Sears Holdings is an American corporation that owns the
retail store brands Sears and Kmart. Several
years ago Sears Holdings was struggling to put together personalized promotions
in a timely manner. Due to the large
volume and fragmented nature of their data it was taking Sears up to 8 weeks to
create a personalized promotion package for a customer. To address this Sears turned to Hadoop, a big
data technology. Sears started using
Hadoop to store their incoming data and also integrated their existing data
warehouses into Hadoop. This enabled
Sears to bypass the time consuming step of integrating data from different
sources. The result: the time taken to
create a personalized promotion package dropped from 8 weeks to 1 week.
New South Wales State Emergency Services (NSWSES) is the
organisation responsible for providing emergency services such as natural and
man-made disaster relief in the state of New South Wales in Australia. The agency is responsible for an area of 800 000
square kilometres and relies on a core staff of 280 supplemented by over 9 000
volunteers. NSWSES also works closely
with the meteorology service and the state fire department. Given that time is critical in saving lives
in disaster situations NSWSES felt that in order to perform their role more
effectively they would need to integrate and analyse data from multiple
sources, such as the meteorology service and social media, and also be able to
share information with other services and the public in real time. Using a combination of SAP and Web 2.0
technologies NSWSES create a platform that enables the agency to communicate
and collaborate with external parties as well as manage internal resources much
more efficiently. The agency has been
able to derive several tangible benefits from their big data
implementation. For example, real-time
monitoring of disaster related information enables management to quickly
identify where personnel are most needed.
Another benefit that the big data solution offers is predictive
analytics. Predictive analytics helps
the agency to anticipate natural disasters before they happen enabling response
plans to be activated in advance.
These are just three examples of big data at work in
organisations. There are many more
stories out there. What is important to
note is the different industries that these organisations are in. This highlights how big data analytics can be
applied in many different industries. As
long as you collect and analyse data in your organisation there is a possible need
that big data can fulfil so don’t immediately dismiss big data because you
think you cannot make use of it.
Hi Ken.. I agree, the prospects for big data within organisations is immense. And I think most companies are keen to “implement big data” but just don’t know how to. At least these studies provide real-life examples of how organisations can go about implementing big data. The reality is that any and every organisation is involved in one or more form of data collection, storage and analysis…(this is part of modern business really) and I think companies need to dedicate effort in exploring the technologies available, I think its one of things where the one organisation is perhaps waiting to see what the other company is doing.. but organisations don’t realise that they don’t have to look outside, but look inside… they are already sitting on a wealth of data that needs to be unearthed… I like what Dickey Barbecue Pit is doing…. There is always a need for this, but the willingness from the organisation itself needs to be there too..
ReplyDeleteI think the reason why organizations may be intimidated to implement big data is because of the magnitude of such a project. Such an initiative would require a large financial backing and the correct resources to make it successful. A lot of people in general don't know what big data entails and when they do find out, just as you mentioned Mpho, they don't know where to start. I think it is only a matter of time before concepts of implementing big data on smaller scales becomes a trend. I feel as though there is this "all or nothing" approach with the concept where there shouldn't be. The implementation of big data should be accessible to even smaller organizations. I believe that this could be done if the market were to provide software which were to be smaller, easier to implement and not require sophisticated skills from scarce resources. Big data software needs to develop to a point of sophistication whereby most IS professionals can use it to benefit their organizations instead of a select few resources who possess the skill and know-how of doing so. If this were to be done, many barriers of its implemetation within organizations would dissapear.
DeleteI think what would help is a change in mindset. People musn't look at big data as just a project which ends once the systems are in place. Rather it should be looked at as an on going journey. Start off slow, make mistakes, find your way and learn as you go. If you incorporate good knowledge management practices you will eventually get a good understanding of big data as an organisation. Didi's point about scarce skills in this space is important. The conventional wisdom is that one person should possess all the skills to leverage big data. However there is another school of thought which says that instead of employing these data scientists that are hard to find and even harder to afford you can just have a team of people that together have the requisite skills. That is another option to address the lack of skills.
DeleteBig data has been used extensively in the banks to proactively detect and prevent online fraud, particularly in the space of internet banking where we use behavioural patterns to identify and model risk patterns that inform the bank when a user behaviour is out of the norm. Through the use of big data, business rules have been developed which has heldped reduce fraud numbers in the banks. I believe the effective use of big data can and will advance a seamnless attempt to combat online fraud.
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