Upside Briefing: IBM Advanced Analytics
IBM believes that analytics can be everywhere. We offer Upside's first impressions from a business briefing.
- By Fern Halper
- April 20, 2016
Name of Company/Solution: IBM Advanced Analytics (IBM SPSS and IBM Decision Optimization) (More information here and here)
TDWI Product Categories: Advanced Analytics, Predictive Analytics, Prescriptive Analytics/Cloud
Product-line Vision: IBM believes that analytics can be everywhere, infused in what you do. The vision is that analytics becomes part of your everyday experience in a natural way.
Briefing Notes: I recently had a briefing with Jane Hendricks, Worldwide portfolio marketing manager at IBM Predictive Analytics to get an update on IBM SPSS Predictive Analytics and IBM Decision Optimization. These two product families provide the predictive and prescriptive capabilities that are a key part of the broader IBM Analytics platform.
We covered a lot of material in the session. The highlights were focused on making advanced analytics actionable by bringing predictive and prescriptive capabilities closer together, IBM's support for open source, and its push to help developers create applications infused with analytics using composable microservices (with the first set focused on scoring and decision optimization).
Key themes included:
1. Bringing predictive and prescriptive closer together. At a very high level, predictive analytics looks at the probability of what will happen. Prescriptive analytics goes further than predictive analytics to either suggest or automatically initiate subsequent actions—such as a plan, a schedule, or next-best action—from that prediction that would produce an optimal result, often using optimization.
In other words, it does the predictive piece and then decides how to take advantage of it. Predictive and prescriptive analytics have been part of the IBM Advanced Analytics portfolio for some time. Predictive analytics falls under the SPSS Predictive Analytics suite. Prescriptive analytics is part of the IBM Decision Optimization Suite. This includes CPLEX Optimization Studio, Decision Optimization Center, and Decision Optimization on Cloud.
Now predictive and prescriptive are part of the same advanced analytics product family and they will continue to be "blended." That is good news because action is where the real value is with analytics. IBM is also offering better cloud options for its advanced analytics, including Bluemix, SaaS, and hybrid options. Prescriptive analytics can be deployed in the cloud, in streams, and on big data platforms such as Hadoop.
2. Commitment to open source. IBM is committed to support open source in its advanced analytics portfolio. The company has extended its open source integration to Python and the ability to take advantage of Spark libraries natively within IBM SPSS Modeler and via a CPLEX API for Python users. Optimization models can be built and solved in Python, and optimization models can be embedded into any Python-based application. This is also extended to the cloud. If the Python API detects a running Decision Optimization on Cloud subscription, it will automatically launch and solve on the cloud without the need for manual configuration and set up.
3. Analytics microservice development support. IBM wants to enable developers to build analytics applications from composable microservices. The goal is a complete self-sufficient workplace on the cloud where data scientists, operations researchers, and application developers can build, deploy, collaborate, and sell prescriptive analytics services and solutions. Stage 1: IBM has a predictive analytics scoring service through BlueMix for IBM SPSS Modeler users and a free trial of self-service decision optimization on the cloud is available now.
First Impressions: The IBM Advanced Analytics Portfolio is a robust set of platforms, tools, and solutions that I've been following for years. This platform will continue to evolve. What I think is important here is that IBM is emphasizing how important it is to embed and operationalize models to drive action from data. Prescriptive analytics can help with that. Additionally, the notion of building and deploying applications "infused" with analytics, based on an open and extensible platform, on premises or in the cloud, is where the market is heading.
IBM has so much to offer in analytics and advanced analytics. It has been a leader in advanced analytics. It can be easy for its customers and potential customers to get confused by the breadth of its offerings, especially with its move to positioning with cognitive computing as an umbrella term, an area that many organizations are just beginning to even try to understand. One goal for IBM Advanced Analytics is to continue to try to clearly articulate its offerings in the space.
Fern Halper, Ph.D., is well known in the analytics community, having published hundreds of articles, research reports, speeches, webinars, and more on data mining and information technology over the past 20 years. Halper is also co-author of several “Dummies” books on cloud computing, hybrid cloud, and big data. She is VP and senior research director, advanced analytics at TDWI Research, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and “big data” analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her at [email protected], on Twitter @fhalper, and on LinkedIn at linkedin.com/in/fbhalper.