Data science and machine learning provides the basis for business growth, cost and risk reduction and even new business model creation.

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IBM Watson Studio is named as leader by Forrester Wave report

The new Forrester Wave report examines Predictive Analytics & Machine Learning Platforms

Capabilities

Predictive analytics

Predict with confidence what will happen next, so you can make smarter decisions for your organization. IBM is a leader in data science platforms.

Machine learning

IBM has one of the leading data science platforms, enabling you to easily collaborate across teams, use the open-source tools and scale at the speed your business requires.

Prescriptive analytics

Help organizations make better decisions by solving complex optimization problems involving trade-offs between business goals and constraints.

Leading modern data science teams

By Carlo Appugliese
@CAppugliese

While data science and AI are relatively new in the market, the concept of extracting value from data has been around for a while. But time has changed things —  and leaders are dealing with a variety of tools (open source options with Rand Python and proprietary packages, like SAS and SPSS), skills and techniques (machine learning, optimization, stats and more) and a variety of workload options dealing with large data (EDW, Hadoop and more). Leading  data science teams is no easy feat — and many have been asking questions about how they can maximize their business value today. Carlo Appugliese shares key insights from his team's experiences helping hundreds of companies enhance their data science practice, ranging from how to shorten time to impact to how to extract the best work from your team.

Carlo shares six steps to leading an effective DS team:

  1. First and most important, focus on your business objectives and problem; don’t get caught up with technology. Don’t do technology for the sake of technology.
  2. Don’t overanalyze what you’re trying to do. Some of the most successful projects are delivered by just doing it. Identify your business problem/use case, put a working team together and go.
  3. Don’t limit the data your team has available to them. Data scientists need to explore, and sometimes their approach will change based on findings. Give your scientist access to all the data.
  4. Think about how you will operationalize your project at the start. Don’t code an entire solution and then rewrite the model in a digital application. Build the solution in a technology you can operationalize.
  5. Don’t be afraid to use the latest Python frameworks. Open frameworks are community-developed and have outperformed traditional approaches. Just scan some the Kaggle winners, http://blog.kaggle.com/category/winners-interviews/. Python is the clear leader, so don’t be afraid of open source.
  6. Empower your team, so that even the most junior scientists can use open frameworks to solve your business problems. Some of my most productive team members are knocking it out of the park right out of graduate programs.
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Client success

Third Fifth Bank

Fifth Third Bank saw the potential of state-of-the-art data science tools to optimize marketing analytics—and used IBM Watson Studio to create a controlled environment.

Accenture

Understand from Accenture’s Julio Sanchez on how they use IBM Watson Studio to bring together data management, real-time analytics, AI and machine learning into one environment

Is data science driving value for the business?

Hear from data science, analytics and business leaders at Revelwood, QueBit and Liberty Seguros in this trailer video.

 

XO Communications

Take control of customer satisfaction by using predictive analytics to embed a deeper understanding of customers into operations.

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