Data Analysis: The Key to Good Loss Prevention and a Great Career

Technology is amazing. It has changed our personal lives, the way we do business, and is changing the loss prevention profession in every way. We are surrounded by CPUs and memory chips. As a result of this melding of mankind and microprocessors, we have become exquisitely skilled at hoarding data. We collect every bit of data possible and store it for later access. Most of us don’t know exactly where it goes or even why we are saving it, but we just do. Two even greater mysteries are how to get the data back and how to turn it into valuable information to benefit your enterprise and your career goals. This is the essence of data analytics.

Data analytics (DA) in business is the scientific analysis of raw data to create information that will allow us to draw assumptions and logical conclusions about what that raw data represents and how it can improve profitability.

The Digital Ceiling

As a retail loss prevention professional, you are likely very familiar with the data-hoarding tendencies of your enterprise. This is why you must be highly knowledgeable of data and DA. This knowledge allows you to provide the highest level of performance to your employer and the greatest opportunity to promote your career and improve the quality of your life. The days of just being able to resolve theft cases are quickly coming to an end. You must act now to become technically savvy regarding databases and basic computer architecture. Failure to act, and very soon, will cause your career to come to a screeching halt when you reach the “digital ceiling.” I refer to it as the digital ceiling because it is the threshold in any enterprise where you just simply need to know computers and data to be productive at that level. You will forever bump your head on the digital ceiling until you make the effort to gain the tools and knowledge to smash it into the billions of bits it is made of.

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Learning Data Analysis

There is a misconception that you need to have a doctorate in computer science to understand computer architecture and DA. Nothing is further from the truth. Databases are quite simple when you grasp the basics of relational database design and how to make the individual bits of data maintain their proper relation to other individual bits of data. It really is as simple as picking up a few books, downloading a free database program, and playing around with the simple and easy-to-learn code known as structured query language (SQL).

The web is loaded with SQL information and tutorials. W3Schools.com is highly recommended for its SQL tutorial. It is a straightforward learning tool that is free and in line with SQL coding best practices. If you like the more romantic approach to learning, pick up a book. Any SQL book will do if you’re just trying to get a basic understanding of relational databases. SQL for Dummies is a great starting place. It is a simple read with reduced technical jargon. Move on to more advanced texts if you catch the coding bug. I highly recommend that you download an open-source free distribution of PostGreSQL, so you can explore making a variety of tables and understand how data is organized and linked between them.

Developing Questions of Data and Answering Them with Information

Data analysis is all about answering questions. To properly develop the questions, you must first identify your enterprise’s opportunities to increase profitability. Second, you need to identify the specific data needed to answer these questions. Finally, you must determine the tools and resources you can use to turn that data into actionable information to resolve the opportunities.

Develop the Questions. Increase in profitability is always the goal of any enterprise. Opportunities reside in breaking up organized retail crime (ORC) rings that are having a heavy impact on shrinkage and item availability across your business. Improving safety protocols to reduce internal and external injury claims would avoid payouts. Upgrading software to improve workforce efficiency and productivity would result in reductions to labor cost centers. How do you resolve these opportunities? The specific questions can be limitless, but the answers are almost always contained in your data.

Find the Data. Once you have determined your questions, you need to find the data to answer them. Look at every level of your enterprise and understand all of the data being collected and how it can possibly morph into information that can answer your questions. You may find that the human resources department has data that can help with your efficiency and workforce productivity. There is also information in logistics and point-of-sale databases that could help. You must look at a variety of sources to gather sufficient data to answer your questions.

When collecting your data, you must be proactive and diplomatic at the same time. Snooping around in databases often is met with resistance, for good reason. Information security, proprietary secrets, HIPAA, and a whole host of other factors may create a discussion as to why you may want access to the data. Make sure you have clearly defined goals, ensure that you have proper oversight while examining, and be patient but politely persistent.

Find the Tools. Now that you have formed the questions and identified your data, you need to aggregate the data in a methodical way and turn it into actionable information. This is the most difficult part of the DA process. We have all sat at a desk trying to make the link between transaction data, inventory data, suspect data, tips, and video images to resolve an ORC group or an internal case. And we have all conducted analysis of cycle counts and physical inventory with multiple databases and applications open on our laptops to try and find out why our numbers are off. There is software out there to help aggregate and analyze data to answer your questions.

A great data analysis software platform should be able to:

  • Instantly access and view all relevant databases through one interface.
  • Graphically create relationships between data and build a case for the answers to your questions.
  • Create multiple graphs of the information for better understanding.
  • Accept and analyze social media and hashtagging.
  • Use facial recognition of stored photo and video images as well as run actively against live CCTV feed.
  • Be flexible in allowing specific modification to LP needs but also to any need in your enterprise.
  • Not change or alter your data in any way.

All of these points are key to getting the capital expenditure toward DA software. You can prove value in purchasing DA software by showing that it can accommodate any department in the enterprise, not just LP. And the fact that software only looks at data and does not affect the data in any way will give you greater access and buy-in from IT and other stakeholders.

The Coming Data Revolution

Are you aware that we are in the very first stages of the next great technological boom in history? The next great advancement is the significant increase in data storage available to us. It will make data analysis more difficult but at the same time more important than ever.

3D X Point (pronounced 3D cross point) memory is here. It was released in July 2015 by IM Flash, an Intel and Micron joint corporate venture. It is the first new nonvolatile memory revolution in twenty-five years. It is a completely new memory architecture that eliminates resistors and uses columns to store data. It is 1,000 times faster than current NAND memory, and ten times the density and durability of NAND. In short you can store ten times the data for ten times as long and access it 1,000 times faster than you ever could before. Processors will now be able to access their full potential enabling technology that could only be imagined until now. You must stay ahead of the curve, and you must prepare for this. 3D X Point technology will create an environment where the requirement to have DA skills and the ability to access the massive stores of data will reach critical mass. It is here, it is amazing, and it will change your role as a loss prevention professional dramatically.

Clearly, data analysis is a critical skill needed by all loss prevention professionals to stay viable in the technology-driven marketplace. Enhancing your knowledge of data, information, and technology will not only create more value for your employer, but also give you a competitive advantage when looking to advance your career.

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