Blogs

Top 3 ways to measure the success of your analytics investment

Top 3 ways to measure the success of your analytics investment

May 9, 2017 | by Susara van den Heever, Product Manager – IBM Decision Optimization, IBM
Line-of-business (LoB) stakeholders want to know that their analytics investment will help them make better, faster, and smarter decisions, with measurable business results. But for many, measuring success from applying Machine Learning and Decision Optimization is not obvious. Learn the top 3...
The quant crunch: The demand for data science skills

The quant crunch: The demand for data science skills

May 1, 2017 | by Steven Miller, Data Maestro, Global Leader Academic Programs, IBM Analytics Group, IBM
Extreme focus has been placed on the nascent data scientist role but, in contrast, the much larger demand for data-savvy managers (1.5 million new positions) has largely been ignored by academia.
Real-time personalization with streaming analytics

Real-time personalization with streaming analytics

April 27, 2017 | by Preetam Kumar, Product Marketing Manager, IBM Analytics, IBM
Context-aware stream computing helps you become more responsive to emerging opportunities. By using innovative technologies to understand the context of data and analyze data in real time, you can put data to work.
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...
Development lifecycles for defining the meaning and structure of the data lake

Development lifecycles for defining the meaning and structure of the data lake

April 18, 2017 | by Pat O'Sullivan, Senior Technical Staff Member, IBM Analytics
In the past, the relationship between the different models that might be used in defining a data warehouse was a very linear one. There may have been different model artifacts used as the team responsible for developing the data warehouse progressed through the usually waterfall-type set of...
What to do with all that machine learning data

What to do with all that machine learning data

April 18, 2017 | by Susara van den Heever, Product Manager – IBM Decision Optimization, IBM
Many businesses are starting to notice that without converting the masses of new data generated by the Machine Learning (ML) wave to Smarter Decisions, the impact falls short of expectations.
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?
Charting the data lake: Using the data models with schema-on-read and schema-on-write

Charting the data lake: Using the data models with schema-on-read and schema-on-write

March 14, 2017 | by Pat O'Sullivan, Senior Technical Staff Member, IBM Analytics
In many cases the data lake can be defined as a super set of repositories of data that includes the traditional data warehouse, complete with traditional relational technology. One significant example of the different components in this broader data lake, is in terms of different approaches to the...
Understanding the power of real-time geospatial analytics

Understanding the power of real-time geospatial analytics

March 13, 2017 | by Preetam Kumar, Product Marketing Manager, IBM Analytics, IBM
With the Geospatial Analytics service in IBM Bluemix, you can monitor moving devices from the Internet of Things. The service tracks device locations in real time with respect to one or more geographic regions. Geospatial Analytics can be used as a building block in applications that support...
Fundamentals for sure-fire cloud data warehouse optimization

Fundamentals for sure-fire cloud data warehouse optimization

An interview with James Kobielus

March 2, 2017 | by Andrea Braida, Portfolio Marketing Manager, IBM
There is a growing need for versatile, hybrid architectures that can combine the best of both data warehousing and big data analytics. The cloud is the perfect solution, because it makes it easier to build a robust data warehouse as a central “hub”, and then add other environments that can be...