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Why Agility is the Most Important Aspect of Big Data Analysis

Big data hasn’t been easy. It’s not been easy to understand, nor to adopt, nor to implement. It hasn’t been easy proving an ROI to executives who okayed investing in it, and it hasn’t always been so readily embraced by consumers. So, what is the key — the one thing — that will mean the difference between success and a lack of success with big data? Agility.

Agility is Essential for Maturing Your Data Analytics

Break challenges into mice-sized chunks to achieve greater agility.

When data analytics is mature, it does deliver ROI. It is able to deliver high-performing customer service, and improve marketing efforts. It can streamline production and help out with future R&D projects. But mature analytics means being able to produce answers in real-time or darn near it, and most of all, being able to respond to the insight the data delivers.

What is the definition of agility? Agility is the ability to move rapidly and with grace. It means being resourceful and adaptable. In business, agility means the ability to respond quickly and decisively to change or challenge. This means being able to adapt products and services to what the customers demand.

It requires breaking down large questions or challenges into smaller, more manageable chunks so that the company can respond. In other words, you can’t be so agile with an elephant, but you can be agile with several dozen mice.

Agility Demands Agnostic Design

Agility with data also demands that you be agnostic. The most agility comes from development projects that aren’t dependent on or relegated to a single platform, device, language, etc. This is evident in the products that have been designed to utilize big data, primarily NoSQL and Hadoop. Both of these innovations are born of the need to scale the data and scale it quickly, without ‘boxing it in’ so to speak. These tools allow you to connect directly with the data and the tools used to analyze the data, without the boundaries imposed by particular formats, structures, or systems.

Experimentation is Critical for Building Agility


Is experimentation the key to achieving agility and big data success? Ask a few upstarts you may recognize, like Google and Twitter. Those guys think it is.

Finally, agility with big data is only achieved by experimentation. You have to start asking questions, and be willing to alter your questions as analysis reveals different answers than what you thought. You have to be able to dream big, but keep your experiments small enough to manage. Still, you have to be able to scale up quickly, because big data has the power to generate lots more questions than it does answers, meaning you have to constantly refine your questions, think even bigger, and embrace even more change. The change must come quickly to remain agile.

What’s the bottom line? Adopting big data isn’t like adopting a kitten. With a kitten, you can bring her home to the box your last Amazon book order came in, and it’s likely to suit her through adulthood. Big data isn’t like this. It outgrows its little box regularly and often exponentially. Be ready and willing to change when the data leads you where you didn’t intend to go. Be ready to experiment to find out where else the data might take you. Always, always keep the tools and systems as agonistic as possible. Don’t box yourself in, and you’ll be agile.

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