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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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Get A Data Scientist to Work On Your Problem For Free

According to the Consumer Goods Technology and RIS 2016 Analytics Study, 33% of companies lag behind their competitors in analytics skills and people, and 31% lag behind in analytics tools.
2016-05-17 10_27_33-2016 Retail and Consumer Goods Analytics Study.pdf - Adobe Reader

In an effort to help, we are seeking CG companies that have a real data science need or question that they would like to see solved or automated, and we would like to help for free. Whats the catch? Well, we have a short survey we want to collect data on and use to analyze the industry, specifically which industries are having the most problems, but we also want to examine the real world problems out there and see if we can help solve them. We are not collecting any personally identifiable information to sell off to anyone – we just want to chew on some data science problems with you and see if there things we can do to help. With 77% of company respondents to the Consumer Goods Technology and RIS 2016 Analytics Study stating they either planned to or did not have any investment in data science, here is a way to possibly test drive a data science team at no cost to you.

Just fill out the 18 question survey below and if you have a real question and think we can get the data to solve it – then you are in! We are not limiting the winning questions so more than one question could be selected.

2016-05-17 10_41_56-Retail Questions Needing Answers

CPG Retail Data Question Survey

If you have any questions about the survey or want to find out what we do, feel free to contact us here.

 

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Have You Tried Out Wavemaker Studio Yet?

2016-01-21 08_35_23-WaveMaker Studio

If you are thinking about cross-platform mobile app development, chances are you have noticed the sea of products out there and may have come across Wavemaker. Wavemaker studio offers the promise of a cross-platform rapid application development environment for mobile and web.

Using a drag-and-drop user interface to allow developers or non-developers a low barrier of entry in robust data driven applications, the Wavemaker platform seems like a dream come true. I’ve been watching Wavemaker for about two years now waiting for it to mature into a stable product, and last week it seemed like they might be there, so I tried out Wavemaker community edition, version 7.8.0 for about a week now, and here are my takeaways.

Documentation

My first thought is that the documentation is a little lacking. After stepping through the training videos and getting started documents, I then thought the materials were out of date. As it turns out, virtually all of the documents were for the cloud version 8.x – not the community version 7.8.0. Coming across a few issues, I dug deeper into their wiki, and the forum posts that seemed to pertain directly to the issues I was having did seem to be based on an older version, but it is virtually impossible to differentiate. No version is ever mentioned anywhere on what the articles, documentation, or videos pertained to. This made it frustrating as I am trying to get up to speed and working through bugs or a lack of understanding. With such a huge gap between versions and no indication of what the documents were tied to, it seemed like I was wading through an incomprehensible sea of mishmash except at the highest level, like how to import a database.

Cloud Versus Community

At first it would seem that these two versions of the product were closely related. As I played around with both, it is apparent they are not. I 2016-01-21 08_54_11-Pricing _ WaveMakerdo understand that one is open source and one is a pay for product, but perhaps my expectations were set too high that the community edition would actually be usable. I came across a pretty glaring bug in version 7.8.0 that prohibits you from creating a database lookup using another table and a foreign key. For an IDE that is supposed to be there to create database driven apps, this is a major flaw – ultimately one that caused me to drop the product from review. If you cannot build a simple data model and use master tables as a lookup for another detail table, then whats the point? I’m not talking about anything really complex – three tables, that’s it. I never got a chance to add anything else. The cloud version does seem to have worked out the issues, but they don’t really have a pricing tier for somebody that is trying to prototype products to sell in a startup environment and recoup the capital with paying customers down the road.

The 30 day trial is just not long enough to build and sell anything, and then I run up against the monthly fees of $167 per month. This is kind of pricey for cross platform development. I can get an indie Xamarin license for $25 per month. Plus, since the community edition is riddled with bugs that renders it almost completely unusable, I’m not feeling very inclined to shell out dough for Wavemaker. It’s not even really monthly pricing. You make the annual fee of $2000, or four monthly installments of $500 (if you read the fine print).

Also buried in the feature list, it also states that you can’t deploy mobile apps on community. That was not readily discernible early on. They say it’s got a reduced feature set, but they don’t really tell you which features come with each version. Is that a documentation error, or shady marketing? I can’t tell, but all of the lacking/deceptive documentation is also not giving me a high confidence level to part with any cash.

Features

2016-01-21 08_37_46-WaveMaker _ RAD for Web and Mobile AppsThere is a lot of promise here. I really liked the premise of what they are going after. A visual drag and drop IDE to build fast database forms, security, and deploy across any platform. I never actually got to the point of deploying a real mobile app, but it sounded easy. The database designer seemed top notch and very intuitive. The first problem I encountered with the DB import feature was that I have MSSQL 2008, and not on a default instance. This means the port number is random, and I see that being problematic if anyone is using more than one MSSQL instance, as most ODBC drivers for MSSQL need the instance name, not the port. The second problem I had with the DB designer was when I started with an existing DB model in MySQL, as the PK and FK settings seemed different from what you would build in Workbench than what Wavemaker needs to run. I had much better results when I designed the DB model from scratch using the Wavemaker tools. I would almost be tempted to keep the CE version around just to visually design DB models in MySQL.

The last criticism I had was when working on security. The community version seems to lose it’s security settings, or when I tried to delete them and recreate them, I started getting errors on my page that some function endpoint was not set. I have no way of telling if this bug was related to the security features, or the multiple importing of my DB model until I recreated it from scratch, but there is no way that I could find to go in and correct this bug, which caused me to have to start all over, ultimately ending my proof of concept in Wavemaker. It reminded me of building a huge form app in Access only to have it get corrupted and needing to start all over again, which is very frustrating.

In conclusion, this project seems like it holds a lot of promise. I really wanted this to be an awesome experience, but it just wasn’t. It is obvious that they have put a ton of work into the UI to make it intuitive and automate everything possible. The maturity of the documentation might be a barrier to entry, and the pricing needs a startup-entrepreneur tier that has all of the features and keeps up with the bugs, like an Indie tier, that allows for people to go and build products that can pay licensing once revenue for those products comes in. I simply want to build something, sell it, and then give them their fair licensing share. I’m not trying to be underhanded here – that’s just how the small startup works. I could even see a business model where I could just build mobile apps for companies and let Wavemaker host them for me. It seems like this is really geared towards big companies for internal apps and a budget to throw away if it doesn’t work out. Give these guys another couple of years, and this might really be something. Unfortunately, it just isn’t at the right place in it’s life cycle for me to really use yet. I’ll keep watching.

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Mobile Development vs MicroStrategy Mobile

Mobile-Application-Development

I had a potential customer ask me yesterday if we could build mobile apps. Apparently they had just spent a lot of money on an app that would allow them to collect data in the field, but it did not integrate with any of their shipment data or POS sales data to complete the picture for their field personnel. This company also did not have the resources to build any kind of custom app from scratch themselves. So, if you are thinking about taking this feat on, let me break it down for you in a few high level steps.

There are many scenarios that you can walk down, but I am going to walk down two specifically: Custom app vs. MicroStrategy mobile app.

Scenario 1 – Building a custom app from scratch.

First off, you are going to need a good overview on iOS development. Try here for starters. When you go down this road, you are going to need a Mac to do your development on. You will also need a developer account with Apple to be able to publish the app. If you want to be backwards compatible, you may need more than one Mac to test on, as the XCode environment is tied to the OS (from what I can tell). You also will need an iPad or two, or three for testing. If you want to support iPhones, you will need some of those. What about the version of iOS? We are currently on 9.x. Do you want to support 8.x as well? While there are testers for some of this in xCode, if you want to make sure your app works across all of these environments I think it is a good idea to develop a test plan on actual HW so that your app isn’t flakey.

On your app side, you are going to be writing a lot of Objective C code to run the app, but you are also going to need a service in the background to dish out data and be the backend for the app. I doubt you would want the app to connect directly to your database. This service should also handle secure logins, passwords, user management, resetting a user password – all of the plumbing that will enable a user to mange the app, their account, and themselves. It also needs to grab data from the data warehouse and package it back to the app. You might need to compress it to make the app more faster.

Now, once you’ve climbed through all of that, you get to manage change management coming from user feature requests, from corporate, from bugs. You get to roll out new versions, craft a test plan to make sure it all remains backwards compatible with older versions of iOS, across all apple devices. To keep up, you may have to juggle a roadmap with multiple versions in play at various lifecycle stages – or in other words, you may be performing user acceptance testing on version 2.5 while you are working on publishing version 2.4 to the app store, as well as scoping changes to version 3.0 to be released next quarter.

I would not say any of this is rocket science, but it can grow to be quite an undertaking if you want to do it right. Wait – where is your Andriod app at? Corporate CFO has an android and wants his version for his phone. Where do you start for that? Now, remember that app that you thrashed in the comments because it was so buggy last week? Feeling even the least bit sorry for that company if this is one or two people trying to keep up with all of this?

Scenario 2 – MicroStrategy Mobile

Now that your head is spinning from trying to develop and support a custom app, there is a bright side to all of this – MicroStrategy Mobile. There are lots of other platforms, and this article could go on for days, but we have direct experience in MicroStrategy Mobile so we will give a glimpse of this one to compare and contrast.

First off – you will need a MicroStrategy environment. This of course is not free – you will need to get an enterprise license for this and each user will need a license. Second – you will need to develop your data objects. This also is not for the faint of heart. Most companies do all of this because they want slick reports, dashboards, and gorgeous data visualizations., regardless of mobile or not. This is pretty much MicroStrategy’s bread and butter. It handles all of the service back end, scheduling, report automation, security, throttling, and presentation. You just need to get your data into a data warehouse. There are lots of strategies for BI – but, if you go down the MicroStrategy route, then you inherit a Mobile strategy second to none.

All of the reports you built for your monday morning dashboard can translate directly into a mobile app with just a small amount of effort. There is no source code you need to master. MicroStrategy can handle much of the iOS compatibility and hardware testing. It’s almost like a buy one, get one free. You get enterprise class reporting along with enterprise class mobile.

MicroStrategy also has transaction services, which allows you to input data on the iPad. Need to capture store shelf quantity, or survey questions? No problem. It can capture data alongside all of your enterprise data warehouse metrics for a complete, 360 degree dashboard. It can show images, take pictures, capture data, report data, drill into your data, visualize your data in graphs and charts. You can build an entire customer service app – just in MicroStrategy – with your company icon and logo.

Summary

Now, if you just needed a mobile app, is this the easier route? Depends on how you look at it. There is probably equal amount of effort getting both scenarios up to speed. I won’t lie and say that MicroStrategy is easy. The payoff comes downstream when you need to support your app. If someone requests changes to your app, you can make a change to your MicroStrategy dashboard inside of MicroStrategy – without needing to recompile, test, and publish your app to the Apple app store. This change, depending on the significance, could literally take you 2 minutes to log in and change something minor. Want to roll out a version of this app for a new customer? Copy, paste, and change the logo – again, maybe a 10 minute change. Because of the object oriented development nature of MicroStrategy, each dashboard will inherit all of the building blocks in the foundation you build. So if you formatted a date wrong, you go change the date attribute. All of your reports, dashboards, and mobile apps then inherit the change – no need to touch them.

Hours or days – not weeks or months. No objective C code to maintain. No API service backend to maintain.

80% of what you build in MicroStrategy is reusable. This is not the case with Tableau, Qlikview, SSRS, Crystal Reports, or custom ASP.NET portals. This is why we lead with a MicroStrategy solution. If we build a customer a neat dashboard to be consumed in a web browser, and the CFO determines they want it on their iPad, we just have to copy, paste, and then do a little resizing so it fits nicely and viola – instant mobile app. Maybe less than a day’s work. If you are building a custom app from scratch – where is your git repository hosted at again?

If your organization could benefit from a BI platform to deliver reporting,dashboards, and data discovery – and also needs a mobile app strategy – then this seems like a no brainer to me. Even if you think it might be useful down the road, then having a combined strategy for BI and mobile makes sense. If you go down the road of separate BI and mobile, then you are eventually going to have to join them up, and it will be twice the support at that point. Twice the cost and twice the fun.

Please contact us today to see how we can help you with your mobile app and BI challenges.

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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What Do Marketers Really Want in #DataAndTechnology?

Marketers get data – or at least they get the importance of data. Data answers questions such as:

  • Can you help me understand my customers?
  • Which customers are my best customers and why?
  • How can I find profitable new customers?
  • How can I sell more to existing customers?
  • How can I retain my existing share of each customer?
  • How can I increase the velocity of my sales?
  • How can I integrate my marketing through all available channels?
  • How can I maximize the impact of my marketing budget?

However, data is just data unless you have the marketing technology to “make the data talk.” Marketers are increasingly in charge of marketing technology spend to drive better data outcomes. In fact, technology has become the core of marketing. According to research by IBM, marketing executives are adopting technology in the following areas:

  • 88% Customer Relations

  • 83% Digital Marketing

  • 68% Customer Analytics

  • 49% Mobile Advertising

Read more. . .

Contact us today to discover how Vortisieze analytics can take you to the corner of Marketing and Technology.

Source:  What Do Marketers Really Want in Data and Technology?

#DataAndMarketing
#BigData
#CPG
#CPGMarketing

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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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Exploring #BigData Business Models & The Winning Value Propositions Behind Them | #CPGMarketing

This article, by Justin Lokitz – a thought leader in big data, provides an excellent overview of three distinct business models using big data.  Any one of which, or a hybrid, can be incorporated in a CPG category manager’s analytics process.  The article is worth a separate read but the main points are summarized, and applied to CPG, below:

It goes without saying, innovative, sustainable Big Data Business Models are as pervasive and sought after as they are elusive (i.e.  “data is the new oil”).  For every startup that designs and implements what amounts to a devilishly simple and effective big data business model (see any social network), perhaps changing the entire landscape with it, there are literally hundreds (if not thousands) of larger, more mature companies looking for ways to monetize their own big data in the hope that they can capture new revenue streams (and compete effectively in the future).  Of course some of the larger, mature companies have done quite well in this regard.  Apple (40 years old) and Amazon (20 years old), for instance, have vastly different business models.  Yet, both companies have built solid business models around big data; both use big data to present to consumers products and services that might be relevant to them.  Similarly, Netflix and Pandora, 18 and 15 years old respectively, designed brand new big data business models around understanding and creating value for customers in ways that seemed like magic at the time.  So, what’s behind these business models?  And, are there other business models that might help other (mature) companies create, deliver, and capture value using big data at the core?  The answer (to both questions) is simple: it’s all in the value proposition.

He further states in his introduction:

“Fall in Love with the Problem, Not the Solution.” As simple as this quote is it speaks volumes when considering how mature companies tend to think about utilizing their own big data stores to create new business models.  That is to say most mature companies first ask, “What big data do we have today?” followed by, “how might we sell this data?” Looking back on my favorite aforementioned quote, you can probably see the discrepancy here: most mature companies believe there is some mythical marketplace where they can simultaneously sell their big data whilst not pissing off their customers.  These assumptions are more often than not wrong.  Moreover, while there are LOTS of “problems” to fall in love with when it comes to big data business models, in order to provide some focus, this post highlights three categories of big data business models based on their value propositions and customers (e.g.  DaaS, IaaS, and AaaS respectively).

Big-Data-Pyramid

  • Data as a Service (DaaS)

DaaS hinges on a value proposition for supplying large amounts of processed data with the idea that the customer’s job-to-be-done is to find answers or develop solutions for their customers.

For CPG companies partnering with large retailers as a trusted supplier – this usually begins with the POS/Inventory data supplied at the vendor level or, where appropriate, the category level.

The granularity of data can be daily or weekly and provide historical data – usually 104 weeks.  While the author speaks in general terms about marketing data to monetize it (in fact the entire article has an eye toward this), CPG companies cannot sell retailer supplied data.  This does not mean that you, as a CPG category or sales manager cannot monetize the data.  For you – monetization occurs when you use analytics to gain insights to share with your buyer(s).  The goal of this activity, of course, is to flank your competitors within the category, increasing your brand’s market share within the retailer ecosystem.

Part of the Vortisieze service offering is providing a fast, clean, single source of the truth, aggregated data and the analytics tools to empower you to produce your own insights.

data-as-a-service

  • Information as a Service (IaaS)

IaaS focuses on providing insights based on the analysis of processed data.  In this case the customer’s job-to-be-done is more about coming up with their own conclusions or even “selling” an idea based on certain information.  Additionally, IaaS customers don’t want to or do not have the resources to process and analyze data.  Rather they are willing to exchange value for analysis from trusted parties.  Unlike the DaaS business model, which is about aggregation and dissemination of lots of processed data for customers to create their own value propositions from, the IaaS business model is all about turning data into information for customers who need something – and are willing to pay for something – more tailored.

Because we have category manager DNA in our company’s DNA, Vortisieze can meet this need by providing ready-to-use analytics, dashboards such as Business at a Glance (BaaG) for example.

information-as-a-service

  • Answers as a Service (AaaS)

AaaS is focused on providing higher-level answers to specific questions rather than simply the information that can be used to come up with an answer.  CPG companies who implement the AaaS business model do so in gain answers to answer specific questions.

This business model, as you might guess, is the top of the pyramid when it comes big data.  The key with this business model is that given the CPG company’s ability to create real, trusted value in the answers it provides to buyers, buyers take note and value the insightful answers provided.

When a category manager partners with Vortisieze by asking very specific questions needing answers (remember strategy drives questions), we can provide answers to your most important questions.

answers-as-a-service

 

Vortisieze technology, unlike rigid DSRs or yesteryear, provides pliable, and rapid, solutions to meet your analytics needs.

Contact us today to discover how.

 

About the article author:

Justin Lokitz is Strategy Designer & Managing Director at Business Models Inc.  San Francisco

Source:  Exploring Big Data Business Models & The Winning Value Propositions Behind Them

 

#BigData
#BigDataAnalytics
#CategoryManagers
#CPG
#CPGMarketing
#DataasaService
#InformationasaService
#AnswersasaService

 

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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/

 

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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

 

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#DataAsAService
#BigData