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How To Successfully Implement a Big Data Project in 8 Steps

October 28th, 2013 by - 10,271 views

There are countless ways to incorporate Big Data to improve your company’s operations. But the hard truth is that there’s no one-size-fits-all approach when it comes to Big Data. Beyond understanding your infrastructure requirements, you still need to create an implementation plan to understand what each Big Data project will mean to your organization. At a minimum, that plan should include the following 8 steps.

Big-Data-Cloud

Step 1: Gain executive-level sponsorship

Big Data projects need to be proposed and fleshed out. They take time to scope, and without executive sponsorship and a dedicated project team, there’s a good chance they’ll fail.

Step 2: Augment rather than re-build

Start with your existing data warehouse. Your challenge is to identify and prioritize additional data sources and then determine the right hub-and-spoke technology. At this stage, you’ll want to get approval to evaluate a few options until you settle on the appropriate technology for your needs.

Step 3: Make value to the customer a priority

Once you’ve identified and prioritized data sources, you have to connect them to the needs and desires of your customers. For example, if a customer likes jelly donuts and is walking by a new donut store, wouldn’t it be great to push out a coupon for a free jelly donut to get that customer to come in and try out the store? Of course it would.

Step 4: Run an Agile shop and increment over time

Once you’ve established priorities and a project team, begin to work on incremental releases and incorporate new data hubs one at a time. This approach will let you adjust your operation incrementally and understand how to use data to influence actions throughout your organization. Many projects fail because they try to do too much at once. It’s okay to start slow, learn, adapt, and then move on to the next step. In fact, this is the easiest way to help your organization understand all the possibilities.

Step 5: Link customer data to company process

Each new data set presents an opportunity to change the way you deliver products and services. Push data-driven decisions into the organization at all levels—from product development through to packaging, promotion, pricing, and advertising.

Step 6: Create repeatable process and action paths

One of the hurdles to overcome when adding additional data sets is the desire to run one-off reports to answer interesting questions without connecting those answers to actions. Big Data shouldn’t mean data paralysis. Take a thoughtful approach to incorporating data sets. Ask team members what can be gained by adding the data set and what actions should be taken from the learnings. It’s crucial to clear a path for execution within the organization to prevent the data learnings from becoming just another interesting factoid devoid of connection to the customer or the product.

Step 7: Test, measure, and learn

With each data set, test your assumptions. For example, responsive marketing systems should let you push personalized marketing out the door quickly with a variety of messages. If you’re using Big Data appropriately, you can determine instantly which ads are performing, allowing you to optimize them
on-the-fly.

Step 8: Map data to the customer’s life cycle

Once you’ve been successful with a project or two, you can begin to get more creative and map Big Data needs to each stage of the customer life cycle by asking questions like these: When a customer is discovering a product or service, where are they getting their information? How do they discover new products? Can you connect that activity to your promotional activities?

Taking your company through the above 8 steps should help your Big Data project stay on track and help you understand how each project will impact your business.

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Kole Hicks

Senior Director of Product Management at GoGrid
Kole Hicks is the Senior Director of Product Management for GoGrid, the leader in Open Data Services (ODS) and committed to delivering purpose-built, non-opinionated Big Data solutions and services for the management and integration of open source, commercial, and proprietary technologies across multiple platforms..

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