Delivering On the Promise of Big Data
The promise of using Big Data to drive business value is creating tremendous excitement within the IT industry. We are all enthusiastic about the opportunities enabled by the Big Data. But, how can we get our clients excited about it and start embarking on their Big Data initiatives?
Geoffrey Moore’s “Crossing the Chasm”
I couldn’t agree more with what Bill Schmarzo pointed out in his recent post “Crossing the Chasm with Big Data”, the best way to drive Big Data across the Big Data chasm in the organization is to focus on specific business solutions.
This is easier said than done. Developing the right technologies and the right processes to deliver Big Data analytics to business users is no small task. We’ve all been there – remember Siebel? The rise and fall of its popular CRM solution promised to provide valuable customer insights and improve sales and operational efficiency. It’s the classic example of high tech marketing hype and failure to execute.
So what did we learn?
The usefulness of Big Data is clear, but the real promise lies in the economical and efficient solutions that make it easier for the end users across the organization to access and analyze diverse data sets, unlocking the business intelligence that gives them an advantage in the marketplace.
To cross this chasm with Big Data, we must take into careful consideration how to make the technology work for the decision makers. The key criteria couldn’t be more simple, but are often overlooked by the Big Data technology marketers and vendors alike:
- Cost – Big Data projects can cost millions of dollars if the client starts making wholesale changes to the hardware, software and processes as Thor Olavsrud pointed out in CIO magazine’s How to Avoid Big Data Spending Pitfalls. Instead of investing heavily on the new technologies, business and IT leaders need to first leverage existing data and applications to make some incremental value. Whether it is to enhance current platforms with Big Data components (i.e. Hadoop, Map reduce and R) to achieve real-time analytics, or to introduce Big Data tools to the traditional BI environment to unveil predictive trends beyond structured data, this will save time and money while giving insights into the shortcomings of data as well as better ways to use it. From there, leaders can make a firm decision to build, buy, or create a hybrid solution to support the business case.
- Usability – To successfully meet the expectations of an increasingly tech-savvy user base, IT organizations have to work closely with business and get creative in developing intuitive and accessible tools for business users to test different hypotheses and validate or disprove assumptions to unveil predictive trends, rather than focusing merely on the “feed and speed” of big data challenges. The end goal is to empower a majority of the people outside the data scientists and developers community to share their analysis on a routine basis, and cultivate collective knowledge of an organization to leverage Big Data for actionable insights and measurable business results.
- Relevancy – In addition to addressing common business problems around real time data access, data aggregation and data mining challenges across information barriers due to application and functional silos, Big Data solutions need to be able to quickly zero in on the analytics that matter the most for the selective user groups. Few business users care about the terabytes of their data from Twitter, Facebook, and news feeds; but they do want to know what customer sentiment is toward their products and services, and who their most loyal, profitable customers are and how to retain their business. Give them the answers, not data!
- Demonstration – The most effective way to make true believers of Big Data technologies is to bring live practical solutions that are approachable, easy-to-use and can be seamlessly integrated with an organization’s existing business process. Unfortunately, many of today’s Big Data “success stories” are one-off projects developed by large teams of brilliant data scientists and programmers that worked together for months to produce a single result. Big Data technology will continue to face skeptics until we can count on a number of well executed demonstrations, and show that it can be as simple as a mobile application that queries nearby restaurant with real time table availability and makes reservation.
Cost + Usability + Relevancy + Demonstration! With these key considerations in mind, we, at EMC, still have a lot of work to do if we are to continue to make positive impacts with the Big Data phenomena. We need to take our conversations with customers and prospects beyond the technology towers, engage their business users and IT leaders to provide practical guidance and demonstrate cost effective Big Data applications and tools that solve real problems related to their businesses. We need to be more than just technologists in developing scalable analytics platform, but trusted advisors in evaluating the opportunities and risks, and help set the pathway for them to adopt and benefit from Big Data technologies and our proven solutions.