Blogs

Fast track your data

Fast track your data

May 25, 2017 | by Oliver Clark, Social Media Execution Strategist, IBM
Join our CrowdChat about developing a competitive advantage with machine learning, data governance and data science.
How to get fast insights from the right data

How to get fast insights from the right data

May 22, 2017 | by Ronan O'Connor, Digital Content Manager, Watson Data Platform, IBM Europe
Data, insights, cloud, agile, analytics. These are all terms that get thrown around a lot in technology these days. But the truth is that unless you can combine some or all of these concepts, the bottom line benefit to your business will likely not as great as you may expect.
What is optimization and how it improves planning outcomes

What is optimization and how it improves planning outcomes

May 16, 2017 | by Ryan Arbow, Product Manager, Advanced Analytics, IBM
This is the first in a sequence of blogs that looks at how Planning Analytics and Decision Optimization can help organizations go from a plan to the right plan by leveraging optimization throughout the planning process.
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?

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