Best of Upside’s Data Digest
Interesting articles we found on the Web focus on data scientists, real-time analytics, governance/compliance, and making old data profitable.
- By Quint Turner
- February 12, 2016
Turning Old Data into New Profits
Once your data quality is high and all governance operations have been completed, what can you do with it after it is analyzed? The new economy of data says “sell it!” This article covers some of the steps to take to sell data as a service.
Hiring a Prep Data Scientist to Boost Your Team’s Productivity
(Source: Tech Republic)
Data scientists, according to most enterprises, have not lived up to their hype. However, this is partially because their main job isn’t analyzing data; 80 percent of the time they are preparing data. The remedy, this article says, is a “new” position, the sous data scientist, who prepares data for the lead data scientist to analyze.
Real-Time Analytics is the Evolution of Big Data
(Source: I Crunch Data News)
The most efficient use of big data is analyzing it as soon as your enterprise gets it. Also known as real-time or streaming analytics, this process is the next big thing in big data. This article is the first in a multi-part series on the rise and impact of real-time analysis in the enterprise.
Better Governance Equals Better Data
If your enterprise collects a bunch of data but does not know where said data came from, if it is accurate, or can answer any number of important questions, that data is not useful. Governing big data is a chore, but an important one. This article reviews the major problems in data quality and governance and how to fix them.
Making an Efficient and Compliant Data Strategy
(Source: Dark Reading)
Before undertaking an ambitious big data plan, it might be prudent to make sure that your plan falls within legal boundaries. This article offers nine tips for making a big data strategy that is effective and compliant with regulations.
Don’t Dive into the Data Lake without a Plan
(Source: Inside Big Data)
Big data should deliver insight faster than any other method for BI, but many enterprises still struggle with it. This article explains that the troubles come from drowning in the data lakes; enterprises have so much data they do not know what to do with it once they have it. Rather than waiting until after collecting data to make a strategy, perhaps enterprises should make a strategy before building up their data lakes.
Quint Turner is an editorial intern at TDWI and an undergraduate English student at Skidmore College. Follow his blog at pungry.com.