Big data consulting differs from any other kind of IT consulting, so it’s important to not only understand how big data works, but how to talk about the value of big data engagements. When selling big data consulting services, you have to manage the conversation to focus more on the business value derived from big data and less about the bits and bytes of big data delivery.
More executives see value in big data, but fail in implementing big data projects. An InfoChimp’s survey notes that of the CIOs surveyed, 81 percent listed big data projects as a priority but 55 percent said big data projects are never completed or fall short of objectives, and 58 percent report big data failures due to inaccurate projects scope.
Clearly, big data consulting requires having initial conversations about setting the objectives and scope of the big data problem first. Once the nature of the business problem has been clearly defined you can engage in a discussion about infrastructure and analytics.
Start with the Big Business Question
Big data consulting requires the sales engagement to be made at a much higher executive level within the organization. You are not just talking with the CIO to determine their enterprise needs; you are meeting with the CEO, CFO, CSO, COO, or CMO about strategic needs that will shape the future of the organization.
Big data consulting requires you to start with a business problem. Start by identifying the question the customer wants to use big data to answer. A big data question will have larger implications for the company, uncovering insights that will change the direction of marketing, sales, customer support, and operations.
Part of the task of big data consulting is to determine that you actually have a big data question rather than a business intelligence challenge. Business intelligence can be derived from structured data already in the corporate database; if they want product performance analytics or sales data that is structured information that can be derived using conventional database tools. If, however, the question requires input from synchronous and asynchronous data sources inside and outside the company, it is probably a big data question.
In reviewing the business question, apply big data’s three Vs:
- Volume – How much data is needed to answer the question?
- Variety – Are different types of data (database, social media, mobile, audio) required to best answer the question?
- Velocity – How fast does the data need to move? Does the answer require periodic data or real-time responses?
Gather the Stakeholders
Since big data questions tend to have a broader impact within the company, it’s important to involve the stakeholders early in the process. Big data projects tend to yield insight that will point to an action that will redefine how the company operates in some way. You want to include all parties that may be affected by the outcome of a big data project.
For example, if a company is looking to big data to identify new features for a next generation product, the outcome of the big data project will affect manufacturing, sales, marketing, finance, shipping, and other departments. Talking to them about the big data engagement and discovering how the project could affect them in advance will not only increase your chances of big data success, but it will make them feel included in the process which will make it easier to implement any changes that may result.
Assess Available Resources and Returns
As part of the big data consulting conversation you will need to determine what resources are available and what resources are needed.
Start with data resources. The big data stakeholders should be anxious to assist and assure the success of the project by making their data sources available. One of the reasons big data projects fail is because important data repositories are overlooked. You have to break down any data siloes.
Also determine how much big data expertise you have at hand. Since no one organization can effectively manage all aspects of a big data project, big data consulting is largely a matter of project management. You will need to have experts in big data enterprise architecture design as well as Hadoop and analytics.
And be sure that your big data project will yield an ongoing return. Big data projects deliver the best ROI when they can be used to automate a tactical problem, such as inventory management or product pricing; some aspect of the business that has to keep pace with the market.
If you can set expectations at the outset while aligning your big data resources you will have a much better chance at big data success. Where do you like to start your big data conversation?