By Alan C. Brawn CTS, ISF-C, DSCE, DSDE, DCME, DSNE, DSSP
The unprecedented explosion of (big) data gathering means that the digital universe will reach 180 zettabytes (180 followed by 21 zeroes) by 2025. 2.5 quintillion bytes of data are produced by humans every day. The challenge is not so much the sheer amount of data… but what to do with it. Unfettered this is like drinking from a fire hose at full blast. Too much is just too much, and thus of little value. That begs the question of how to wrap our arms around big data. This is where analytics comes into play.
Types of Analytics
The work of analytics is to inform and improve decision making, but ultimately is about measuring and then quantifying ROO and ROI. With the sheer magnitude of big data available to us we can look at historical data and current data and then forecast what might happen in the future. This tells us where we have come from and where we are most likely headed. The promise of “doing it right” can result in optimized supply chains, lowered operating costs, increased revenues, and/or improved customer service and product mix to name just a few.
Analytic options abound, often varying by vertical market or application and this can be confusing. The good news is that these options can be categorized at a high level into three distinct types. Keep in mind that one type of analytics does not fit all, and in fact several may co-exist with, and complement, each other. As one expert opines, “In order for a business to have a holistic view of the market and how a company competes efficiently within that market requires a robust analytic environment”.
Three basic types of analytics:
- Descriptive Analytics tells us “What has happened?”
- This data allows us to learn from the past and understand how this might influence future outcomes.
- The past refers to a previous occurrence, one minute ago, or one year ago.
- Common examples of descriptive analytics are reports that provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory, and customer information.
- Predictive Analytics, tells us “What could happen?”
- This is based on probabilities and provides estimates about the likelihood of a future outcome, keeping in mind that no statistical algorithm can “predict” the future with 100% certainty.
- For retail sales in might be predicting what items customers will purchase or forecasting inventory levels based upon a myriad of variables.
- Prescriptive Analytics, tells us “What should we do?”
- This data suggests several possible actions and outcomes before the decisions are actually made.
- Properly gathered and applied, prescriptive analytics predicts not only what will happen, but also why it will happen. It provides actionable recommendations taking advantage of the predictions.
- This entails input from many different data sets including historical and transactional data, real-time data feeds, and yes, big data.
- Prescriptive analytics can be used to optimize production, improve scheduling and inventory to make sure the right products are delivered at the right time thus optimizing the customer experience.
- Anonymous Video Analytics (AVA)
- Anonymous is the key word. This is facial recognition software that can provide a full range of demographic information (gender, age, eye response, emotional responses, socio/economics, etc.). this allows the delivery of targeted messages as customers move through a space.
- AI-powered Analytics
- This refers to machine learning and automates (and makes more efficient and effective) the process of evaluating data from the wealth of information enterprises are constantly generating.
- AI can even predict outcomes based on historical information. It can “sort” information in a format that is most accessible and critical to the organization’s objectives.
- This can transform big data and big content into self-service data visualizations for users across the organization to increase automation, operational efficiencies and maximize revenue.
- Mobility and beacon connectivity
- Adding mobile devices to the mix enhances and expands the amount of data retrieved and track more types customer engagements.
- Wi-Fi location tracking
- Allows you to track the route of a customer through the store or employees in a building to ascertain the best location for your screens plus gather further data via mobile devices and foster person to screen interaction.
- Measures, quantifies, and substantiates ROO and ROI
- Measures how much of your missions’ statement is accomplished
- Encourages and supports smart decision making
- Provides clearer insights through data visualization
- Keeps you updated with the most current information
- Let’s you know where you have been and where you need to head
- Offers and promotes efficiency
Benefits of Analytics (messaging):
- Measures and tracks customer/employee interest from signage in-store or in office.
- Facilitates the fine tuning and targeting of a message.
- Measures response to a call to action and shows what works and does not.
- Measure peak periods to display key messages to the most viewers.
- Gives the ability to make faster, more agile message changes as varying needs are assessed.
- The data collected can be augmented with data from other information systems as needed.
We have the internet of things (IoT) with ubiquitous devices that can be connected and interconnected. We have big data increasing by the second and we have advances in analytics that help makes big data quantifiable and thus useful. As a point of reference, analytics that “touches” pro AV and digital signage applications is growing at >30% per year. This market alone is forecasted to reach > $33 Billion by 2026.
As the famous bank robber Willie Sutton said when asked why he robbed banks, he said “That is where the money is”. Increasingly our money can be traced back to all the information out there, the analytics to make is useful, and finally the content management systems to help us manage and distribute it all.