The Business of Tech
Putting tech to work is the core of what we do. To do that, we make sure innovation is in our DNA, whether it’s breakthrough technology or a smaller change in a process that leads to greater productivity. Read on to learn how to innovate at any level of enterprise tech, how to implement AI in enterprise (my best advice there is often, start small) and more.
Db2 for z/OS is one the most business-critical products in the IBM portfolio and remains core to many transaction processing, advanced analytics, and machine learning initiatives.
Looking back…
Imagine being able to arrive at your destination as much as 200 times quicker or being able to complete your most important tasks as much as 200 times faster than normal. That would be pretty impressive. What if you could get answers to your analytics queries that many times faster and run your machine learning algorithms with maximum efficiencies on your data by simply plugging in a pre-configured and pre-optimized system to your infrastructure? That’s what the IBM Integrated Analytics Systems (IIAS) is designed to do.
I have just finished presenting at the DataWorks Summit in San Jose. CA. where a partnership between IBM and HortonWorks was announced the aim of which is to help organizations further leverage their Hadoop infrastructures with advanced data science and machine learning capabilities.
With many of the top banks, retailers, and insurance organizations using IBM® z Systems® , combined with tried and tested virtualization capabilities, EAL5+ security rating and the ability to handle billions of transactions a day[1], the platform becomes attractive as a private cloud for running advanced analytics as well as cloud managed services.
Readers of this blog know that I like to imagine the world through the eyes of my young son. His effort to find the right balance of exploration and safety resonates with what we mean by “private cloud” and preparing clients for a hybrid cloud environment: private plus public.
In a previous blog I talked about the value of machine learning and how it could help organizations by making smarter predictions by continually learning and adapting models as it consumed new interactions, transaction and data. I compared that to how my son embraced learning about the world around him to become gradually smarter, more knowledgeable. But that doesn’t always mean he is going to make the best decision because he may not have all the information or foresee or correlate past events.
Billions of connected devices, zetabytes of data, power and brand loyalty now in the hands of the consumer, businesses having to market and sell to each and every one of us. What just happened? Three driving forces – mobile, cloud and a continuing explosion of data. Everything just got personal.
It seems to me that more and more companies are adding Apache Hadoop to their mix of technologies in attempts to spend less time in the creation of an Hadoop infrastructure and focus more on gaining the benefits. IBM BigInsights (our implementation of Hadoop) is ready to take on that challenge. It provides a complete solution that is not only open and combines Hadoop and Spark, but also provides the right tools to help make Big Data easier and more scalable, while integrating it seamlessly with SQL, noSQL and other data types.
Apache Spark™ puts both deep and broad advanced analytics capabilities in the hands of the masses. Whether a data scientist, data engineer, analytics app developer or citizen analyst – Spark delivers sophisticated analytics simpler, faster and more efficiently than ever before.
In my last blog “Business differentiation through Machine Learning” I introduced and described the concepts of machine learning. We traced its origins from a computer science project to Watson showcasing and winning on the Jeopardy TV quiz show and its real world use across numerous industries including health care.
I look at my child and marvel as he embraces the ever fast moving world around him, adapting to new experiences, grasping technology, absorbing a bombardment of information from so many sources. It’s staggering to watch their progress from basic learning of just accepting facts they are taught, to augmenting those facts with their own knowledge, to asking questions, using their knowledge to express their opinions and values to others, then challenging facts and hypotheses that they once accepted to adapting their knowledge, understanding, decisions and value systems.
The origins of Apache™Hadoop® go as far back as 2003 in reference to the emergence of a new file system, followed by the introduction of MapReduce and the birth of Hadoop in 2006. It achieved notoriety and fame as the fastest system to sort a terabyte of data and when it became an Apache open source project (Apache Hadoop) it sent a signal that it was ready for prime time. The world never looked back.
A few months ago, I was talking with the CTO of a major bank about machine learning. At one point he shook his head ruefully and said, “Dinesh, it only took me 3 weeks to develop a model. It’s been 11 months, and we still haven’t deployed it.”