Data science modernization is key for digital transformation

Digital transformation is a holistic change across an organization’s people, processes, and technology. This change uses digital technology to modernize how the organization operates, delivers services, and creates opportunities in the market. The key to successful digital transformation is data science and the related ability to use the power of data to drive speed, accuracy and agility for decisions. This includes infusing machine intelligence into day-to-day decisions using a combination of automation, prediction and optimization. You can learn more about the role of data science in bringing additional value to an organization and becoming AI-empowered with the ebook, “Six reasons to upgrade your data science”.

Here are three examples of how data science modernization leads to successful transformation.

1. Make better decisions with predictions and optimization

A key source of competitive advantage is the ability to make data-driven decisions rather than basing your decisions on intuition or analogous past experiences. This is especially important for companies and organizations that deal with a number of moving factors such as decision variables and business constraints when performing resource allocations, planning and scheduling. So it is critical to capitalize on the combined power of business intelligence, predictive analytics and optimization tools to move from data to insights to actions faster.  Another critical capability is “what-if scenario” analysis which helps get ahead of uncertainties and achieve business goals such as cost savings, revenue maximization and customer satisfaction.

For example, Serco, a company responsible for maintaining a bike-sharing service in London, needed to find a cost-efficient way to manage and maintain 12,000 shared bicycles across 800 stations. Using a combination of IBM® Decision Optimization and machine learning, Serco was able to forecast and predict the movement of bikes as well as customer behavior and needs, which helped them make decisions about when to repair bikes and how many bikes to station at every location.

The result was a 15% reduction in costs related to replacement parts and a 10% increase in productivity for their drivers. Rather than making a decision based on intuition or past experiences, Serco turned to decision optimization technologies to make a data-driven decision based on customer data and usage patterns.

Read the ESG Technical Validation Report to find out how IBM Decision Optimization for IBM Watson® Studio can help your organization use predictive and prescriptive insights to make better, data-driven decisions and extract value from AI faster and more efficiently. 

2. Automate AI model development for the highest quality models

A key component of data science modernization is using AI to build AI. This enables more accurate models to be built while accelerating time to value. Wunderman Thompson, a New York-based global marketing communications agency, automated its AI model development, producing the highest quality models possible using AutoAI within IBM Watson Studio, now integrated into IBM Cloud Pak® for Data.  By using AutoAI to boost model quality and performance, Wunderman Thompson was able to speed up the production of models that help them discover new personas and potential customers within their database, and then target campaigns to help their clients grow business.

First, Wunderman Thompson uses decision tree modeling to select the most important features for training data. AutoAI then executes its algorithm to determine new possible features by combining and transforming existing features and testing them to see which ones boost performance. Wunderman Thompson is then able to incorporate thousands of features within their models, resulting in better performing models with an overall improvement of 150%.

Read the 10 ways to use AutoAI eBook to learn more about how AutoAI can help you make the most of your data.

3. Bring your data to life with visual data modeling and data visualization

Data science can be highly technical and somewhat confusing or intimidating to those of us who do not have mathematical backgrounds. This can hinder the ability to maximize value from AI because SMEs and business analysts are unable to bring their business expertise to improve data science models. This is why data visualization and visual data modeling are so critical in translating data outcomes into business recommendations. Visual techniques help tap the combined expertise of business analysts, data scientists, citizen data scientists, and subject matter experts to build innovative data science solutions.

James Fisher and Sons plc, a UK-based marine solutions organization, used IBM SPSS® Modeler to conduct visual data modeling and  data visualizations to plan the maintenance of their undersea cable lines that connect offshore wind farms to the mainland. With visual data modeling, data scientists could access 40 drag-and-drop algorithms, test their ideas, and develop and deploy models with an easy-to-use graphic interface. Users could access data about their subsea cables via an easy-to-consume dashboard and get live updates on the status of the cables. This helped James Fisher improve its service of providing renewable wind power from offshore wind farms to consumers on land while saving maintenance costs and decreasing power outages.

With IBM Cloud Pak for Data, you can enjoy the benefits of visual data modeling and data visualization, while taking advantage of open-source innovation, including R and Python.

Read The Forrester Total Economic Impact™ Of IBM  SPSS and discover how you can bring your data to life, engage key business stakeholders and achieve a positive ROI.

Jump-start your digital transformation by using an all-in-one data platform

Whether it is improving the quality and efficiency of your AI models through automation, making data-driven decisions with decision optimization technologies, or visualizing your data for better collaboration and easier insights, IBM Cloud Pak for Data can help you achieve data science modernization milestones towards a successful digital transformation.

With IBM Watson Studio in Cloud Pak for Data, you get an  all-in-one solution that allows you to take advantage of predictive and prescriptive analytics. With a cloud-native architecture that is critical for scaling and accelerating data science, businesses can innovate anywhere—whether on cloud or on-premises.

Find out how you compare to best-in-class companies with the interactive guide, “Unleash the Power of AI: Bridging the Gap Between Analytical Activity and Mission Critical Decisions”.

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