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For data scientists, now is the time to act; Forrester has insights to help you get started


IBM, a leader in 2017 Forrester Wave Report for Predictive Analytics and Machine Learning Solutions, offers data scientists a complete toolkit, including predictive analytics and machine learning capabilities and more.



By IBM Sponsored Post.

It’s no secret that enterprises which make decisions based on predictive insights will have an important advantage over the competition. What’s less understood is how to reliably create these predictive insights. Of course, data scientists have an important role to play in this process, but they must have the right tools available in order to do their jobs well.

The 2017 Forrester Wave Report for Predictive Analytics and Machine Learning Solutions looks at how different vendors provide predictive analytics and machine learning (PAML) solutions to empower data scientists to quickly and consistently turn out better predictions.

Forrester defines PAML solutions as software offerings that provide data scientists with:

  • Tools to build predictive models using statistical and machine learning algorithms
  • A platform to deploy and manage predictive production models

The report rates 14 of the most prominent PAML vendors across three main categories: current offering, strategy, and market presence. IBM was named a leader in the report, indicating high marks across all the criteria used in the ratings.


Forrester Wave Big Data Predictive Analytics 2017

The Forrester report points out that spending in the PAML space is predicted to experience a 15% compound annual growth rate through 2021, indicating high demand for the kind of predictive power these solutions offer. Organizations that invest in this innovative technology today have the potential to get a jump on the competition; those that wait until later risk falling behind.

Key takeaways

In addition to providing vendor profiles that readers can use as a starting point to help them as they evaluate PAML solutions, the Forrester report also includes key takeaways about the current state of the marketplace, and what characteristics are important to consider:

  • Open source: In its research, Forrester found that there has been a lot of open source innovation in the area of data science lately. Many of the solutions included in the report have open source capabilities embedded, which makes it easier for data scientists to work with their favorite tools and libraries.
  • Predictive model management: Forrester found that one problem data scientists face is with models that grow less predictive over time. According to the report, providing superior features to monitor models in production is one of the key concepts that sets leading vendors apart.
  • Artificial intelligence: Forrester argues that implementing machine learning capabilities now should be considered a requirement for organizations that intend to pursue artificial intelligence in the future.

IBM enables better data science

At IBM, we understand the unique challenges facing organizations hoping to fully benefit from data science capabilities. Highly qualified data scientists can be hard to find, which is why the IBM portfolio helps existing data scientists be more productive, while breaking through barriers that might have prevented them from living up to their potential.

IBM offers data scientists a complete toolkit, which includes but is not limited to the predictive analytics and machine learning capabilities described in the Forrester report. In addition, IBM data science solutions include:

  • Self-service data science: Helps data scientists take advantage of the best that open source has to offer, while also benefiting from IBM innovation. Data scientists are empowered to work with Jupyter notebooks in the language of their choice.
  • Cognitive computing: Across industry settings, cognitive is enhancing and scaling the expertise of human analysts, and data science is certainly no exception.

Another way in which IBM data science solutions drive better results is by empowering non-technical resources to take some of the burden off of their technical counterparts. With tools such as IBM SPSS Modeler and IBM SPSS Statistics, users can build and deploy models visually without needing to write code. As a result, business users are able to connect their organizations with actionable knowledge, while also freeing up data scientists for more high-value work.

IBM Data Science Experience fuses the best of IBM and open source capabilities in a single cloud-based social workspace. This platform brings together data science professionals to create and collaborate while choosing from a variety of open source tools, including Spark, Jupyter, R, Python and Scala in an extensible architecture. There are also additional capabilities from IBM, IBM partners such as RStudio, H20.ai and others.

Be sure to read the 2017 Forrester Wave Report for Predictive Analytics and Machine Learning Solutions for insights you can use to pick the best solution to meet your needs.