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DataViZ, Data Science and Machine Learning White Papers – Part 4

Here are some white papers about Tamr, Lavastorm, Teradata, Rapidminer, Looker, Thingworx, and DataRobot :


  • Three Problems that Sabotage Analytics and How to Fix Them – Three Problems that Sabotage Analytics Every Time — and Four Ways to Solve Them. A recent Forbes survey of large global company executives found 47% “do not think that their companies big data and analytics capabilities are above par or best of breed.”
  • 2016 Big Data Predictions from Mike Stonebraker and others – It’s the season for predictions, and Tamr is no exception. We’re fortunate to have some of the most forward-thinking minds in the data world involved with our company. So we asked them to hit pause on 2015 for a few minutes and turn their attention to what’s to come in 2016. 


  • Overcome the Analytic Limitations of Access and Excel – Although they are often a cornerstone of a company’s analytic toolkit, tools like Access® and Excel® are designed for data storage and basic analytics, not for creating the complex analytics that are required by today’s fast-moving businesses. New technologies can help organizations move past the analytic limitations of Access and Excel, especially when dealing with the demands of processing more data, and making analytics available to more decision makers and stakeholders – all at greater speed than ever before. 


  • Flip the 80/20 Rule for Analytics in the Hadoop Data Lake – How to Flip the 80/20 Rule for Analytics in Hadoop. Hadoop Data Lake — Data Preparation Success. Data Scientists and Analysts in the Hadoop Data Lake are spending 80% of their time on data preparation and only 20% of their time on the actual analytics.


  • Are Your Predictive Analytics Secure on Hadoop? – RapidMiner Whitep… – As Big Data initiatives move into production, Hadoop security has shifted to the forefront of priorities. How can you keep data secure as it is being mined for predictive insights? As you know, most traditional analytics vendors extract data from Hadoop to build and score analytic models. Moving big data out of Hadoop increases security risks, reintroduces bottlenecks, and increases complexity. 


  • Whitepaper: O’Reilly Research on Integrating Data for Better Analytics – Companies are collecting more data than ever. But, given how difficult it is to unify the many internal and external data streams they’ve built, more data doesn’t necessarily translate into better analytics. The real challenge is to provide deep and broad access to “a single source of truth” in their data that the typically slow ETL process for data warehousing cannot achieve. More than just fast access, analysts need the ability to explore data at a granular level.


  • Need Help Tackling the Challenges of IoT Analytics? – IoT analytics is one of the hottest areas of technology today and presents huge opportunities for cost savings and improvement in various organizational functions like service, product development, manufacturing and more. However, generating insight from IoT data is not without its challenges – one must consider the cost of data scientists and performance limits, data type and volume changes, and static versus dynamic modeling.


  • Machine Learning is a Game Changer – Making sense of the mountains of data collected on a daily basis requires specialized data science skills that are hard to come by and hard to keep. But what if some of these specialized tasks could be augmented or even eliminated by machine learning?

For more white papers, click here.

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