- March 18, 2014
- Posted by: Chris Surdak
- Category: Blog, Industry
Here is my closing post for the new Top Ten Myths of Big Data for HP. Enjoy!
As I prepared to wrap up this ten-part blog series, I quickly reviewed all of the prior nine posts for The New Top Ten Myths of Big Data. We covered a lot of ground over those posts, and I’m happy to see that each of those prior posts are as relevant today as they were when I started this series—if not more so.
From my interactions with literally thousands of technology, legal and business professionals; I am confident that big data is one of the most important business topics in today’s world. The points that I made in my prior nine posts demonstrate that as the big data market matures, the challenges that big data presents to organizations have been evolving. This should not come as a surprise, as billions of dollars are being invested in the technology and solutions, and more and more companies push the state of the art in how these tools are being used.
However, I wanted to end this series with an emphasis on what I believe will be the number one determinant of whether your organization can succeed with big data. This last myth could be the make-or-break for all of your efforts in this arena. Where you stand on this myth will likely determine whether your organization is successful in the future, or shuttered. Your position here will decide if you are a big data winner, or an analytics has-been.
Big Data Myth #10: You Can Get Value from Big Data without Information Governance
If you have invested any time in the big data space over the last year, you may have noticed that the topic of Information Governance (IG) is growing more and more prevalent. People have been discussing IG for decades—typically, with an extremely skeptical or even sarcastic tone. In the past, IG was consistently viewed as a “nice to have” capability, rather than a necessity.
Partially, this was because governance was viewed as a cost with little or no return on investment. It was viewed as a necessary evil that was required due to regulatory or legal requirements, rather than a sense of protecting and enhancing the business. Further, the state of the art in governance technology wasn’t very advanced beyond simple keyword search and basic workflows. As such, the market for IG languished for decades.
Languish No More
As data becomes the new capital, as information on your customers and on your internal operations becomes the primary value driver of your business, proper management of that information becomes critical. Indeed, monetizing your data is no longer optional; it’s a matter of mere survival, rather than one of competitive advantage. As such, not having effective governance of your organization’s information is not just irresponsible, it’s irrational. It would be like FedEx or UPS managing their logistics in Excel spreadsheets or a major bank managing their accounts in physical, dual-entry ledgers.
If you buy into the notion that data is your organization’s most valuable asset,—and I certainly hope that you do—then it should stand to reason that it is critical that you effectively manage this data, in order to extract maximum value from it. This then, is the new purpose of IG infrastructure: to ensure that all of your information assets are utilized to the maximum effect possible, in order to drive the best possible business outcomes.
New Rules, New Technologies, New Expectations
While there has been relatively little investment in IG technology over the last fifteen years, there has been tremendous investment in customer analytics and eDiscovery throughout this same timeframe. As a result, organizations have been able to analyze and understand millions of customers with incredible intimacy, and review and understand the content and context of trillions of emails. Capabilities that seemed like science fiction two years ago are now commonplace—even pedestrian.
Most recently, the innovations and new tools used in analytics and eDiscovery have been finding their way into IG use cases, and the impact has been profound. Capabilities such as contextual understanding, dynamic application of business rules or policies, etc. are now not only possible; they are increasingly becoming necessary to simply keep up with the accelerating growth of data. And the longer you wait to implement these tools and techniques, the harder such implementations will be and the further behind your competition you will find yourself.
A Call to Action
Because innovations from analytics and eDiscovery are seeping into IG, it is imperative that you review, revise and reengineer your existing IG policies, procedures, processes and technologies. If you haven’t revisited each of these in the last 18 months or so, you are operating at a significant disadvantage. Many organizations have not performed such a self-assessment recently, believing that IG continues to be a business cost, rather than an enabler. Those who continue to hold this view are likely to create a self-fulfilling prophesy, where the lack of a modern, powerful, flexible IG infrastructure will prevent their organizations from properly monetizing their data, and thereby becoming irrelevant.
Looking Backwards, Looking Ahead
In wrapping up The New Top Ten Myths of Big Data, I urge you to re-read the prior posts, digest their recommendations, and formulate a plan for how best to address these myths as you create your big data strategy. Our team at HP is ready to assist you in this effort, leveraging our own understanding of big data technologies, the science behind the analytics, the business challenges that you are facing, and the imperative to act with speed and decisiveness. Together, we can help you put your data to work and help you face the future with confidence
- Big Data Myth #1: Big Data is the same as other analytics, it’s just bigger
- Big Data Myth #2: Big Data is about Volume, Velocity and Variety of Data
- Big Data Myth #3: Big Data is Just Marketing Hype
- Big Data Myth #4: If You Are Doing Hadoop, You’re Doing Big Data
- Big Data Myth #5: Big Data Requires the Hiring of Programming “Rock Stars”
- Big Data Myth #6: Analysis is the Hardest Part of Big Data
- Big Data Myth #7: Big Data is About Analyzing Customers
- Big Data Myth #8: Big Data is about the “what”
- Big Data Myth #9: Big Data requires good data