TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

Best of Upside’s Data Digest

Articles examine hiring data scientists, turning IoT data into profits, making predictions from data lake data, ensuring data is trustworthy, hoarding data, and the menace of shadow IT data.

• By Quint Turner
• June 29, 2016

Hiring the Right Type of Data Scientist
(Source: Kdnuggets)

Many enterprises are hiring data scientists only to be let down if the employee is not a good fit. This article describes two broad categories of data scientists and recommends concentrating your hiring search in whichever one best fits your business needs.

Turning IoT Data into Profit
(Source: IT Pro Portal)

The Internet of Things has to be more than everyday objects with sensors on them. This article claims that monetizing the new streams of data from IoT devices is one of the biggest opportunities IoT offers in terms of value.

Turning the Data Lake into a Crystal Ball
(Source: Information Management)

Enterprises implement data lakes because they hope to predict the future with analytics, but getting to that point takes far more effort than just collecting data. This article explains the benefits of a well-managed data lake and the best practices for setting one up.

Make Your Data Trustworthy
(Source: Information Age)

Data quality is a challenge in every enterprise, but large amounts of data make it even more difficult. This article reviews how new tools are helping business users and IT work together to quickly validate large amounts of data.

The Trouble with Data Hoarding
(Source: Datanami)

Thanks to extremely inexpensive storage, many enterprises are now hoarding huge amounts of data "just in case." According to this article, data hoarding wastes money and time. To fix the problem, enterprises need better employee education about realistic data value.

The New Menace of Shadow Data
(Source: Computer Weekly)

Shadow IT is a well-known problem in the enterprise, but the new threat on the block is dubbed "shadow data" -- any data uploaded, stored, or shared in the cloud without adequate access control. This article outlines some advice for finding shadow data and keeping it secure.

Open Source or Commercial for Analytics?
(Source: Dbta)

The first choice enterprises face when selecting analytics solutions is open source or commercial. This article explores the pros and cons to help those making this decision.