What is Cluster Analysis? And Why Use It?

2015-09-14 15_01_17-K-Means Clustering - Store. MicroStrategy 9

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.

2015-09-15 09_53_10-K-Means Clustering - Store - Original. MicroStrategy 9

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.

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Retailers to increase IoT spend fourfold by 2020 to US$2.5bn | #BigData #TechnologyTrendsInRetail

Anticipating the winds of change, major retailers are expected to increase their investment in internet of things (IoT) technology fourfold to US$2.5bn by 2020.

In the commercial space, IoT in retail is one of the clearest examples where connected network technology could have a significant impact on customer interaction in stores.

In particular, retailers have already begun investing in IoT hardware, including Bluetooth beacons and radio frequency ID (RFID) tags that allow them to send information to them in-store and keep a tab on stock and price levels, respectively.
Read more . . .

How will your DSR handle all that new, differently structured data? (Hint – it won’t!)
Contact us today to discover how Vortisieze analytics can bring this, and many more, types of data to your analytics package.

Source: Retailers to increase IoT spend fourfold by 2020 to US$2.5bn

#BigData
#CPG
#CPGMarketing

#TechnologyInRetailIndustry

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New #BusinessIntelligence skills in, BI developers out

Consultant Lee Feinberg says organizations need to build up their data storytelling and visualization capabilities — and details a competition for finding people with the right business intelligence skills.

You’re investing a lot of money in business intelligence tools and applications to keep up with your organization’s changing business needs — perhaps hundreds of thousands of dollars, if not more. But are you investing enough in the people who you expect to use those tools — or in the right kind of people to begin with? Probably not.
Read more . . .

Contact us today for your complimentary BI consultation.

Source: New business intelligence skills in, BI developers out

#BusinessIntelligence
#CategoryManagers
#CPG
#CPGMarketing

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Vortisieze Reduces Stress Related Deaths at #CPG Companies: #CategoryManagers Take Note

Well – ok – that’s a “wee bit of a stretch,” as my Irish grandfather used to say – but what is reported about long hours and early death is not a stretch – and it’s serious.

Two Yahoo articles published today show a direct coorelation between working long hours on the job and increased risk of stress- related early deaths from stroke, heart attacks and suicide.

In Japan, death by over work, or karoshi, is a legally recognized cause of death.

While the demands of CPG category managers and sales managers grow, there is pressure to keep staff levels at predetermined levels, sometimes without regard to the amount of work to be done.

This presents real headaches (and worse) to CPG vendors.  However, one possible solution is outsourcing some of the routine and mundane aspects of gaining insights – building reports and dashboards.

As a category or sales manager for a CPG company you are paid to gain insights from what is happening in your retailer environment.  But do you really need to know the nitty-gritty of building dashboards or reports?

Probably not.  At Vortisieze our founders have over 25 years combined experience in building analytics in the CPG category management arena.  Even if you aren’t ready for big data we can take some of the grunt work off of your desk.  We understand BI – included traditional DSR data warehouses – and especially the analytics engines.  MicroStrategy is our primary expertise but we know other tools as well.

Contact us today to discover how we can lesson your workload so you can focus on what is important – growing your brand.

 

Sources:

The 100 hour work week in Japan

Working longer hours increases stroke risk by up to 33%: study

 

#CPG

#CategoryManagers

#BusinessIntelligence

#CPGMarketing

 

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10 Steps for #CloudComputing #BusinessIntelligence Success

  1. Study Pricing and Total Cost of Ownership (TCO)
    Total cost of ownershipMany cloud analytic solutions offer per-user licensing on a monthly, quarterly or annual subscription basis. Be sure to work through the model that best fits your firm. Take into account the overall length of the project and expected growth to ensure the total cost of the project over time meets your goals, EMA asserts.
  2. Architecture and Features
    2. Architecture and FeaturesIn addition to studying current features, examine the release schedule and roadmap for your provider to better understand when new releases will be available. Some cloud analytic firms have a monthly release cadence. Others are quarterly. Depending on the architecture of your provider, you may be able to align these releases with your optimal IT schedule and business flow. Customization can be difficult in many cloud environments; be sure to investigate your specific needs and determine costs in advance. Some environments that are multi-tenant (many customers sharing a single architecture) can be restrictive with regards to custom features, EMA says.
  3. Service Management and Service Level Agreements (SLAs)
    3. Service Management and Service Level Agreements (SLAs)Determine how the provider supports new user provisioning, license management, system load balancing, and elasticity, as these are all critical components of a high performing system. Find out if the provider has assigned technical support and real-time chat support for customers, EMA recommends.
  4. Transparency and Communication
    4. Transparency and CommunicationCheck to see if the platform offers a real-time performance portal so customers are able to see system performance, uptime, outage scheduling, support issues, and scheduled maintenance, EMA suggests.
  5. Application Programming Interface (APIs)
    Robot with application programming interface sign. Technology concept. Isolated on white backgroundAPIs can ensure a cloud analytic environment doesn’t become an isolated silo. Additionally, some vendors will create an extensible environment by publishing and supporting Software Developer Kits (SDKs). If flexibility and connectivity are critical to your deployment, look for vendors who excel in this area, EMA asserts.
  6. Platform Security
    Safe cloud computing. Digitally Generated Image isolated on white backgroundLook for vendors that take the security challenge seriously. Leaders in the market are investing in their infrastructure and policies to deliver a high level of performance, EMA says.
  7. Proof of Concept (POCs)
    network_simple_commnunication12Deploy a POC before committing to a long-term cloud analytic partnership. POCs reduce risk and help ensure a smooth transition to the right vendor solution, EMA notes.
  8. Training
    8. TrainingMany SaaS solutions offer intuitive user interfaces, but training remains important. Research whether the vendor supports a community of users that you can leverage and learn from. Visit the forums and FAQ resources, research user groups, onsite seminars, and conferences designed to support you and your team post-purchase, EMA recommends.
  9. Vendor Strength
    9. Vendor StrengthResearch a company’s revenue and financial status (if the information is publicly available). For newer options, check the funding sources and venture capital positions, executive team, partner networks, and references, EMA says.
  10. Exit or Relocation
    Emergency exit sign glowing in the dark

    Emergency exit sign glowing in the dark

    Before committing to a vendor, understand the process for migrating to on-premises deployments. Some vendors are capable of serving their solution both ways, and at some point your circumstances may require moving the solution back behind your firewall. Be sure there is no impractical data transfer fees or penalties in the contract that make this process difficult, EMA concludes.

Source:  10 Steps for Cloud Business Intelligence Success

#BusinessIntelligence
#CloudComputing

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How GoPro Is Using #BigDataAnalytics in The #CloudComputing to Kick Everyone Else’s Butt

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.

Contact us today for your complimentary BI consultation.

 

Source: How GoPro is using Amazon, BMC, and Cloudera to kick everyone else’s butt

#Cloudera
#BigDataAnalytics
#BusinessIntelligence
#DataAnalyticsTechnology
#DataAndAnalytics

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27 Free Books on #DataMining

 

Part of our mission statement is to make CPG reports that strive for a higher level of insights. Consider the 8 levels of analytics below as coined from SAS. Where does your organization stand on this list?

1) Standard reports – Standard reports provide summary statistics and answer questions like “What happened?” and “When did it happen?” said Davis. “That’s analytics, but not enough.”

2) Ad hoc reports – Ad-hoc reports answer questions like, “How many? How often? Where?” he said. They provide a level of independence on desktops that allow an individual, for example, to see sales in a particular region or at a particular point in time without needing to go to an IT governance counsel and wait three months for the result.

3) Query drill-downs – Also referred to as OLAP, query drill-downs answer questions like, “Where exactly is the problem?” and “How do I find the answers?” said Davis. This is for when an organization wants to see not only the results, but what the results mean and what backs it up, he explained.

4) Alerts – Alerts answer questions like, “When should I react?” and “What actions are needed now?” said Davis. “This is when you reach a particular threshold … something changes from green to red, so you do something about it.”

5) Statistical analysis – Statistical analysis answers the questions, “Why is this happening?” and “What opportunities am I missing?” he said. “You begin to take the data … and you begin to understand why things are happening.”

6) Forecasting – A popular level, forecasting answers questions like, “What if these trends continue? How much is needed? When will it be needed?” he said.

7) Predictive modeling – Predictive modeling tells users what will happen next and how it will affect the business, Davis said.

8) Optimization – Optimization answers the questions, “How do we do things better?” and “What is the best decision for a complex problem?

 

Want to read up on how to make it to level 8? We’ve found a list of 27 free machine learning (think artificial intelligence, data mining, statistical inference, predictive modeling) books to whet your appetite.

Don’t pull a muscle! For a free consultation on your business intelligence strategy contact us.

  1. An Introduction to Statistical Learning: with Applications in R
    Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed using the R programming language.
  2. Data Science for Business: What you need to know about data mining and data-analytic thinking
    An introduction to data sciences principles and theory, explaining the necessary analytical thinking to approach these kind of problems. It discusses various data mining techniques to explore information.
  3. Modeling With Data
    This book focus some processes to solve analytical problems applied to data. In particular explains you the theory to create tools for exploring big datasets of information.
  4. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
    On this resource the reality of big data is explored, and its benefits, from the marketing point of view. It also explains how to store these kind of data and algorithms to process it, based on data mining and machine learning.
  5. Data Mining: Practical Machine Learning Tools and Techniques
    Full of real world situations where machine learning tools are applied, this is a practical book which provides you the knowledge and hability to master the whole process of machine learning.
  6. Machine Learning – Wikipedia Guide
    A great resource provided by Wikipedia assembling a lot of machine learning in a simple, yet very useful and complete guide.
  7. Data Mining and Analysis: Fundamental Concepts and Algorithms
    A great cover of the data mining exploratory algorithms and machine learning processes. These explanations are complemented by some statistical analysis.
  8. Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
    The exploration of social web data is explained on this book. Data capture from the social media apps, it’s manipulation and the final visualization tools are the focus of this resource.
  9. Probabilistic Programming & Bayesian Methods for Hackers
    A book about bayesian networks that provide capabilities to solve very complex problems. Also discusses programming implementations on the Python language.
  10. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
    A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world.
  11. Inductive Logic Programming Techniques and Applications
    An old book about inductive logic programming with great theoretical and practical information, referencing some important tools.
  12. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
    This is a conceptual book in terms of data mining and prediction from a statistical point of view. Covers many machine learning subjects too.
  13. An Introduction to Data Science
    An introductory level resource developed by a american university that presents a overview of the most important data science’s notions.
  14. Mining of Massive Datasets
    The main focus of this book is to provide the necessary tools and knowledge to manage, manipulate and consume large chunks of information into databases.
  15. A Programmer’s Guide to Data Mining
    A guide through data mining concepts in a programming point of view. It provides several hands-on problems to practice and test the subjects taught on this online book.
  16. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
    The objective of this book is to provide you lots of information  on data manipulation. It focus on the Rattle toolkit and the R language to demonstrate the implementation of these techniques.
  17. Reinforcement Learning: An introduction
    A solid approach to the reinforcement learning thematic providing solution methods. It describes also some very important case studies.
  18. Pattern Recognition and Machine Learning (Information Science and Statistics)
    This book presents you a lot of pattern recognition stuff based on the bayesian networks perspective. Many machine learning concepts are approached and exemplified.
  19. Machine Learning, Neural and Statistical Classification
    A good old book about statistical methodology, learning techniques and another important issues related to machine learning.
  20. Information Theory, Inference, and Learning Algorithms
    An interesting approach to information theory merged with the inference and learning concepts. This book taughts a lot of data mining techniques creating a bridge between it and information theory.
  21. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die [Broken Link] A great predictive analytics book providing an insight about the concept, alongside with case studies to consolidate the theory.
  22. Introduction to Machine Learning
    A simple, yet very important book, to introduce everyone to the machine learning subject.
  23. Data Mining and Business Analytics with R
    Another R based book describing all processes and implementations to explore, transform and store information. It also focus on the concept of Business Analytics.
  24. Machine Learning
    A very complete book about the machine learning subject approching several specific, and very useful techniques.
  25. Think Bayes, Bayesian Statistics Made Simple
    A Python programming language approach to the bayesian statistical methods, where these techniques are applied to solve real-world problems and simulations.
  26. Bayesian Reasoning and Machine Learning
    Another bayesian book reference, this one focusing on applying it to machine learning algorithms and processes.  It is a hands-on resource, great to absorb all the knowledge in the book.
  27. Gaussian Processes for Machine Learning
    This is a theoretical book approaching learning algortihms based on probabilistic gaussian processes. It’s about supervised learning problems, describing models and solutions related to machine learning.

 

Original Source: http://www.dataonfocus.com/21-free-data-mining-books/

 

#DataMining

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Demystifying #SelfServiceData and the Future of #BusinessIntelligence

Imagine that you wanted to see a review of the latest weekend blockbuster, but you couldn’t just Google its Rotten Tomatoes score. Instead, you had to submit a written request to an information technology department, wait five days, then sift through a binder-sized report that breaks down the reviews by publication type, readership size, and reviewer age.

As fascinating as that information might be, it took five days too long — you went to the movie days ago on a hunch it would be good.

Welcome to the world of traditional business information.  This is the beginning of a look at self-service data analytics which is the future of business intelligence.

Continue reading the article from the link below and contact us for a free consultation on how to put self-service BI into the hands of those who need it the most – your category managers and sales analysts.
Source: Demystifying Self-Service Data and the Future of Business Intelligence

 

#BusinessIntelligence
#DataAsAService
#BigData

 

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How #BusinessIntelligence Will Power The Next Industrial Age

Recent article discusses why big data puts us at an economical crossroads nationally – analogous to the crossroads that occurred between the agricultural and industrial eras.
The summary includes three actions that can be taken to get your business going in the right direction.

Steps towards the future of process
Business process leaders can take practical action now to get their digital process train on the right track:

1. Analyze your company at the process level: Review in detail your processes as they exist today (new product/service development, sales and customer relationship management, operations, etc.). Infuse a digital process plan, including the applicability of Code Halos, by re-imagining moments of customer engagement or constituent journeys. Target tangible process metrics: cost-per-claim, clinical trial yield, healthcare unit cost, fraud prevention rates, etc.

2. Perform an automation readiness assessment: Map processes to a level of detail that includes inputs, process and outputs. Scan the market for tested and ready-to-implement technologies that have established tangible proof of success. Apply intelligent process automation technologies that are minimally invasive to operating environments today, but keep your eye on the prize for where digital process transformation makes most sense tomorrow.

3. Help humans evolve toward the work of tomorrow: Start by giving employees access to digital processes and machines that help them do their jobs better, smarter and with more meaningful impact to the business. It’s not about the number of people tied to “doing the process”; it’s about outcomes and making smart people even smarter.

Whether your organization completely digitizes its business processes or takes a one-off approach, advances in foundational information technology, process automation and analytics, as well as machine intelligence, will unleash the potential for more productive and innovative ways of working. Don’t wait to get to the future of process.

Start today, by imagining how the future of work will look tomorrow when digital machines, information and processes help humans do their jobs better, faster and with greater impact.

#BusinessIntelligence
Source: Why smart hands and smart machines will power the next industrial age

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#DataAndAnalytics – Why are #BusinessIntelligence Professionals Missing the Boat

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.

Give us a call or click here to contact us.

#DataAndAnalytics

#BusinessIntelligence

#DataForAnalytics