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The Truth Finds A Way

One trend that continues to gain momentum in the BI world is self service business intelligence, and it has IT groups concerned that the whole focus of a BI team is to champion a single version of the truth. MicroStrategy just released it’s desktop application for free, and version 10.6 is now available. If you have not taken a look at it, it is worth spending some time on. Tableau has built virtually it’s entire business model on self service BI. Any department that can’t get enough resources from the BI team can go and build their own dashboards now. Qlik is the same way. Now Microsoft’s Power BI has stepped into the ring with a growing offering. Alteryx, Sisense, Birst, Zoho – all of these are pouring resources into self service BI. IT groups are running scared, and maybe rightly so. Self service BI doesn’t have to have any training, any experience, any skill sets, any data governance, any single vision of what the single version of the truth is supposed to look like. Power to the people. Democratizing data like never before. Gartner is even saying that this is going to be the death of BI.

ms-excel

Or is it? People have always gone outside of the BI or the IT teams to build reports and analysis. It’s called Excel, or Access, and its been around for, oh I don’t know, a few decades? I’ve seen entire departments run from Excel and Access applications. You can’t stop them from using these. I’ve seen desktop computers that used to belong to an enterprising employee 10 years ago, that built an Access application that became mission critical to a department, and the department has seen the employees turnover two or three times in that period. Nobody remembers who the original developers were, or even what it was running, but each newly appointed department head got the instructions to make sure that desktop computer remained powered on under their desk and hooked up to the network. God help them is this computer dies or something. And these scenarios are a nightmare for IT groups that get handed to them to support – but you cannot stop it. Much like that Jeff Goldblum line from Jurassic Park – Life finds a way.

 

jp93-eggs1These new tools just give the enterprising users a new means to create things that BI teams or IT groups are going to have to support. They are given tasks to run the business, and then the BI group doesn’t have the time or means to provide them the reports or analysis they need to meet those new milestones that leadership keeps placing on them. And, nobody likes a whiner, so they invent what they need outside of the process. Business is happy. Users are happy. IT is blissfully unaware. All they know is that they stopped emailing them asking for a status on their request and didn’t even miss the emails. Is this really such a bad thing? I mean, if the BI team built every single thing the business thought it needs to run then they would collapse under their own weight. Rather than cringing at these outlaw scenarios, if you look at them as a proof of concept exercise, and let the POCs that live life past a year or some determined amount of time that proves the reasoning and the needs were real, then really everyone who does these are helping out the BI team.

 

Rather than seeing these activities as competition or amorphous growth that cannot be supported, BI teams should looking to guiding these rogues in a way that helps keep some sanity to possibly taking over the project when it has matured. Choosing one self service BI tool and embracing it, training on it, training others on it, would be a much better alternative than leaving it up to the department and needing expertise on 5 or 6 different tools de jour. For one, it enables you to hire or train a resource for the BI team to assist users in developing their own projects and for taking over projects that have grown to big to be a department only project. Secondly, it provides some consistency to the rogue POCs so that the company looks like it knows what it is doing. Third, taking the lead to facilitate this movement means BI and IT can guide the company down the path to some degree, rather than being handed who knows what to support. Don’t fight the tidal wave. Grab it, embrace it, lead it. Sticking your head in the sand is never a good strategy.

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Agreement Allows Vortisieze Technology to Resell TimeXtender’s Data Warehouse Automation

AARHUS, Denmark & BENTONVILLE, United States – September 2016 – TimeXtender, the world’s leading provider of data warehouse automation (DWA) software for Microsoft® SQL Server®, today announced a reseller partnership with Vortisieze. This agreement allows Vortisieze to offer the combined data warehouse and business intelligence package to their clients.

 

timextender-small-logoTimeXtender’s successful track record in helping companies with data warehouse and business intelligence has spread across the globe, and TimeXtender’s partnership with Vortisieze strengthens TimeXtender’s leadership in the Data Warehouse Automation (DWA). With more than 2,600 customers and various partners worldwide. TimeXtender’s Data Warehousing Automation platform simplifies the data warehouse process and minimizes the time spent on turning complex data into valuable information.

“Vortisieze has a great reputation for helping customers make better business decisions to compete in the marketplace,” said Heine Krog Iversen, CEO, TimeXtender. “By partnering with TimeXtender, this mission can be accomplished even faster and easier than ever before. The pairing of our TX DWA with Qlik will help their customers have access to corporate data, thereby realizing the benefits of the Discovery Hub in days rather than months.”

The partnership provides current and future Vortisieze customers an avenue to democratize access to corporate data, enabling business users and liberating IT.  TimeXtender’s TX DWA puts user-friendly data in the hands of the right people at the right time.  It protects the data in a secured and governed fashion while displaying the data in a stunning, analytical visual presentation, helping organizations reduce the gap between business and IT, and to realize the numerous benefits of a modern data infrastructure. This integration package also allows business users to independently and easily make changes and upgrades through drag-and-drop functions and without IT support.

 “Vortisieze has been helping companies build and deploy data management systems for many years,” said Cary Hague, Vortisieze. “This new alliance with TimeXtender now affords us the opportunity to help companies build and deploy the Data Discovery Hub.  We look forward to helping companies streamline their data warehouse and business intelligence systems, while reducing costs and strengthening operational efficiency.”

For sales or partnership information regarding this announcement, contact Kelsey Smith TimeXtender partnership manager, at ksmith@timextender.com.

About TimeXtender

TimeXtender has headquarters in Denmark and the U.S. The company has more than 2,600 customers across six continents using its data warehouse automation (DWA) platform, making it the world’s leading DWA solution provider for the Microsoft® SQL Server®. The company democratizes access to corporate data, enabling business users and liberating IT. It sells its products direct and through its global network of channel partners and is a Qlik Technology Partner. TimeXtender helps companies, from any vertical industry, rapidly deploy and automate their data warehouse solutions on a Microsoft SQL Server. It is fully compatible with Visual Studio and all associated Microsoft SQL Server tools and has a strategic role in helping customers save deployment and maintenance time and costs, while offering end-users a more robust and easier-to-use data warehouse and business intelligence system.

About Vortisieze

There is only one company in the world that is combining category sales data with customer sentiment data, weather data, US Census data, and third party marketing sources using state-of-the-art Hadoop “Big Data” technology and then layering the most advanced analytics platform on top for the most powerful, insights driven category reporting platform on the market.

We have over 50 years of experience in data warehouse modeling, data integration, category reporting, predictive analytics, MicroStrategy architecture, and Hadoop big data in Northwest Arkansas (Bentonville, Rogers, Springdale, Fayetteville). We build solutions that help you squeeze every penny of margin using data that nobody has tried to leverage before on a platform nobody is using for this. We think this is pretty exciting!

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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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What’s next for #CPG #CategoryManagement?

What do Walmart, Facebook, Yahoo, Twitter, IBM, Google, EBay, Teradata, LinkedIn, Hulu, The New York Times, MicroStrategy, and P&G have in common? They are all harnessing the power of Hadoop to store, serve, slice, and rationalize Big Data to advance their business. To peer into volumes of data like never before. Volumes of data that have been too big to be this nimble and uncovering things about their customers that they never dreamed possible – until today.

The biggest downside to standard data warehousing and BI tools today is that you have to know the questions you want to ask ahead of time. This creates a never ending search for patterns, outliers, and relationships in your data. If you dream up a question your existing architecture doesn’t support, you have to involve IT or software vendors and re-architect the whole data warehouse.

What if you could gaze into a magic 8-ball and it would tell you everything you needed to know about your retail category – all of the SKU changes to maximize sales,  your out-of-stocks and phantom inventory, your sales by geography or store traits, plus patterns in your data that you did not even know to look for. Welcome to the next generation of BI data warehousing in retail category management – Hadoop!

Why Hadoop?

  • Hadoop is powering today’s Big Data initiatives and is gaining more and more acceptance across many different business units. Coupled with Hive, Pig, Scoop, MapReduce, and numerous others, there are multiple robust ways to attack and slice your data.
  • Your original data formats are unchanged, so you can reuse them in their raw form at a later date. This guarantees no data loss in case you think of some way to explore your data in the future that you have not thought of today. It also does not lock you into a proprietary third party data format.
  • No ETL is required. Data is loaded into the HDFS and then you are done. Then use coupled tools to go unearth the data you are looking for rather than churning it into a cookie cutter format that you hope will give you insights.
  • Hadoop is scalable using inexpensive hardware. Add nodes to your cluster all day long, using junker PCs you have lying around in the closet. No longer do you need a $50K RAID SAN to house and protect your data. Running out of space after 5 years of category data? Just load up some more nodes and you will be good for another few years.
  • Hadoop couples with several analytics vendors – MicroStrategy, Pentaho, Zoomdata, SSRS, Tableau, SAS, with other open source products as well as numerous several built-in packages.

We are breaking new ground focusing on implementing Retaillink or other Demand Signal data in a Hadoop cluster, and applying several analytics packages on top of that to let this new Big Data platform shine in the category management space like never before.

#CPG

#CategoryManagement