One such methodology is the Big Data Business Model Maturity Index (BDBM), developed by the so-called “Dean of Big Data,” Bill Schmarzo. Bill explains how his index works over at the EMC blog, and has also created an engaging video, which condenses the theory behind BDBM into a neatly packaged 7-minute animation. We’ve embedded the video later on in this post, but it might help to take a step back and take a look at what big data is all about.
Do a Google search for “big data” and the first thing you’ll notice is that you’ll get about 852,000,000 results in 0.47 seconds. Yeah, big data, is kind of hot right now. The second thing you will come up with is dozens of different definitions of exactly what “big data” really is. The one that I like the most and that I think fully encapsulates what’s going on today comes from Gartner:
“High-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
So big data is about a lot more than just large quantities of information. In layman’s terms, it’s a load of information, coming at you from all directions, in a never-ending, unrelentingly fast stream. Dealing with it effectively requires innovative solutions, and getting the analysis right will help you to make the best decisions to take your business forward.
The BDBM Index is designed to do all of the above, by helping organizations to understand where they are on the road to big data proficiency, highlighting the key challenges to progress, as well as the business drivers that will keep them moving forward. The key focus of the index, according to its designer, is not on the technology, but on the business model. The index identifies five main stages on the road to harnessing the full power of big data. Let’s take a look.
Business monitoring. According to Schmarzo, business monitoring is the stage that the vast majority of businesses are at right now on their big data journey. Competence is being established in traditional data warehousing and business intelligence methods, and key data processes are being mapped out. The three hazards of volume, variety, and velocity are, however, making it a very hard environment to work in and get reliable results out of.
Business Insights. In order to get to the business insights stage, businesses need to move from viewing data as a cost to be borne, to thinking of it as an asset to be harnessed. Totally makes sense, doesn’t it? The estimated 20 times reduction in the traditional data storage cost model, brought about by the availability of innovative data handling and storage methods, needs to be harnessed in order to achieve that aim. The company should be moving towards real time analysis of all data, whether internal or external, structured or unstructured, to reveal “significant, actionable, material insights.”
Business Optimization. At the business optimization stage, a business should be able to convert the insights they’ve gained into practical recommendations that business users can act upon. A continuous loop of fine-tuning, driven by analytics and not humans, will allow for more business process decisions to be handled automatically, based on the data analysis.
Data Monetization. The data monetization phase allows for the packaging of the insights gained in order to create monetization opportunities. For example, these could be by way of the production of innovative products, sales of insights such as consumer buying patterns to partners, and/or also by refining the customer experience to maximize revenues.
Business Metamorphosis. The promised land for big data that Schmarzo describes is the business metamorphosis stage, and one that will ultimately “Transform the entire business model by leveraging new insight to provide a new ecosystem upon which partners and developers can create new solutions that that benefit partners and customers alike.” He is not too clear about the detail of how that is actually accomplished, but it looks like a laudable ambition.
Here’s the video that I promised that’ll fill you in on a lot of the detail about the BDBM Index.
The index gives us interesting insights into the road ahead for big data, and also, hopefully, removes a bit of the fear. That said, like any journey, the data path is often littered with obstacles and challenges, and those plotting a route for big data certainly face quite a few. Writing back in 2013 at Dun & Bradstreet, big data and cloud expert Lynn Langit highlighted four major pitfalls to watch out for with any big data project.
Mess. Disorganized and error strewn data storage systems, both in the cloud and in-house.
False Hope. Ignoring quality of data until the end of the process. Relying too much on new technologies too make sense of what might be unsound data.
Letting IT Run a Project. IT pros, don’t get mad and storm off. Langit’s experience has been that projects sponsored by other business analysts and supported by the IT group are likely to be the most successful. Yet again a reason for collaboration within an organization and a move away from our inherent tendencies to silo operationally.
There is definitely a common theme running through Langit’s article, one that revolves around the quality and the accuracy of data. Any data system and strategy will only be as good as the data quality allows, and the IT acronym GIGO (garbage in – garbage out) definitely applies when it comes to harnessing big data. I recently wrote about an Experian report that suggested that, while almost all organizations have a strategy in place to manage data quality, the vast majority of them are plagued by inaccuracies. The report found these were largely caused by human error, as well as duplication caused by poor communication between internal departments. (You can find a link to the article and the report in the “other resources” listed below.)
Any big data strategy built on inaccurate information is bound to have flimsy foundations. However good their overall big data strategy might be, until organizations move beyond simply being aware of the data accuracy problem, and instead treat it as a major roadblock that needs to be dealt with head on, they are unlikely to move far along the Schmarzo road to “business metamorphosis.”
Without question, big data is playing and is going to continue to play a big role in the future for our businesses and organizations – no matter what their size. What’s your take on the challenges big data poses and how are you dealing with that internally? I’d love to hear more.
Additional Resources on This Topic: