Why would you want to use cluster analysis on your retail sales data? Well, cluster analysis helps you identify non-independence in your data. Here is an example to help illustrate the point. Lets say we want to ask loads of teachers from many different schools what they think of their principal. If you ask two different teachers from two different schools, you will get two completely different answers that will be independent. But, if you ask two teachers from the same school, the answers will not be completely independent and could be very similar – but not EXACTLY the same. Now if your job was to take the raw data and try to predict which school each teacher came from based on their answer – then you have an application of clustering.
The same thing can be applied to Walmart store performance for a supplier. You have some data points for a store like how long that store has been open, how many competitors it has located in its vicinity, what was your products sales performance for that store, some demographics for that area like unemployment and population, possibly even some historic weather data. Now you use a clustering algorithm to group your stores that are most closely related. This could be the first step in identifying under performing stores and why. It could give you a viable store list for a product test based on more than sales performance. It might help you further identify your product identity and who your actual customers are using enough demographic data. You might not find anything you didn’t already know. The important thing is that you are diving into your data to truly understand it on a level you never have before, and uncovering one of these nuggets could be millions of dollars difference to your company.
Once you’ve built your base analysis, and in our case we built our report that you see above, turned it into an in-memory cube, and then built a MicroStrategy dashboard on top of it – we can then explore slicing and dicing our data along the different data points to help identify if any of the metrics in our analysis are a key contributor to a cluster alignment. This way we can determine what factor affects sales the most. Could it be store age? or store square footage? or unemployment? Ethnic breakdown? What of these are driving markdowns?
The great thing about using this analysis as a MicroStrategy dashboard is that it is pretty easy to tweak to look for your top performing stores, and refreshing the data source is very easy. In fact, this report could be automated each week and emailed to you. There might even be an application to look for cluster changes and have something like that generate an alert so you only need to be bothered if anything changes.
Contact us today to discover how Vortisieze analytics can help you explore your own data science.
We run around talking about how important analytics is and yet there are few really compelling examples of how well it is working. Part of this is because the vast majority of implementations are still in process and haven’t gotten to value yet, part is because they were done wrong and value wasn’t found, and part because firms don’t like sharing with competitors how they are kicking those competitor’s butts.
GoPro, however, is the perfect example of how analytics are being used competitively to out-execute much larger companies like Sony.
So starts the article on TechSpective.net published on August 3, 2015. This article is a must read on how to use big data and analytics to out maneuver your competitor. The challenge is adapting the technology to brick and mortar retail.
Interestingly GoPro implemented the same strategy that we at Vortisieze execute every day. Data in the cloud, strong big data technology and a top-notch analytics engine. Like GoPro, Vortisieze partners with Cloudera. To round it out though, Vortisieze maintains its own data cloud and leverages MicroStrategy (et al.) for the analytics engine.
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As the amount of digital information generated by businesses and organizations continues to grow exponentially, a challenge –or as some have put it, a crisis–has developed.
There just aren’t enough people with the required skills to analyze and interpret this information–transforming it from raw numerical (or other) data into actionable insights – the ultimate aim of any Big Data-driven initiative.
One survey recently carried out by researchers at Gartner IT +0.93% found that more than half of the business leaders they queried felt their ability to carry out analytics was restricted by the difficulty in finding the right talent.
Overcoming this problem is a challenge that all companies will have to face, and market leaders–aware that they have more to lose than many by falling behind in the race to keep up with technology–have come up with some innovative solutions.
Walmart decided to apply one of the fundamental weapons in the Big Data arsenal–crowdsourcing–to the problem, with positive results.
Last year, they turned to crowdsourced analytics competition platform Kaggle. At Kaggle, an army of “armchair data scientists” apply their skills to analytical problems submitted by companies, with the designer of the best solution being rewarded – sometimes financially, in this case with a job.
D&A is rising up the agenda – for executives, for business managers, for customers and, increasingly, for equity markets and investors. And now, more than ever before, organizations are starting to sharpen their focus on driving value from their D&A investments. And as organizations move along the journey from data to insights and from insights to value, key themes are emerging.
Go to the article (link below) to read a couple of good reports regarding data & analytics.
A recent online article in the data & analytics arena talks about business intelligence professionals who describe their job as 90% cleaning data.
While this isn’t a big deal to you as category manager or sales analyst working for a CPG supplier to a large retailer – it affects you every day in how your data for analysis is delivered and presented to you.
“Reformatting, cleansing and consolidating large volumes of data from multiple sources can be overwhelming,” Yaniv Mor, CEO and co-founder of Xplenty, said. “BI professionals are still struggling with the best approach to shorten the time between integration and analytics. As a result, businesses are often slow to unlock their data’s true potential for revenue or operational improvements.”
This process is called ETL (for Extract, Transform, and Load) and while slight majority of BI performs this locally, 49% are using cloud services for this.
You may be asking “so what?”
The so-what is when choosing a BI, or data-as-a-service, provider you may want to consider one that uses technology that 1) delivers your insights on-time, 2) is designed for rapid ETL with disparate data, and 3) gives you control over what you want to see.
One such technology is using big data (Hadoop) in the cloud, which is exactly what we at Vortisieze use to deliver the right insights at the right time.
Nearly every organization today uses analytics. But not every organization is getting as much out of its analytics as it could. So, how do you truly excel with analytics to deliver the best support for decisions?
Don’t fail to plan: Doesn’t sound like a secret at all does it? Well, too feworganizations have spent the time to begin with the end in mind. The most successful companies always begin their analytics projects with a clear vision of what is the target. The key stakeholders should be aligned by writing down and sharing:
What you’re trying to achieve
Who you’re trying to reach
Why it matters
How you’ll measure success
Use your analytics tool to uncover data quality issues: Don’t let the desire for perfect data be the barrier to very good data. Instead, use your analytics tools to spot abnormalities in your data and learn from them. Then, work with the people who own the data and share your insights; thus helping them fix their processes. By forming partnerships, you can significantly improve your data quality over time.
Use Good Design: Most of your data consumers visualize data to understand it, so aesthetics play an important role. Like an interior decorator, a good designer can help you develop an intuitive and effective user experience and a great look and feel for dashboards and visualizations. However, data visualization best practices always outrank aesthetic design – every time.
Repetition, repetition & repetition – learn through play & through doing: Your worst data model is your first one – nobody creates a perfect model for their data on the first try. And that’s OK. Truth is, looking at your data from different angles can teach you a lot about it. Let everyone connect with the data in their own ways — you’ll be amazed at what they discover. Use what they do to inform your strategy (back to #1).
Be your loudest evangelist! Some software projects are mandatory for users, however, adoption of analytics is voluntary in most organizations. So, if you want people to know you have built a better mouse trap, act like Guy Kawasaki and start promoting it. Recruit your marketing department and sell the value of analytics throughout your entire team and organization.
You need a champion:Find an influential person or team that has an unmet need and empower them with analytics. This can turn them into true believers by showing them what’s possible. Then turn the spotlight on their success to prove the value of analytics to the rest of your business.
Build a Cross Functional Team: Selling analytics is simple when it becomes easy to repeat successes and avoid failures. Bring together a cross-functional team and put them in charge of:
Deciding the role of analytics
Defining the standards and tools
Identifying best practices and gaps
Iterating and improving the solution over time
Have dual processes: Changing the method of measuring KPIs or profits requires taking your time and getting it right. However, sometimes you have a unique and urgent situation and must develop an app right now to analyze it. Put in place different processes for both scenarios — and accept the fact that it’s OK to build temporary throw-away apps for one‑off projects.
Reports are so 90’s: Don’t be like most BI deployments that tend to focus on delivering the same out-of-date reports that has been around for decades. Simply describing the situation presented in the data does not provide analytic value for decision makers. You must answer the ‘why?’, not just the ‘what?’. So, shift your efforts to emphasize diagnostic discovery and exploration capabilities.
What is your data worth? Are you sitting on the proverbial goldmine with your information? Would outside organizations (internal or external to your company) pay good money to gain access to your proprietary data? Or, as some large retailers do, can you use it to add value for your customers or vendors? Take a step back and see the forest – think creatively about all the ways you could monetize the data you already own.