3 Proven Steps to Make Big Sense of Big Data

by Pete Schmitt, CTO, cStor

By 2020, 1.7 megabytes of data will be created every second for every person on earth.

By any measure, that number is staggering. Big data is more than big, it’s phenomenally overwhelming. For companies of all sizes, that means they continue swimming in a sea of data, struggling to process and filter what’s most relevant, and determine how to make the right data meaningful to the right people within the organization. Only then can the data truly affect positive change and measurable business outcomes.

While analytics tools (e.g. using Splunk for IT data collection) have been around now for years, turning machine data into actionable intelligence is truly the name of the game. Not only is that true for the large-scale enterprise, but it’s also true for small- and mid-sized firms who can see even greater, faster impact if data is effectively analyzed and leveraged. The good news is that an increasing number of organizations are finally making real progress.

According to a Harvard Business Review study surveying Fortune 1000 executives since 2012, for the first time a near majority – 48.4% — report that their firms are achieving measurable results from big data investments, with 80.7% of executives characterizing their big data investments as “successful.”

The same HBR survey revealed that Fortune 1000 firms are leveraging big data most successfully for the following goals:

So how are they defining success, and how exactly did they get there? Here are some things we see as common denominators in their big data initiatives:

1. Create a strategic plan.

The ‘shotgun approach’ just doesn’t cut it when it comes to making real progress with big data. Whether conducted completely in-house, or with the guidance of an expert IT consulting partner the most meaningful results come after the organization conducts a thorough analysis and maps a strategic plan.

That helps initiative leaders understand precisely what metrics are important to the business overall, what metrics are needed for each business unit and individual role, what metrics are important to more than one business area, what data needs to be archived for potential future use (e.g. regulatory and compliance), and what data simply isn’t important to store and analyze.

As you evaluate your data and information needs as a company, you’ll begin to see clear approaches for an optimized and integrated infrastructure strategy that will support the capture, consolidation, management, protection, insights and actionable outcomes needed to align with your business objectives

If you simply don’t have the right skill sets in-house to create the strategic plan, consider leveraging a big data and analytics consultant who can assess your environment, systems and processes to help identify the capabilities necessary to create a successful analytics engine unique to your firm.

The partner should bring a ‘best-of-breed’ portfolio of technologies and partnerships to your project, saving you time and money in researching the best tools for the job and giving you an unbiased view of your environment. That means you’ll be able to operationalize your data analytics results quickly and efficiently.

Once the strategic plan is in place, it’s time to let the data consolidation work begin.

2. Identify, consolidate and assess data sources.

Part of your strategic planning process should include interviewing every business unit and functional area so you can identify all data sources to learn precisely which business metrics are mission-critical versus others.

That might include helpdesk responsiveness, system availability, tracking of development projects against product launch plans, financial performance, inventory monitoring, employee turnover, etc.

A word of caution on data collection: it’s important not to guess at what metrics are most meaningful to each business area. Get it from end-users and connect each metric to a higher-level business objective. Once that’s done, identify the top 5 metrics overall, and determine the commonality between them that will deliver the most ROI to the business.

3. Operationalize the plan.

Understanding what data will impact overall business objectives most significantly before your data gets completely out of control (if it isn’t already) will help you see how those metrics should and can be measured and reported on in a way that is both meaningful and not overly complex.

That means you can focus on the shortlist of metrics that can be operationalized, monitored, adjusted and reported on with consistency and frequency over time.

In order to do that effectively, run trend analyses to see how the data can be leveraged, determine your unique needs for each business area, outline your SLA requirements, and filter out data that’s important from unnecessary data. You can run queries in tools such as Splunk that are inclusive of only the data you need, quickly filtering out everything else you don’t need. Collecting and storing data for data’s sake is not only unnecessary and time-consuming, but it can also get extremely costly from a storage perspective.

If you’re still struggling with your big data initiative, don’t get discouraged… get a plan! And if you don’t have the right resources in-house, find them from a trusted source such as a big data and analytics consultant so you’re not waiting any longer to get started (or sitting in ‘analysis paralysis’ mode).

cStor offers a free strategy consulting session to review your environment, unique needs and business goals so you can begin to uncover the best next step. If you have questions or want to speak to a consultant even before that, don’t hesitate to contact us.

Happy data mining!