
Significant cultural, technological, and business process changes may happen, so it is important to find ways to make the integration seamless and non-disruptive. It may be necessary to offer incentives to encourage everyone to embrace the change and cooperate towards the successful utilization of Big Data to facilitate the achievement of business objectives.
Change management and marketing of the strategy
A TechTarget article by Editor-at-Large Craig Stedman, which features Global Data Strategy Managing Director Donna Burbank, yields a wealth of insights on developing an enterprise data strategy. These insights supplement the key points outlined by the Enterprise Big Data Framework.
Stedman lists 10 steps for developing an enterprise data strategy. Three of which stand out for emphasizing points that may not be that clear in the framework above.
- Develop a plan to manage cultural changes – Just like the common advice for the digital transformation process, it has to be a people-driven matter. Determine what works best for the people in an organization and do not fixate on the latest trends and technologies.
- Start with the strategy, not technology – The Big Data use cases and roadmap should be determined with a focus on sensible processes and desired outcomes, not the technology the management or employees want to use. It is preferable to decide on the technologies to use only after the methods and sound plans of action are established.
- Market and sell the data strategy in your organization – “Evangelism and outreach are crucial both in getting approval for a data strategy and then getting it adopted by departments and business units across the organization,” Stedman declares. The Big Data strategy cannot work if the people who will be using it do not know, understand, and appreciate its importance and benefits. Promoting the strategy can be done through newsletters, webinars, and even casual learning sessions.
Measurement of progress and success
Once a Big Data strategy is implemented, it is crucial to closely track its progress and measure its success. Doing this ensures that the strategy proceeds according to expectations. If there are issues, they can be corrected or tweaked as soon as possible.
Key performance indicators to watch out for include the following:
- Stats on data errors identified and fixed
- Accuracy and error rates
- Data completeness and timeliness
- The business impact of data quality problems
- Data quality feedback from end-users.
To develop an effective enterprise big data strategy, it helps to follow a framework that comprehensively addresses the core concerns of the business in relation to building such a strategy. To make sure that it works, it will be critical to carefully manage the changes and actively promote the strategy to everyone in the organization. It helps to have advocates such as executive sponsors and business figures who explicitly promote the implementation of the strategy. Finally, there should be a periodic evaluation of the progress of implementing the strategy and the assessment of its success or failure.