4 Big Data Implementation Dos and Don'ts 18 Feb 2015

4 Big Data Implementation Dos and Don’ts

Today’s era of Big Data means that companies of all sizes are faced with massive amounts of both unstructured and structured data — in practical terms, big data implementation often results in difficulties for businesses relying upon traditional software and database management techniques.

What should you do — or avoid doing — to manage your data effectively? As a suggested starting point, Research Optimus presents four big data dos and don’ts to consider.

1) The Starting Point: Your Customer and Not the Data

First and foremost, remember some standard wisdom — “The Customer Is Always Right.” With regards to big data implementation, start the process by thinking of what your customers want and need and how they make buying decisions. For example, how often do customers buy your product or service and how long do potential customers spend on your website before buying?

With your customer data questions in mind, focus on what data you need to produce key insights about behavior patterns. Don’t start with a bunch of numbers and expect the data to tell you how to find or solve a problem — start with your customer rather than the data.

2) Your Measurement Scale: Analytics and Not Standard Reports

Harnessing the power of big data analytics will happen more quickly in your business if you start with an understanding of critical differences between data reporting and data analysis. Standard reports will give you information but have severe built-in limitations — the query process does not usually involve a person and is inflexible and standardized while providing only what is asked for: “data.”

On the other hand, data analytics typically involves a person and is customized and very flexible while providing what is needed: “answers.” You should insist on data analytics — don’t settle for standard reporting.

3) The Driving Force: Business and Not Technology

A proactive approach to big data implementation is much better than a reactive one. Start by anticipating needs and potential problems — give your IT staff new data management and analytics tools and then ask them to apply these tools to new types and sources of big data. Harness new technology and make it work for your business model instead of letting technology limit your business strategy,

You need a business-driven approach to data management — don’t wait for technology to force your hand by emphasizing an IT-driven approach.

4) Seek Specifics and Not Large Vague Goals

Instead of adopting a vague and larger goal that is difficult to measure, always look for a way to create a specific and smaller goal. Rather than choosing a goal of increasing online customers, adopt more specific goals — for example, establish goals and data testing involving a more limited population such as regional instead of national. Follow this with further investigation that includes data analytics (“Answers”) and not simply data reporting (“Data”).

No matter what size your company is, do start small — and don’t let “Big Data” overwhelm your business goal-setting.

What Should You Do Next?

Paying attention to big data best practices will help keep you out of trouble with big data dos and don’ts — Research Optimus can guide you along what probably seems like a steep and winding road.

Would you like to share your experiences with integrating big data into your daily business routines? Please leave a comment and share your thoughts by using social media buttons.

– Research Optimus

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