Data science for all – empowering business with visual approaches to data science

Data is an unstoppable force that is transforming industries before our eyes. Decision makers now have access to more data from more sources than ever before, including IoT, weather, mobile and traffic data. Organizations that can uncover new trends and opportunities in big data and apply that knowledge to differentiate themselves will be the ones to lead in their sectors.

Data science is the key to turning the data deluge into actionable insights. However, as more businesses strive to become data driven, they are finding that a shortage of data science skills is preventing the organization from becoming more data literate and benefiting from data science insights. For this reason, a growing number of organizations are seeking to enable more knowledge workers by providing a platform that is more intuitive to use, scalable across a variety of data science projects and open to other tools, including open source projects.

Rise of the citizen data scientist

Data science today is about much more than just technology; it is about people with different skills, backgrounds and roles who need to collaborate by solving problems for the good of the organization.

The worldwide shortage of skilled data scientists is well known, and has given rise to a group of analytic professionals often referred to as citizen data scientists or citizen analysts. These individuals are already working in core business areas such as marketing, finance, human resources or operations, and are being tasked with challenges such as:

• Increasing revenue by adding new customers or selling targeted offers to existing customers
• Improving cross sell rates during call center interactions
• Decreasing risk and costs by identifying fraudulent claims before payment
• Servicing a component in a production line to minimize downtime

While these analysts have extensive business knowledge, they are not necessarily skilled in mathematics, statistics or predictive modeling. That means there is a growing need for businesses to adopt solutions that are intuitive and approachable so these users can explore data more easily, find answers quickly and share results on their own.

At the same time, companies need to give data scientists and other analytical professionals the power and flexibility to create the best models, using their choice of either visual interfaces or programming for increased productivity. These power users need the ability to quickly refine models to target different segments, choose different variables and experiment with various algorithms and modeling techniques as well as integrate with the latest open source tools.

"Both approaches are viable and critical, and it’s important that organizations support both to meet demand and collaborate across the analytics pipeline," states Rob Thomas, VP of product development for IBM Analytics. "Every leader should be asking: How do I move data science into every part of my business?"

A new breed of data science solution

A new kind of data science tool has emerged that is powerful enough to satisfy the advanced needs of data scientists, yet easy enough for business analysts and others to use effectively. This tool helps extend data science and analytics beyond the domain of the data scientist by placing capabilities like predictive modeling and machine learning within reach of just about anyone.

IBM SPSS^® Modeler^® combines ease of use, power and flexibility to extend the ability to make data-driven decisions more widely than ever before. This graphical data science platform empowers users of all skill levels to:

• Intuitively visualize each step in the analytical process as part of a stream: analysts and business users can easily add expertise and business knowledge to the process.
• Transform data automatically into the best format for the most accurate predictive models
• Analyze data, identify fixes, screen out fields and derive new attributes with just a few clicks
• Identify the techniques that are best suited to solve problems and quickly create the best-performing model or models in a single step
• Use R, Python, Spark and Hadoop to amplify the power of their analytics, either through manual coding or by selecting from an extensive library of extensions that add advanced functionality while allowing users to maintain control

Designed to support the complete data science lifecycle, from data understanding to deployment, IBM SPSS Modeler democratizes access to data and insights across the enterprise. Learn how to make it part of your organization’s data science strategy.

Source: IBM Analytics