Posted in Operations & IT Articles, Total Reads: 2731
, Published on 15 July 2014
If you torture the data long enough, it will confess.-Ronald Coase, Economist
If you are not able to churn out insights from the available data, if you are not able to run that extra mile or swim a few inches deep with the data in hand, you surely don’t have that competitive advantage in the current business scenario. Analytics is all about getting computational or meaningful pattern or information from the data. In today’s world, the accumulation of ever growing data is so large and so tremendous that it can provide a whole new plethora of options for business. This accumulation of data, popularly known as Big Data, comprises of complex data sets that are generally beyond the power of an organization to gather, process and get significant understanding.
image:renjith krishnan, freedigitalphotos.net
As companies begin to exploit the options of digital business and technologies, data and analytics has become vital for a business’ success. There is an increasing amount of data being collected in huge volumes and at an increasing speed from both internal and external sources.
Big Data can be broadly categorized into three classes:-
a) Internal Data: data collection methods and processes followed by the business itself. b) Structured External Data: generally provided by third party sources, may need to tweak the data to align with the existing system. c) Unstructured External Data: taken from sources over which we have no control. However, a careful combination of unstructured and structured external data can provide powerful and concise insights.
Many types of data, which businesses never thought would be useful or ever cared to think about, are now being. Whether you are working in manufacturing, finance, retail etc., Big Data can do wonders if used in appropriate way. Businesses have the potential to uncover more through larger volume of data, without relying on the human recall accurately. The potential can be realized only if you pull together and analyse all the data. Business that augment their human analysts with Big Data tools could have a remarkable competitive edge by filter out potential problems sooner, identifying opportunities earlier and performing mass customization for all grades of people.
Big data approaches can be used to authorize analytics-based services that improve the business itself, such as in – context suggestions to customers, more accurate predictions of bankable service conveyance and even more accurate data of unforeseen failures.
Analysing data that wasn’t designed for Business Intelligence (BI) big data also lets you work with data from multiple sources (specifically known as “gray data” or “gray web”) that is not scrutinized for your specific needs, and that varies significantly in its level of detail and accuracy—and thus cannot be examined by BI systems.
These techniques crawls huge data sets of information collected for specific purposes (such as monitoring individual financial records), looking for patterns that might identify good prospects for loans and flag problem borrowers. Increasingly, they filter out external data, data which is not collected by a credit reporting agency—for example, trends in a neighbourhood’s housing values or in local merchants’ sales patterns—to provide insights into where sales opportunities could be found or where higher concentrations of problem customers are located.
The information provided by third party resources is generally equipped to use. However, this data veneered over existing internal information should yield richer meaningful information and provide concise and targeted insights.
Recommendations to future Chief Information Officers:-
Like finding the pearl in the oyster, a few enterprises are making discoveries by exploring big data. The terrain is complex and far less organized than the data managers are accustomed to. And it is growing by petabytes and zettabytes (1 zettabyte = ~109 terabytes) each year. But it is also getting easier and lot less expensive to explore and analyse, in part because software toolkits built to take advantage of cloud computing infrastructures are now available.
Don’t panic and certainly there is no need to rush. However, sooner or later, analytics may become the part and parcel of your daily decision driving mechanism. So make a little efforts to acquire mind set, skill set and tool kit for the same.
These are still early days. The prime directive for any manager is to deliver value to the business with the help of latest technology. One way to do that is to integrate new technologies in abstemiousness, with a focus on the long-term goals and outcomes they may yield. Today, leading managers pride themselves on waiting until a technology has proven value before they adopt it.
However, managers who ignore the aforementioned big data trends risk being marginalised in the CEOs, COOs and CIOs (also known as the “C-Suite”) of the organizations. As they did with earlier technologies, including traditional data warehouse / business intelligence and data mining, executives are ready to seize the big data opportunity and turn to account.
With this in mind, PricewaterhouseCoopers (in their study on “Emerging trends in IT industry”) encourages managers to take the following steps:
1. Start to add the discipline and skill set for big data to your organisations; the people for this may or may not come from existing staff.
2. Set up sandboxes (which you can rent or buy) to experiment with big data technologies.
3. Understand the open-source nature of the tools and how to manage risk.
The new analytics certainly doesn’t lack for ambition, vision, or technological innovation. The big data model was a huge step forward, but it may not be sustainable for a longer period. A company who wants to leverage on the big data analytics should fundamentally rethink how the analysis can create value for the shareholders.
I would just conclude by saying that “Utopia is a process of making a better world, Analytics is no exception for your business too”.
The article has been authored by Sourabh Paul, TAPMI
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