Blogs

Charting the Data Lake: How models can support the data scientist

Charting the Data Lake: How models can support the data scientist

June 14, 2017 | by Pat O'Sullivan, Senior Technical Staff Member, IBM Analytics
Perhaps one the single most significant changes to the analytics landscape in recent years had been the emergence of the data scientist. This role is continuing to evolve, with many organizations still in the process of establishing how best to incorporate this relatively new discipline into their...
IBM and Hortonworks: Accelerating data-driven decision making

IBM and Hortonworks: Accelerating data-driven decision making

June 13, 2017 | by Rob Thomas, General Manager, IBM Analytics Platform, IBM
Many of today’s top business performers successfully leverage a discipline – data science. Machine learning is one major way to apply data science and with machine learning, the more data we feed in, the better it performs. However, much of the world’s value data cannot be found on the Internet. It...
IBM and Hortonworks collaboration unveil data science for the enterprise

IBM and Hortonworks collaboration unveil data science for the enterprise

June 13, 2017 | by Holly Nielsen, Social Media Strategist, IBM
Companies have access to an unprecedented amount of data. Do they have the tools they need to make sense of these increasingly gigantic data stores?
#ITIQSpotlight: Trends and challenges in hybrid cloud analytics

#ITIQSpotlight: Trends and challenges in hybrid cloud analytics

June 9, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
In any successful modern organization, analytics is likely to play a central role in helping decision-makers design and execute effective business strategies. At IBM, as we work with clients across the globe, we’re seeing ever-increasing levels of maturity and confidence in data-driven business...
Charting the data lake: Model normalization patterns for data lakes

Charting the data lake: Model normalization patterns for data lakes

May 15, 2017 | by Pat O'Sullivan, Senior Technical Staff Member, IBM Analytics
The data lake can be considered the consolidation point for all of the data which is of value for use across different aspects of the enterprise. There is a significant range of the different types of potential data repositories that are likely to be part of a typical data lake.
Rethinking the modern data warehouse: Passé or progressive?

Rethinking the modern data warehouse: Passé or progressive?

May 15, 2017 | by Michael Lock, Vice President and Principal Analyst, Analytics & Business Intelligence, Aberdeen Group, A Harte Hanks Company
It’s easy to be blinded (and impressed) with the rapid innovation and evolution in the arena of big data. Today’s most technically sophisticated companies have the opportunity to exploit big data tools to address mind-numbingly cool use cases and produce very enticing results. However, so many...
Analytics and the cloud: The rise of open source

Analytics and the cloud: The rise of open source

May 9, 2017 | by Steven Lockwood, Executive Information Architect, IBM
This is the fourth in a series of blogs on analytics and the cloud. Read our introduction to the series. This blog concerns itself with the rise of open source software and how it is used for a whole host of analytical purposes. However, as will be seen in this blog, there are significant gaps in...
Big Replicate: A big insurance policy for your big data

Big Replicate: A big insurance policy for your big data

April 18, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
Dwaine Snow is a Global Big Data and Data Science Technical Sales Manager at IBM. He has worked for IBM for more than 20 years, focusing on relational databases, data warehousing, and the new world of big data analytics. He has written eight books and numerous articles on database management, and...
Analytics and the cloud: NoSQL databases

Analytics and the cloud: NoSQL databases

Schemaless databases and the role they play

April 6, 2017 | by Steven Lockwood, Executive Information Architect, IBM
Although NoSQL database technology has been around for a long time (before SQL actually), not until the advent of Web 2.0, when companies such as Google and Amazon began using the technology, did NoSQL’s popularity really take off. Market Research Media forecasts NoSQL Market to be $3.4 Billion by...
Incorporating machine learning in the data lake for robust business results

Incorporating machine learning in the data lake for robust business results

March 28, 2017 | by Karan Sachdeva, Sales Leader Big Data Analytics APAC, IBM
Building a data lake is one of the stepping stones towards data monetization use cases and many other advance revenue generating and competitive edge use cases. What are the building blocks of a “cognitive trusted data lake” enabled by machine learning and data science?

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