Slideshow 5 Steps to Maximizing the Value of Your Big Data Lake

Published
  • September 01 2016, 6:30am EDT
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5 Steps to Maximizing the Value of Your Big Data Lake

Big data lakes have created a lot of change, a lot of angst and most importantly -- a lot of opportunity, according to Avi Kalderon, big data and analytics practice leader at NewVantage Partners. Here are five ways in which you can maximize the value of your data assets.

1.<TAB>Load the Data First

“Instead of modeling the data first, load the data first, then model based on the content and meaning of the data that matters the most for the decision at-hand,” Kalderon advises. “The switch adds power, saves time and enhances your ability to understand and make faster, more focused, smarter decisions. Breaking data barriers -- blending multiple sources, mining for new insights, analyzing sets and correlations and linking internal and external data environments to create new insights, new analytic views, and new business opportunities – maximizes the value in the shortest possible delivery time.”

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2.<TAB>Opportunities In Storing Everything

“Data storage is cheaper and easier to manage on commodity hardware and open source software, freeing capital for analytics and ideation geared toward new problem solving,” Kalderon says. “By not discarding or ‘filtering’ data due to performance and cost concerns, you are able to maximize the depth and breadth of your analytics and increase its accuracy. Data discovery will be easier with more accessible data, enable faster turnaround and user-friendly tools to manage the environment in a self-service model, decreasing the need for on-going IT support and maintenance. Data should be versioned, curated and tagged so that users can readily attach confidence levels to the data as they are using it.”

3.<TAB>Move the Data Warehouse to a New Neighborhood

“While data warehouses are important tenets of an organization's business operations, they are falling short in delivering an agile, exploratory ideation facility for incepting new business capabilities,” notes Kalderon. “In today’s environment, accounting for every question in one model is impossible. New data sources are emerging just too fast. New questions are sprouting up even faster. A highly engineered environment that only takes the data it needs upfront is going to have difficulty adapting to rapidly changing requirements. Augmenting the data warehouse with agile analytical exploratory environments allows a business to leverage both environments successfully while mitigating cost and risk.”

4.<TAB>Worry Less and Execute Fast

“Gone are the days where every expense needed to be justified over a five year depreciation cycle and achieve the approval of the board of directors,” Kalderon says. “Big Data is accessible within the spending limits of most departments. Think of your data lake as an enterprise platform, find a department interested in taking the journey with you and go do. Most organizations see a very quick benefit and ROI by adopting the technology which is proven to be both cost-effective and impactful. Sometime it’s ok to break the rules, this is one of these cases.”

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5.<TAB>Take the Best, Leave the Rest Behind

“Best of Breed is back,” Kalderon stresses. “Leverage your current investments while keeping an eye towards the future of data management. It’s big, it’s fast, it’s bold, but most of all it’s smart. You cannot afford to be left behind in the war over who has the most accurate and current information to run their business.”