Future of lp VIEWPOINT ACADEMIC
What Is Big Data?
By Tom Meehan, CFI Meehan is the corporate manager of data, systems, and central investigations with Bloomingdale’s where he is responsible for physical security, internal investigations, LP systems, and data analytics. Meehan specializes in new technology deployments, business intelligence, industrial intelligence, and systems implementation and design. Meehan brings nineteen years of expertise in retail LP, information technology, and process improvement, the last eleven years with Bloomingdale’s and eight years prior to that with Home Depot. Meehan is also chair of the LPRC’s future of LP working group and co-chair of the fraud working group. He can be reached at tom.meehan@bloomingdales.com.
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ig data is a very large amount of complex data that can be structured, semi-structured, and unstructured. This can be a collection of traditional data sources, digital data sources from both inside and outside of your organization, social media, or public records, just to name a few. The reality is big data could be all of your data and any data that is available. The data generally is owned by several different departments within your organization. Big data is a bit of a buzzword and in some cases isn’t that big at all. It really depends on what your organization’s big data strategy is and where the loss prevention department fits in. It’s really not about the amount of data; it’s what you plan to do with it. Big data can help develop predictive models for risk, shrink reduction, fraud prevention, or refund management; provide process improvement information to the field; help identify dishonest activity; and provide better overall insights into the business. Possibilities at times can feel limitless and often overwhelming.
data and are attempting to find patterns or specific aberrations. Another example would be descriptive analytics related to audits or shrink results where you take data, create a summary, and visualize the material. Big data on the other hand will involve multiple data sources (structured, semi-structured, and unstructured) shared from multiple business partners both inside and outside of the organization. At times this will include other non-traditional data sources such as social media, Google search results, news, weather, and public record data. An example of big data analytics would be looking into omni-channel data to help identify process-improvement opportunities. You can be looking at what happens to the shipment when it leaves the store to go to a customer, the impact on the store inventory, ship speed, customer contact, defect rate, non-deliveries, weather, staffing, customer settlement, and fraud risk. Looking at all of the data sets from multiple sources will assist you in developing a prescriptive analytic. Prescriptive analytics takes traditional descriptive and predictive analytics and automatically synthesizes the big data. It makes predictions and then can generally make several recommendations for options to take advantage of those predictions. So in essence, it’s taking that descriptive analysis, which still counts for the majority of business analytics today, and answers the questions of what happened, why it happened, and how it can improve. It also looks for reasons for past failures and successes and then provides options, in addition to showing the implications of those options. All of this information helps improve the customer experience and allows process improvement and shrink reductions. Now that you have better understand of the difference between descriptive or traditional analytics and big data analytics, let’s move onto a plan.
Big data is a bit of a buzzword and in some cases isn’t that big at all. It really depends on what your organization’s big data strategy is and where the loss prevention department fits in. It’s really not about the amount of data; it’s what you plan to do with it. In my last article, we talked about social media monitoring. We’ll talk about this a little bit, but this article is about what you can do with all of the data available. I’ll be defining some of the big data terms and talking about strategies and plans to handle this wealth of information.
Planning Your Big Data Strategy
Here are some key points to keep in mind when planning your big data strategy. One important thing to remember about big data is it’s not just about loss prevention because it requires a lot of IT and other department support, including marketing, operations, finance, omni-channel, and other business stakeholders. It has to be about the organization’s profitability protection, early warning or risk, and process improvement. This isn’t your traditional “let’s stop the shoplifter” or “catch the
Descriptive and Predictive Analytics versus Prescriptive Analytics
The first step is to understand the difference between big data and traditional data analytics. Data analytics generally consist of smaller data sets from fewer sources with structured or normalized data. In the LP world, exception-based reporting would be a type of traditional analytics where you have structured or normalized
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January–February 2016
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