How-To: Successfully Implement a Big Data Project in 8 Steps

Big Data Solution
Implement a Big Data Project in 8 Steps
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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 these 8 steps.
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.
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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
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.
Note: This document is based on a blog post by Kole Hicks.
About GoGrid
GoGrid enables companies to evaluate and run multiple, on-demand big data solutions quickly, simply,
reliably, securely, and cost-effectively. As the leader in Open Data Services (ODS), GoGrid is 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. With
over 15,000 customers and over 600,000 VMs deployed, GoGrid has pioneered cloud infrastructure for
more than a decade for companies like Condé Nast, Merkle, and Preventice. For more information,
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