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What is data science, and why does it matter?
Data science is the process of using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision making.
Today’s data science teams are expected to answer many questions. Business demands better prediction and optimization based on real-time insights backed by tools like these.
The data science lifecycle starts with gathering data from relevant sources, cleaning it and putting it in formats that machines can understand. In the next phase, statistical methods and other algorithms are used to find patterns and trends. Then models are programmed and built to predict and forecast; finally, results are interpreted.
Advances in AI, machine learning and automation have raised the standards of data science tools for business. The result is the formation of data science teams — expert data scientists, citizen data scientists, programmers, engineers and business analysts — that extend across business units.
The opportunity here is massive. The automation of tedious data science tasks such as data preparation, and the empowerment of analysts without coding experience (00:21) to build models, keeps business agile and innovative. Automating the data science lifecycle frees expert data scientists to address the more interesting and innovative aspects of the field. Human intelligence — combined with data science technology and automation — helps a business extract greater value from data.
Data science
By 2025, 50% of data scientist activities will be automated by AI, easing the acute talent shortage*. IBM provides AutoAI to automate data science and AI lifecycle management.
80%
of data scientists’ time is spent on finding, cleaning and organizing data**
#1
Data scientist is the most promising job role in 2019***
Why does data science matter today?
With the volume and variety of social, mobile and device data, along with new technologies and tools, data science (03:43) today plays a broader role than ever before. Business considers data science and AI (06:13) to be a technology-enabled strategy. In order for data science to be effective, its full lifecycle not only must support traditional analytics, but it must also work in concert with modern applications. This means that the data science practice must evolve beyond routine, tedious tasks — as much 85% of a data scientist’s time is spent cleaning, shaping and moving data from place to place, often to feed machine learning. That leaves only a small percentage of time to find patterns and trends, to build models, to predict and forecast, and to interpret results.
Fortunately, there is relief. The latest development in modern data science is an AutoAI capability that automates the data preparation and modeling stages of the data science lifecycle. Now, not only can more data scientists use their specialized skills the way they were intended; but more businesses can benefit from data science, from prediction to optimization.
The big questions for data science
- Which next thousand customers will we lose and why?
- Where should we set up another kiosk or a new store?
- Which high-performing employees are we at greatest risk of losing?
- If we price products differently, will we save costs?
- Is my team offering the right things to the right people?
→ Watch AI playbook webinar (link resides outside IBM)
Success through data science
Here are some ways that businesses use data to gain competitive advantage:
Customer experience
A large provider of call center technology is using data to reimagine the call center experience and gain valuable insights from customers.
Preventive healthcare
An urgent care clinic turned to data scientists to help providers actively monitor and take preventive actions, improving patients’ survival.
Risk management
If a bank’s model is inaccurate, it can have devastating consequences. A major bank used data science to enhance risk mitigation and reduce model risk.
Safety and reliability
One of the world’s largest automobile manufacturers used data science to understand driver behavior and design better, safer, more reliable and personalized automobiles.
Customer loyalty
A major UK retailer is using data science to extract actionable insights, optimize promotions and increase the incremental spend of more than 15 million loyalty card holders.
Related products and offerings
IBM Watson Studio
IBM Watson® Studio provides tools to work more easily and collaboratively with data to build and train models at scale. It gives you the flexibility to build models where your data resides and to deploy anywhere in a hybrid environment, so you can operationalize data science faster.
IBM Watson Visual Recognition
IBM Watson Visual Recognition uses deep learning algorithms to analyze videos and images for scenes, objects and faces using Watson APIs. It helps accelerate time to results using pretrained models with visual content.
IBM Cloud Pak for Data
IBM Cloud Pak™ for Data helps you collect, organize and analyze data with a multicloud platform. It creates a trusted analytics foundation that makes data easier to access and helps you to scale insights on demand with AI.
IBM Decision Optimization
IBM Decision Optimization provides tools that use mathematical and computational sciences to help data scientists optimize machine-learning decisions. Decision optimization models can now be more easily deployed as a service inside Watson Machine Learning.
IBM Watson OpenScale
IBM Watson OpenScale™ tracks and measures outcomes from AI across its lifecycle and adapts and governs AI to changing business situations — for models built and running anywhere.
Deep learning
The deep learning service within IBM Watson Studio helps data scientists visually design neural networks and scale out training runs while paying only for the resources utilized.
IBM SPSS Modeler
IBM SPSS® Modeler empowers an enterprise to use drag-and-drop data science to accelerate time to value for production-scale models.
IBM Watson Studio Desktop
Watson Studio Desktop helps anyone get started, build models and deploy them from the desktop — anytime, anywhere.
IBM Watson Machine Learning Accelerator
Watson Machine Learning extends machine learning and deep learning model deployment in a multi-tenant, distributed environment.
Watson Studio Premium
By bringing predictive and prescriptive analytics together in a cloud-native data and analytics platform, IBM empowers an organization to accelerate time to value with the use of data science and AI. Watson Studio Premium for IBM Cloud Pak for Data helps deliver a substantial return on investment (PDF, 1.6MB).
Data science solutions for your industry
Healthcare
Uncover insights from clinical trials, patient data and more:
- Anticipate health problems and help save lives with alerts
- Reduce misdiagnosis
- Identify symptom patterns
- Remove risk from prescription medicine
Banking
Accelerate customer service with an innovative machine learning-powered hybrid-cloud app:
- Enable sales to give on-the-spot answers to loan applications
- Build a mobile credit-scoring app
- Delight customers and boost revenues
Manufacturing
Support autonomous vehicle manufacturing with machine learning technology:
- Train autonomous car sensors with machine learning
- Reduce cost of production at scale
- Make driverless cars more affordable and safer to ride
Computer services
Enable AI-assisted robotic process automation (RPA):
- Help employees focus on strategic activities
- Augment RPA investment with Watson Machine learning
- Accelerate RPA solution development by 20%
Media and entertainment
Deliver faster and deeper insights into TV audiences with machine learning:
- Accelerate insight into more complex audience data
- Allow fast, easy scaling as demand changes
- Focus on business enablement
Education
Leverage student data, curricula, surveys, testing and more:
- Support personalized planning, tracking, and data-informed advisors
- Identify learning gaps
- Increase student preparedness
Data science success stories
Integrating open-source data science tools while meeting security requirements
Managing model risk using data science and machine learning
Redefining the future of fan experience using data science and AI
Training and deploying deep-learning models for offline optical readers (OCR)
Streamlining the process of modeling and optimizing supply and demand
Accelerating customer service and controlling risk using rapid credit risk assessments
Fighting crime with data science: deploy the right resources to the right place at the right time
Keeping the wildlife population healthy using data science and machine learning
Making factories smarter by harnessing machine learning for quality management
O’Reilly: Agile AI for business
AI will result in a projected $13 trillion in new business value over the next decade. But there is no standard practice for implementing AI, and it's difficult to reduce the risk of project failure. Learn more about Agile AI practices and positioning your team to win, from experts Carlo Appugliese, Paco Nathan, and William S. Roberts.
What will we build together?
* “How to choose the right data science and machine learning platform”, Gartner Research, March 2019
** “What Data Scientists Really Do, According to 35 Data Scientists”, Harvard Business Review, August 2018
*** “Why data scientist is the most promising job of 2019”, TechRepublic, January 2019