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Does Big Data Really Mean Better Decisions? – By Jay Jesse

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Author: Jay Jesse – President and CEO at Intelligent Software Solutions

Does Big Data Really Mean Better Decisions? Yes, But Only if You Keep These Three Questions Front-of-Mind

A mass of data for data’s sake is nobody’s end goal — organizations are investing in Big Data initiatives because they want to drive better decisions in terms of resource usage and measurable outcomes. Gartner predicted that investment in Big Data could hit $242 billion by next year.

In the private sector, this could mean analysis of customer buying trends to pinpoint the best time and place to make a newly designed offer. In the government sector, it could mean integrating several disparate data sources to allocate public safety resources to more quickly nip a criminal or terrorist threat in the bud.

Much has been said — correctly, in my view — about the danger of adopting big-bore technological solutions that don’t conform to your organization’s mission and strategy. At the end of the day, the technology must support where you want to go, not be an expensive distraction that molds your processes to its requirements.

When we start to look at the promises of Big Data and the rapid evolution of tools and practices that yield amazing actionable insight, we must first look at why we’re doing it. How will this initiative support our strategy? What areas of improvement are we targeting? While there is an argument for the “happy accident” — data analysis that points us in new directions and opportunities — every tool and process should clearly align with our strategies and missions.

Once we have the “why” answered, there are three more granular questions that, if plausibly answered, could make the difference between a Big Data initiative that drives results and one that yields only increasing cost, confusion and organization-wide frustration.

How can we get our data connected?

Your Big Data mission enabler is connecting, integrating and staging the data. This step is critical. The “gold” of potential insight is there, but it’s spread across a wild variety of databases, file formats and maybe even physical records. Until it’s all connected, you don’t have a viable Big Data initiative.

Your Big Data partner should have a clear and compelling plan for integrating all those data sources: structured and unstructured, files and documents, databases and even live feeds. Your Big Data partner or vendor should also have a powerful answer in the form of background functions that will ultimately help your analysts make sense of the data and drive better leadership decisions from it.

This “connector architecture,” while it will be invisible to your analysts, sets the stage for success or failure. The result will be data that is normalized, segmented and staged for fruitful analysis.

Can we optimize data for the results our users need?

Once everything’s connected there is a further art to how well your staff can query it. Every enterprise will want their users enabled to search, tag, sort and visualize data to support their unique mission. One size does not fit all!

For this stage, you should expect to have a previously unusable mass of data ready to be pushed into its own search engine that is optimized for your processes and mission. You can’t expect defense sector analysts to be searching for the same things, in the same way, that analysts for a big insurance company would. Optimized search means a shorter path from your analysts’ keyboards through terabytes of data to the answers you’re looking for.

Again, data for data’s sake is no good to anybody. Optimization for unique roles and missions — whether private or public sector — is a key enabler to the fruits of big data. Over time, your solution should “learn” as the user draws a tighter bead on the data he or she wants — constantly improving search results and the ability to act on them.

Can we find the threats and opportunities we missed before? 

You’ve connected your data and refined it for the search relative to your organization’s goals. Now comes the payoff: Seeing the “gold” we set out to find and presenting it to key decision makers. Your refined searching now yields the ability to isolate previously hidden opportunities and threats, then roll up and present the data as graphs and tables of your vital findings. 

Imagine a law enforcement agency that must respond to an upsurge of gang activity in a large metropolitan area. The variety and amount of data involved could be mind-boggling: criminal records and databases of every description, reports, camera, RSS and other media feeds. Hoping to find and see a relationship between gangs and established criminal activity, analysts embark on a geospatial-enabled search that lets them see not just what is happening, but where. 

Because the Big Data system is automatically tuned by its users’ specific types of queries, this sprawling data helps analysts focus on the factors they need, driving department responses: They will have a better idea, for example, of which gang members may be “key nodes” in recent activity and put them under surveillance or bring them in for questioning. Map patterns can reveal where and when patrols can be increased. It’s all about getting to what’s relevant from what was previously overwhelming.

Big Data is a just a buzzword if it’s not answering questions unique to your organization’s challenges and driving measurable change. Otherwise, you’re out to sea. If you’re trying to get more out of your company’s data, keeping these questions front-of-mind will mean more gain, less pain and better alignment with your organization’s strategy.

Which main area/question is your company struggling with answering the most?
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