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

By Archive User posted Tue May 02, 2017 02:57 PM

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.

    The Forrester Wave: Predictive Analytics and Machine Learning Solutions, Q1 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.

    1 comment



    Tue June 27, 2017 08:31 PM

    I thought it was interesting how you said that machine learning capabilities should be a requirement for organizations that want to pursue artificial intelligence in the future. This would be a great way to get machines moving up to par with the tasks they are doing and maybe even being able to get them to do more than they were made for. I think it would be really helpful for data science software to have something like this so it could learn and remember as it gathered new data.