Sunday, February 9, 2014

Three 'Big Rules' of Big Data

The Big Rules of Big Data is not for Limiting the Imagination, but for Framing the Right Questions. 

As Big Data continues to gather momentum everyone wants to catch the wave. "It's a game-changer!", however, very few organizations have achieved the expectation from the investment. Besides avoiding the big pitfalls of Big Data, what’re the ‘Big Rules” of Big Data to follow in order to improve the project's success?

Big Data Rule 1 - Frame the right questions. Start with the business problem you are trying to solve. Who is your customer, what do they want or need, and how are you going to monetize it? Then figure out if you 'need' a big data solution. Big Data allows businesses to see customers’ behavior pattern they have never seen before, it also uncovers the interdependence and connections that will lead to a new way of doing business. The greatest rewards go to those with a clear vision for how it can transform their organization, capabilities, and industry. According to the industry survey, more than 90% of Fortune 500 companies will have at least one big data initiative, the effective use of data can deliver substantial-top and bottom-line benefits, Building business capabilities through it will not only improve performance in traditional segments and functions but also create opportunities to expand product and service offerings and create new business models.  

Big Data Rule 2 - Data Governance - if you want high-quality information, it has to be standardized and consistent. Classification = Relevance. No matter what vendors say, the better the classification and structure of your data, the better your search and analytical capabilities will be. Even tools that help with classification require custom rules and dictionaries, and they tend to be domain-specific. If you want high-quality Big Data, you need Data Governance. Governance can be applied to content as it is collected. This strategy assumes you know what your core business taxonomies are. As you mine your content, you'll learn about other entities that need to be 'promoted' to the governance model and standard for the collection, creating a feedback loop, and improving your classification on ingesting over time. However, governance doesn't always directly deal with classification or taxonomy or categorization issues in most deployments. It is most often a structure for what you are going to do / who is going to do it / how is it going to be done / and how it going to be repeated. A corporate handshake if you would have involved between the parties

Big Data Rule 3: Take a multi-disciplinary approach. Big Data project involves engineering practice, science, and arts (This is the most interesting part when you need to be creative and find the question you want to ask). And it’s also about imagination, and having an understanding of the working principles of industry, vertical, or a problem. Then figure out a way to improve it or change the mechanism. Analytics is the analysis of historical data done by the BI tool. Big Data is the challenge faced by cross-organizational the boundary in storing, processing, & reporting of nearly 80% of the unstructured data collected in this world. From a science and engineering perspective, there is a change of emphasis, especially because of the Variety dimension. Blending data from multiple sources has always been challenging, and in the big data world, the challenge is bigger. The good news, though, is that the tools are also getting better for things like natural language processing, address standardization, fuzzy matching, MDM, and so on. But there is a world of difference between the best tools and the worst, and this is where the focus should be in terms of quality - not just on how good is the source data, but how good is the processing and merging. how good the big data team with complementary skills and expertise.  

Such ‘Big Rules’ for Big Data is not for limiting imagination or innovation, but for framing the right questions to ask, taking a systematic approach, and continue to develop the best practices by leveraging the lesson learned, in order to reap the big profit from Big Data.


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