The hype around big data has crescendoed to the levels of SOA in the early 2000′s, cloud in the late 2000′s, and social in the past few years. Unfortunately the hype is creating three main pitfalls:
- A morass of confused definitions. In fact a quick survey of any educated audience, yields a multitude of definitions. Some folks see big data as large data sets and data warehouses, others see big data as code for analytics and BI. Many see the output of big data as infographics or the hardware behind the support of big data. The V’s of big data continue to expand from volume, velocity, and variety to include veracity, viscosity, and virality. Some folks even have 16 V’s in their definitions.
- Solutions confusion among buyers. A technology vendor land grab for mind share with big data is happening now the same way everyone adopted cloud. Hardware vendors now enable big data. Storage providers now deliver big data solutions. Integration vendors provide plumbing and intelligent connections for big data. Analytical vendors now all support big data. Some folks like to confuse Hadoop with big data. Everyone has a solution, just not the solution a buyer thinks they need. Confused capabilities continue to proliferate amidst a lack of good customer references. Customers feel the chaos.
- Discussion on technology options not business problems. The discussion about big data has evolved into a technology conversation not a business value or transformation conversation. Clients immediately talk about products and technologies without defining the problem to be solved. Technology investments take over the discussions on solution development.
Recommendations: Focus On the Questions To Ask, Not The Answers.
Despite this chaos, organizations can escape the pitfalls of big data confusion. Business leaders should focus the discussion back to business value. In the case of big data, the big question is what is the question? Start each project by asking the following:
- What are the questions that need to be asked?
- What are the answers that help us move from data to decisions?
- Can we shift insight into action?
- How do we tie information to business process?
- Who needs what information at what right time?
- How often should this information be updated, delivered, and shared?
The Bottom Line: Big Data Is About Making Decisions In The Future Not Rehashing The Past
The history in moving from data to decisions is littered with failed technologies. The failure of data warehouses to provide real-time data led to the creation of data marts. Data marts failed to provide complete and updated and comprehensive views. The world moved to business intelligence to access insight yet this still did not address the issues. A movement to master data management attempted to address the lack of a central repository. Information governance emerged as a key people and process issue. Users over the past 20 years saw the complete cycle repeat itself.
After spending hundreds of millions in time and money, existing solutions still don’t solve the problem. Why? The market and business environment have changed. Data moves from structured to unstructured. Sources exponentially proliferate. Data quality is paramount. Real-time creates too much information. It’s not about real time being fast enough. Real-time is irrelevant because speed does not trump fidelity. Quantity does not trump quality. Context is key as we need to shift from real-time to right time data based on roles, processes, location, time, and relationships.
Yet all this is still irrelevant. Organizations must address the key issue – how can an organization or leader make the right decision? Business questions remained unanswered despite the massive number of reports and views and charts. Organizations must make the shift to asking the right questions instead of seeking the right answers. The big shift is about moving from data to decisions. This is transformational in thinking and not easy to achieve. However, this is the journey ahead and big data is one small part.
What business problem will require you to start with Big Data? What are the key outcomes? Where do you expect to move the needle? Add your comments to the blog or send us a comment at R (at) SoftwareInsider (dot) org or R (at) ConstellationRG (dot) com
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