Take A Bite of this

"Take A Bite of This"

Levels to WebFOCUS

In my introduction post we covered how Wendy’s uses data at a store management level, by scheduling employees for different shifts and analyzing lunch rush. I wanted to talk about now how WebFOCUS might be used at a higher corporate level. Corporate executives can through WebFOCUS view anything from high level summaries that reflect all of north America or transaction reports for one particular location.  It is extremely beneficial for a restaurant to be able to view customer satisfaction and food costs all right in one place together.

The vice president of solutions at Wendy’s says.. "WebFOCUS gives management visibility into how the restaurants are operated and managed, and how they compare to other restaurants using benchmark and trend capabilities in the reports. They can analyze why one location is doing better than others, factoring in elements like geographic location, customer satisfaction, and employee training. We can apply best practices from one location to another. This raises the productivity of the whole group."

Having up to date metrics at the touch of your fingertips is essential for restaurant managers, district managers, and all other personnel. Corporate users can drill down regions into restaurants and then view specific sales for per store averages.

Wendy's data analytics team has used WebFOCUS to create function-specific portals, including a District Marketing Plan Portal for the company’s real estate department, HR Portal, and Store Attributes Portal that displays all of Wendy’s locations and attributes such as address, date of remodel, and Wi-Fi availability. Overall WebFOCUS has been a huge success for the Wendy’s team. The main takeaway here is that data is just data unless it is interpreted correctly and in a meaningful way, for corporate level and in store level to make use out of it. By presenting daily data in a simple way for everyone to understand, there is a better work flow at Wendy’s restaurant locations along with corporate level viewing.

Take a bite of THIS.. an intro

 Wendy’s chooses a Web Perspective

We have all had a Wendy’s burger, delicious hot yummy whatever you use to describe it, you’ll definitely find yourself there if you have a craving. Wendy’s like most large chain food establishments recently have shifted over and started using metrics and big data to lead to better decision making and cut costs.

Wendy’s has currently enlisted in something called WebFOCUS. This is a business intelligence platform that Wendy’s data analytics team uses to look at daily food sales metrics and even labor costs. Corporate wide, Wendy’s now uses WebFOCUS to reduce operating costs. Each individual restaurant can now gain unique insights through WebFOCUS.

“The system has rolled out to district managers, operations personnel, and executives at Wendy's headquarters. The company is in the process of expanding to all managers at its company-owned restaurants. Wendy's data analytics team has also deployed WebFOCUS for 6,000 franchise restaurant locations. Approximately 2,500 Wendy's international and domestic franchises currently use WebFOCUS dashboards, with more franchise locations voluntarily adopting the new system every month.”

So what’s all that mean? Wendy’s data analytics is changing the game quite simply. Their old systems were limited to restaurant level, now all levels of management can view food sales and food service metrics thoroughly from top to bottom. Information is refreshed at least 6 times a day in order for restaurant managers to keep close tabs on food metrics and exactly what is going on.

"They can make labor-scheduling decisions, determine if they need to prepare more food for the rest of the day, and get an overall sense of sales," says Trisha Bridger, IT development manager at Wendy's.

A great example of using this data would be analyzing a lunch rush, right after it happened. This way managers can see what types of food were purchased most frequently, and at what exact times. In the future they will know if they need more staff at a certain time, or if they need to have more foods prepared if a certain item is more popular than others. 

Drink up.. An Intro

Have you ever wondered how your favorite coffee shop uses data to know what your favorite drink is?  Well I am here to help with some of the answers, so let’s brew up some data on the largest coffee brand there is. Data is used in Starbucks just like it would be in any other food industry. It is used to collect information about customers, learn who they are, and learn who likes what type of drink. But how does Starbucks create that unique experience for each customer that keeps them returning day after day? Starbucks uses data in so many different ways. Here is a small example before digging deeper. Using consumer data, the coffee chain designed its new line of products to complement the habits it gleaned from its own stores. 

Basically, the company says it talked to its baristas about how customers ordered coffee, lattes and tea while in Starbucks locations and culled several industry reports about at-home consumption. It used that data to create K-Cups and bottled beverages to sell in grocery stores. Other than that Starbucks is using data to generate pliantly of information about their customers. Here are more examples to show all of the ways Starbucks uses big data to learn more about their customer and optimize their company.

Is Data All that Great.. an intro

It is important to challenge data and not just accept facts. If we never questioned anything then we would let errors go unnoticed.  Just like fake news we cannot just accept everything we see at surface level without digger deeper. By no means am I saying lets ignore data, it provides way too many valuable insights. The pros of big data are way larger than the possible bad data we might run into. So from here lets dig deeper into big data, and where it could go wrong.

First off it seems that data has become so pervasive in our lives, we hardly even notice it until it affects us directly. One application that has become particularly common is the use of algorithms to evaluate job performance.

"For example, she tells the story of Sarah Wysocki, a teacher who, despite being widely respected by her students, their parents and her peers, was fired because she performed poorly according to an algorithm." When an algorithm rates you poorly, you are immediately branded as an underperformer and there is rarely an opportunity to appeal those judgments. Along with that seldom find out how they were concluded. In many cases, methods are considered "proprietary" and no details are shared. Numbers are numbers of course so its hard to question it or say they are wrong.

Is Data All that Great?

"Is Data All That Great?"

Blog Post Number 7

I know I know... my whole blog I've been talking about how great data is and all of its practical uses. However, I wanted to spin things around today and play devils advocate in order to take a new perspective. Data is numbers but sometimes numbers are wrong, sometimes numbers don't tell a story, or provide reason. So lets take a look into some examples I found of how data can be doing more harm than good perhaps.

Wall Street is famous for its "quants," high paid mathematicians who build complex models to predict market movements and design trading strategies. These are really smart people who are betting millions and millions of dollars. However, sometimes their models fail. The key difference between those models and many of the those being peddled around these days is that Wall Street Traders lose money when their data model go wrong.

Something has gone seriously wrong here in the world of big data. When machines replace human judgment, we should hold them to a high standard. We should know how the data was collected, how conclusions are arrived at and whether they actually improve things. And when numbers lie, we should stop listening to them.

"Another example, imagine we're running a business that hires 100 people a year and we want to build a predictive model that would tell us what colleges we should focus our recruiting efforts on. A seemingly reasonable approach would be to examine where we've recruited people in the past and how they performed. Then we could focus recruiting from the best performing schools." On the surface, that seems to make sense, but if you take a closer look it is sometimes flawed. 

So data… take it or leave it, but its always good to question and never just assume. Data is really useful and I do believe it is a great asset to many companies. However, questioning and learning along with asking why is important when assessing and viewing data rather than just assuming.

https://www.inc.com/greg-satell/is-big-data-doing-more-harm-then-good.html