9 Questions to Ask Before Kicking off any Big Data Project Posted on : May 22 - 2015

What do you get when you combine rebranded analytics systems, a minefield of consultants turned “big data experts,” and insanely expensive “big data servers” that look suspiciously similar to commodity machines?

You get: the most complicated space for any business or decision maker to navigate.

Frankly, as one of the providers of what we feel are real big data solutions, it’s getting a little annoying. So if you are a decision maker, here are 9 questions to help you navigate your decision:

1. Do you have an actual business objective you are trying to optimize for?

What is your specific business goal? Big data projects aren’t magic and shouldn’t be viewed as such. To avoid an exercise in futility you need to narrow down your desired outcome.

For example: “We want to increase our conversion rate for first time visitors,” or “we want to maximize revenue for the children clothing category,” or “we need to find out how to reduce our loyalty member churn.” You do NOT need a consultant to answer this question for you.

2. Have you identified which data sources are valuable after you have determined your business objectives?

It is important to identify which data sources you will pull from to achieve your desired goal. You can’t assume “the more the better." The term "garbage in and garbage out" is the perfect way to describe how effective machine learning models can be at predicting an outcome.

3. Have you and your team had the internal debate about hosting data on an elastic compute platform or on dedicated hardware for various analysis?

Have you decided if you are comfortable moving your data off-premise and into a cloud? Many new software systems are hosted on elastic compute platforms like AWS, Azure, Softlayer, Google Compute, etc. Security is critical but there are ways to securely move your data into an elastic compute platform (save that for another post). Your big data strategy and cloud strategy have to be discussed in parallel.

4. If you already have a business intelligent tool, are you able to answer the following questions?

What is the true ROI of the BI and analytics tools each year relative to what you are spending? Internal engagement is not enough ROI.

How well are the users in your organization using these tools? Do they feel satisfied with their usage?

Are there available open source plug-ins so that you don’t have to purchase from separate new vendors?

It is also important to weed out the glorified analytics companies that are incorrectly calling themselves big data solutions.  View more