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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