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Breakthrough experiments in data science: Practical lessons for success

Leading firms are integrating data science capabilities within their organizations to capture the untapped potential of data science as a source for competitive advantage. Yet, many enterprises are challenged to successfully integrate these capabilities for sustained value and to measure its worth for the organization. This analytics study conducted by the IBM Center for Applied Insights uses practical advice from those seeing the benefits to establish a proven success formula for integrating a data science capability within your organization.

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Breakthrough experiments in data science: Practical lessons for success

  1. 1. © 2015 IBM Corporation Breakthrough experiments in data science: Practical lessons for success November 2015
  2. 2. © 2015 IBM Corporation2
  3. 3. © 2015 IBM Corporation3 © 2015 IBM Corporation We asked data scientists and their executive colleagues…
  4. 4. © 2015 IBM Corporation In their own words… 4 “When you ask me what the value of data science is, it's almost, like explaining the value of water to a fish.” – Chief Data Scientist, Media “We have an asset: it’s the data. And what you do with that data dictates whether you’ll be differentiated in the future.” – SVP of Digital & Direct Business, Retail “We're going to help the business go to new places that it hasn’t yet even thought of going.” – Chief Data Scientist, Biotechnology “If we didn’t have a data science capability we would lose money.” – Global Director of Data & Analytics, Manufacturing “The business realized that, nowadays, we cannot be competitive if we are not data-savvy enough.” – Senior VP, Banking
  5. 5. © 2015 IBM Corporation Leading organizations are using data science to drive up revenue… 5 Increased business year over year Grew top line and saved on bottom line Reached expanded group of customers Developed a new predictive analytics platform to offer preapprovals in real time Employed modeling techniques to improve online promotion targeting and spend allocation Assembled location-based analytics models to better allocate resources across sites “With our preapprovals, we increased our business 40% year over year. And the losses are about a third to a quarter of what the industry is seeing.” – VP of Credit Risk Analysis & Econometrics, Banking “These are opportunities in the millions, either in terms of driving the top line, or just getting smarter in the online marketing space and spend allocation.” – Chief Analytic Officer, Travel “We’re able to increase revenue because we were now reaching people that we wouldn’t have otherwise gotten to. And we were able to minimize the drain from other competing agents.” – Lead Data Scientist, Insurance
  6. 6. © 2015 IBM Corporation …and are using data science to improve efficiency and effectiveness 6 Saved millions by reducing churn Found new patterns in holiday shopping Forecast effects of weather on sales Built an online predictive analytics tool that helped reduce churn and better direct resources Combined trends from different data sources to enhance inventory management Created a new predictive model to improve supply chain management of stores “Well, certainly the churn model is a big success – between $11 and $16 million dollars in savings per year has been a lot of proof that what we do works.” – Chief Data Scientist, Telecom “We were able to see that people were shopping earlier than ever before. We needed to have more toys on the shelf by October; November was not good enough.” – Director Customer Service Systems, Retail “I'm able to model the effect of a hailstorm on a set of stores that may sell out of shingles. We’re getting into other data in order to enhance some of this bottom line functionality.” – Data Scientist & Advanced Analytics Architect, Retail
  7. 7. © 2015 IBM Corporation What can we learn from how forward-thinking enterprises use data science capabilities to extract value from data? 7 © 2015 IBM Corporation
  8. 8. © 2015 IBM Corporation Those already seeing the benefits can offer practical advice to their peers around how to: © 2015 IBM Corporation8
  9. 9. © 2015 IBM Corporation9 © 2015 IBM Corporation Infuse data science into culture: Smarter, faster decision making
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  11. 11. © 2015 IBM Corporation11 Set expectations “We had to change the culture and say if you don’t bring data, don’t bother bringing a topic up. We established very firm criteria up front: show me the research you did and the data that supports why you believe this to be true.” – Global Director of Data & Analytics, Manufacturing Automate decision making “We’ve tried to integrate some of the business rules into the database and made some of the decisions for them so they don’t have to do the heavy lifting on it. Analytics decision management is definitely a key tool.” – Chief Data Scientist, Telecom Offer easily consumed data “Not only have a product but have a product that is easily viewable and consumable by the business. I think that that’s an absolutely essential thing.” – Lead Data Scientist, Insurance Infuse data science into culture
  12. 12. © 2015 IBM Corporation12 © 2015 IBM Corporation Design a data science capability: Structure, skills and support
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  14. 14. © 2015 IBM Corporation14 Centralize the core with distributed support “We have different verticals assigned to different areas of the business within one central data organization. This works pretty well because then all of the data scientists are together sharing one kind of core research philosophy and then using that in their specific areas.” – Lead Data Scientist, Insurance Pay based on partner’s success “We succeed when our business partners succeed so I even remunerate my people based on their partner's successes, not just their contribution.” – Chief Data Scientist, Media Collaborate with IT “Setting up data scientists alone will get you part of the way there but getting the software engineers and technical people to help you implement is absolutely essential.” – Lead Data Scientist, Insurance Design a data science function
  15. 15. © 2015 IBM Corporation15 © 2015 IBM Corporation Equip with the right technology: Tools, accessibility and efficiency
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  17. 17. © 2015 IBM Corporation17 Consolidate data sources “It’s much simpler because we have a single data source instead of having it spread out over multiple data sources all over the company. Instead of having to go out and request access from 16 different people, you can now get all the access you need.” – Senior Director of Data Sciences, Insurance Build data cleansing into tools “Our warehouse team put together anomaly detection in the warehouse to actually watch the data. We would find date columns with six different date formats, or there would be six months worth of missing data. It's really a hygiene issue.” – Principal Data Scientist, Media & Entertainment Invest in cloud-based solutions “I'm a big believer in moving a lot of this stuff to the cloud, and then in house, let the people focus on their core competencies, which is data analytics, not the maintaining and junk that goes along with the infrastructure.” – Director Customer Service Systems, Retail Equip with the right technology
  18. 18. © 2015 IBM Corporation18 © 2015 IBM Corporation Showcase your results: Targets, metrics and awareness
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  20. 20. © 2015 IBM Corporation20 Focus on high ROI problems first “Senior management is looking for dollar savings or revenue acquisition improvement, those types of things. So, you have to pick the projects with the best ROI first.” – Chief Data Scientist, Telecommunications Establish control groups to measure value “We're running experiments. We've got control stores, and we've got test stores. We're looking at the difference between the two across the variety of different things we're trying in the world of data science.” – Data Scientist & Advanced Analytics Architect, Retail Drive awareness through internal campaigns “We set up kiosks and roadshows to market what we do. We'll set up a couple of tables in the cafeteria of core buildings and have videos of the types of projects we've worked on, especially ones that have some jazzy output.” – Chief Data Scientist, Biotechnology Showcase your results
  21. 21. © 2015 IBM Corporation21 As you establish your own capability…  How committed are your senior leaders to basing decisions on data, not intuition?  Can the business easily understand and act on your data science insights?  Do you have one centralized capability? with all the skills you need?  How well do you collaborate with the business? with IT?  Have you enabled your capability with the right tools?  How accessible and trusted is your data?  How effectively are you driving awareness and adoption?  Do you have metrics in place to prove value?
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  23. 23. © 2015 IBM Corporation For participant companies, big data and analytics are a significant area of focus and investment relative to other business imperatives Industries include banking, education, retail, wholesale, telecommunications, manufacturing, insurance, healthcare, pharmaceuticals, travel, finance, biotechnology and media & entertainment Our research highlights practical advice from data leaders for integrating data science capabilities within your organization 23 Qualitative study 22 in-depth phone interviews with US- based business leaders and data scientists of companies that have been successful at integrating a data science capability within their firms
  24. 24. © 2015 IBM Corporation Learn more about leading data and analytics 24 Data Leaders: Re-imagining the business of data The transformative power of data and analytics is being harnessed by organizations to make smarter, quicker and more analytically-informed decisions. At the helm of this transformation is the Chief Data Officer – a strategic leader who employs data and analytics to create tangible business value. The IBM Center for Applied Insights spoke in-depth with executives to learn how CDOs are making a difference within organizations. Download the study (403KB)  IBMCAI Blog – CDO THINKLEADERS CDO strategies for success in a new era of big data and advanced analytics  Big Data & Analytics Hub for CDOs Discover more:
  25. 25. © 2015 IBM Corporation25 © Copyright IBM Corporation 2015 IBM Corporation New Orchard Road Armonk, NY 10504 Produced in the United States of America December 2014 IBM, the IBM logo and are trademarks of International Business Machines Corporation in the United States, other countries or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with a trademark symbol (® or TM), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. Other product, company or service names may be trademarks or service marks of others. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided.