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Understand the Presales Process of Big Data Consulting

November 06, 2017

Understand the Presales Process of Big Data Consulting

Big data consulting starts long before you start adding storage to the enterprise, writing analytical algorithms, or even sign the contract. In many respects, the presales process is the most important part of big data consulting since it lets you educate the client about expectations at the same time you get to assess the scope of the project.

Most big data projects fail because they put technology ahead of the business need. The customer knows they have a business problem and they are looking to big data to solve the problem, so they set out with the objective of building a big data platform without understanding why. It’s like trying to building a house and then drawing the blueprints. Without an idea of the business goal, you don’t know where to start. A CIO survey revealed that 58 percent of CIOs attributed the failure of big data initiatives to an inaccurate scope for the project. That’s why the presales aspect of big data consulting is so important; it lets you develop a business context for the project.

To be effective at big data presales you have to start at the top. IT management can’t articulate the business problem for you, but the CEO, CFO, CMO, and COO probably can. Once you establish a dialogue with senior management you will be in a better position to offer big data consulting.

Is It Really a Big Data Problem?

As part of the presales process, your first question should be to ask if the problem really requires big data.

Every big data project has to start with a specific business question. This is the heart of the big data blueprint. The business wants to learn more about customers, markets, or business processes in order to generate more revenue or save operating expenses.

Many of these business questions can be addressed using better business intelligence rather than big data. If the company is looking for answers to questions such as sales performance or the success of a product launch, chances are they already have the sales data and related information in their database; getting answers to these types of business questions is more a matter of assessing data you already have.

Big data questions are those that require assimilating different types of data to deliver new levels of insight. For example, if you want to understand how a product is selling, you can look at your sales figures, but if you want to predict how a similar product will perform in a new market, you have more variables to apply so it may be a big data question.

Apply the three V’s as a litmus test for big data: will the question be better answered by greater data volume, velocity, and variety? In other words, will you get better results from more data sets, different kinds of data sets, and data sets that are quickly changing or should be processed in real time?

Gather Executive Support

Once you have determined that you do, indeed, have a big data question, you need to get senior management buy-in. The second reason most big data projects fail is because of an inability to break down data silos and provide the necessary data access to subject matter experts.

Big data projects affect an entire organization so you want to make sure that all the stakeholders are involved before you begin. Stakeholders will come from all parts of the company, including sales, marketing, accounting, production, legal, and of course, IT. Review the project requirements with each group to make sure that all the business objectives are aligned.

One of the benefits of assembling all the stakeholders is that it breaks down barriers between departments and uncovers hidden caches of data. It also provides different perspectives on data sources that may add insight to the project.

As part of the big data consulting presales process, be sure to establish the appropriate expectations with all the stakeholders. Big data is not a magic bullet; it will not answer all their business questions. Nor is the objective to build a big data infrastructure. Make sure that all the stakeholders understand that big data projects assimilate and analyze data from different, disconnected sources to reveal new patterns and insights that improve business performance.

As with any such project, the end result depends on the quality of the initial work; defining the right question, making the right assumptions, gathering the right data sets, etc. If you set the tone and expectations of the project properly as part of the presales process, you will be miles ahead when it comes to project execution.