Thursday, March 20, 2014

Defining the Business Objectives for Big Data Projects

Business leaders need to clearly understand the Business Objectives before embarking on a Big Data project. The first step is to define the business objectives. In a recent survey by IDC, the most popular business objectives of Big Data projects are:

  1. Customer Centric Outcomes
  2. Operational Optimization
  3. Risk Management
  4. Financial Management
  5. New Business Model
  6. HR Analytics

The objectives have to be well defined from a business perspective, else the bag data project will fail. Remember that with big data, you are searching for a needle in a hay stack - but to find that needle, you need to know what the needle looks like, behaves like and feels like.

Every business objective of any big data project  has four main parts:

1. Context of the project
2. Needs that the project is trying to meet
3. Vision of what success might look like
4. Outcome in terms of how the organization will benefit from the result

All Big Data Projects must start by defining the objectives. It could take anywhere from a few hours with one person to months or years with a large team. Even the briefest of projects will benefit from some time spent thinking up front.

Now, lets take a deeper look at these four main parts of business objectives:


Every business objective has a context. It is the defining problem that the big data project must solve, Who are the people interested in the results, what are they trying to achieve?

Contexts emerge from understanding who are interested, say for example Sales managers, or Marketing managers or Finance Managers etc., and why they are doing what they are doing?

The context sets the overall tone for the business objectives and guides the big data project. Context provides the background to the project and help define the mission. The Context provides a project with larger goals and helps to keep us on track. Contexts include larger relevant details, like deadlines, that will help us to prioritize our work.

Context comes from people, so with time new contexts emerge as new groups of  people come onboard and have different missions.

Here are few examples of contexts:

  • A marketing manager wants to know the effectiveness of the marketing in social media.
  • A sales manager wants to predict the how much he will sell next week 
  • A store manager wants to know how often customers visit a particular store.  


Correctly identifying needs is critical. By clearly explaining the needs, business objectives of Big Data projects can be well defined, planned and accomplished.  The need must be defined, perhaps a definite problem has to be well articulated.

When we correctly explain a need, we are clearly laying out what it is that could be improved by better knowledge that can be gained by the Big Data project.

Business faces challenges. And these challenges results in specific needs, That that could be fixed by intelligent data analysis. These needs should be presented in terms that are meaningful to the organization. If your method will be to build a model, the need is not to build a model. The need is to solve the problem that having the model will solve.

Big Data analytics is the application of math and computers to solve problems. Business leaders will have to determine which questions can be answered by data analysis.

Continuing on our retail example, Here are some fairly common needs:

Business leader wants to expand operations to a new location. Which one is likely to be most profitable?
Some of our customers leave our website too quickly. We don't understand who they are, where they are from, or when they leave, and we need to know how to retain them.
As a business leader, I need to choose between two marketing campaigns and choose the most effective one.


Vision is a glimpse of what the results of business objectives will look like - even before implementing a big data project. It is like a mockup, describing the intended results.

Think of vision as a model of a house - a mockup without much low level details - but just enough to give a good picture of what the finished house will look like.

A mockup is a low-detail idealization of what the final result of all the work might look like. Keep in mind that a mockup is not the actual answer we expect to arrive at. Instead, a mockup is an example of the kind of result we would expect, an illustration of the form that results might take. Whether we are designing a tool or pulling data together, concrete knowledge of what we are aiming at is incredibly valuable. Mockups, in one form or another, are the single most useful tool for creating focused, useful big data analytics to work.

Having a good vision is the part of scoping that is most dependent on experience. The ideas we will be able to come up with will mostly be variations on things that we have seen before. There is no shortcut to gaining experience, but there is a fast way to learn from your mistakes, and with big data analytics, you can try out as many of them as you can. Especially if you are just getting started and work on a smaller data set, you can visualize outcomes even before confirming it with complete analytics.

Vision can take the form of a few sentences reporting the outcome of an analysis. For example a simplified graph that illustrates a relationship between variables, or a user interface sketch that captures how people might use a tool.  The Vision primes our imagination and starts the wheels turning about what we need to assemble to meet the need.

Before you start a big data project, you need some vision of where we are going and what it might look like to achieve your goal.

Continuing again with our retail example:

Need: A retailer is trying to measure its successes of its email marketing campaign.
Vision: Present key performance indicators on a regular basis. It will consist of graphs and text.

Need: A retailer is looking for new locations to expand.
Vision: Report that shows each location and expected sales for each location.

The most useful part of creating mockups is that it lets you work backward to fill in the data of what we are actually looking for. If you are looking key performance indicators of an email campaign, then you know where to look for information and come up with metrics and valuation models. This help to get all pieces fall into place faster.


Lets assume that we have all the results, then we need to understand how this insight will help the organization and what will happen once the reports and insights are generated.

How will it be used?
How will it be integrated into the organization?
Who will own this reports/Actions?
Who will use it?
How will its success be measured?

If you don't understand the intended use of the reports that is being produced, then it will easily get lost on the corporate jungle and all the hard work will go to waste.

The purpose of the big data project must be established while defining the business objectives.

Figuring out what the right outcomes are boils down to three things.

First: Who will see the results? List of people who will use the reports/insights. The persons who will see & use the reports/insights must have the skills to interpret the results, give valuable feedback to the project team and explain any modifications to the initial business objectives (if any).

Second : Who will maintain this big data analytics system. Is this a one time activity or does the organization need repeated runs of this analytics? If there is a need for continuous runs, then who will own and maintain the system?
Who are they? What are their requirements?
What should the project do to build a repeatable & sustainable big data analytics?

Third: What will be the business outcome of this analytics? What will change with this reports? How can we verify that concerned leaders are taking  suitable action based on the analytic reports?

It is very important to think through the outcomes before embarking on a project. Therefore the outcomes must be defined into the business objectives.

A good and well defined business objective is the one which has the defined the context, Identified the right needs, and has a good vision of what the results might look like and gives the required outcomes - so that the big data analytic project reports will do something useful.

Seeing the Big Picture

Successful Big Data Analytics Projects start with a well defined business objectives. The business objective needs have a coherent narrative about what the organization might accomplish by analyzing big data, and what problem it is solving.

Once the business objectives are defined, the next step is to identify the right tools and methods to use. Just focusing on maths or software - without a a good business objective will result in wasted time and energy.

Also see:

Understanding Big Data

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