Plugging Analytics into the Business

Black box algorithms. Test data with test results. Predictions with possibilities only. All of these are reminders of analytics teams that have not yet plugged into the “business.” They appear to be making progress, or at least they are busy, but the results are not tangible. They have not yet created value that can be measured and replicated. Although predictive analytics is a “must” for nearly every business today, there are few companies really putting predictive analytics to work for them.

Why are so many organizations developing predictive capabilities, but haven’t put them to use with their sales, marketing or operations people? Are companies really getting value from statistical predictions? If they are, how are they measuring that value and showing it on their bottom line? If not, what are they doing to close the gap between data science and everyday business?

In this session, we’ll evaluate three areas that are most neglected and hardest to deal with when putting predictive analytics to work and show you how to get your predictions used.

  1. Trust.  Getting the users to trust the prediction of an algorithm is fraught with biases. “That prediction can’t be right because the data is all wrong.” “I don’t believe that customer will default next month; I am best friends with the CIO and I haven’t heard a word. Trusting the outcomes of the predictions is the first barrier to overcome.
  2. Teaching.  Teaching sales and operations people to use the predictions can be your secret to having successful deployments of systems that use your predictions. Helping users understand the context around the predictions is essential.
  3. Technology.  Automation and self-service are the keys to use of predictive analytics. It must be easy. It must produce results. IT is the it!

Photo of Theresa KushnerPresenter: Theresa Kushner, partner, Business Data Leadership

Theresa Kushner, partner in Business Data Leadership, comes with over 20 years of experience in deploying predictive analytics at IBM, Cisco, VMware and Dell.  She has co-authored two books: Managing Your Business Data from Chaos to Confidence with Maria Villar and B2B Data Driven Marketing: Sources, Uses, Results with Ruth Stevens.

Analytics in a Business-to-Business Environment – Collaborating on the Solution and the Narrative

The world of Analytics in a Business-to-Business (B2B) environment is fundamentally about helping someone else do their job better — We help Sales sell more, Finance forecast more accurately, procurement to purchase more effectively and administrative teams to be more efficient. In this arena, excellence comes from the ability to identify the true underlying problem, collaboratively work on a solution, present the narrative of what we’ve learned and see the solution through together. In this talk, we’ll discuss how a B2B Analytics team can bring its expertise to make a big impact – bringing core expertise, getting information quickly and supporting the long journey along the way. At each step in the presentation, we’ll look at examples in pricing optimization, job duration prediction and more.

Photo of Neil BiehnPresenter: Neil Biehn, vice president of business analytics, Siemens Healthineers

Neil Biehn is the vice president of business analytics at Siemens Healthineers and leads a team of data scientists and predictive analytics specialists who help business partners make better decisions through data intelligence, dashboards, and data science. Prior to Siemens Healthineers, Dr. Biehn spent 14 years at PROS Holdings, Inc. where he led a team of scientists that designed and innovated the algorithms behind airline, hotel, car rental and cruise ship pricing models, as well as negotiated B2B pricing methodologies.

Dr. Biehn earned a Ph.D. in Operations Research and Applied Mathematics at North Carolina State University and is a published author in a variety of formats.

The AI Talent Race

Part One: Porter’s 5 Forces drive the talent market in data science and machine learning. The supply and demand explanation is too simplistic for businesses to act on. Porter’s 5 Forces more completely describe the governing dynamics of the talent market. The threat of substitutes such as automated ML tools is low. Bargaining power of most employers is low and primarily driven by attractive compensation packages. Bargaining power of talent is high, since most have a current job and multiple offers. The number of new data scientists entering the field is low.

Part Two: Creating a pipeline of talent requires business to address each aspect outlined above. Platforms, tools, automation, etc. need to be selected to improve productivity rather than as a replacement. Outreach through open source projects, hosted hackathons, content sharing, convention presence need to be leveraged to increase the attractiveness of a company’s openings to improve their bargaining position. Businesses need to create internal training initiatives to build a farm system of data science and machine learning talent to address the overall shortage.

Photo of Vin VashishtaPresenter: Vin Vashishta, chief data scientist, strategist, speaker & author

Vin Vashishta brings data science and machine learning products to market. He has spent the last 22 years taking products off the whiteboard and into production. His career is one-part strategy and two-parts technical expertise. Vashishta speaks ground truths to senior leaders so they can drive revenue with emerging technologies.

What’s so Special about Healthcare?

Recent decades have seen data and analytics technology rise to preeminence in one industry after another. The use of so-called Big Data to provide insight into impending changes in consumer behavior, to effectively stratify product offerings toward truly mass customization, and to uncover patterns hiding within already well-understood areas of technical and business activity shows no signs of slowing. One often hears healthcare described as a “lagging” industry as regards exploitation of advanced analytics. The recent entrance of the likes of Apple, Amazon, Berkshire-Hathaway and Facebook into a healthcare landscape populated by health systems, pharma’ and bio’ manufacturers and insurance plans that are already generating 20% of the US GDP suggests the industry is ripe for further analytics development.

In this presentation, we’ll take a look at some of the questions healthcare organizations need to answer, how advanced analytics can support health improvement, and how healthcare might be fundamentally different from some other industries in which advanced analytics met with early success.

How is healthcare special? Perhaps it is not so much in that it is a lagging industry, as that it is waiting for analytics to catch up.

Photo of Stephen BlackwelderPresenter: Stephen Blackwelder, Ph.D., chief analytics officer, Duke Health Systems

Stephen Blackwelder is Chief Analytics Officer at Duke University Health System and leads Duke Health’s Analytics Center of Excellence. The Center is the shared services information and analytics hub for the health operations and clinical data Duke relies upon daily in its care delivery, health data science, research and education missions.

As the executive responsible for making a foundation of actionable information available throughout the enterprise, he leads teams supporting a flexible and responsive data-as-a-service infrastructure incorporating robust structured and unstructured storage, operational reporting from Duke’s Epic EHR, and clinical operations-, clinical research- and financial-focused solutions teams. The Analytics Center of Excellence uses an innovative agile service delivery model to simultaneously advance project and infrastructure work in close collaboration with stakeholders and with the support of an extensive and engaged governance structure. Partnerships include work to deliver Duke data science-developed AI solutions at the bedside, addressing the big problems in modern medicine.

Dr. Blackwelder has previously held executive leadership roles in accountable care, population health, and health insurance organizations. He designed and deployed one of the industry’s first secure cloud-based data warehouses to be comprised of information from the patient’s “neighborhood care community” alongside claims and clinical information, lab results and pharmacy scrips.

A respected thought leader with 30 years in the analytics industry (20 of those in healthcare), Dr. Blackwelder recently joined forces with Duke’s Fuqua School of Business to help design and deliver a graduate-level health analytics curriculum.

Driving Analytical Innovation and Cultural Change in Large Organizations

Photo of Christian CosnerPresenter: Christian Cosner, head of Advanced Analytics for the North American Retail and Mortgage Bank, Citi

Christian Cosner is the Head of Advanced Analytics for the North American Retail and Mortgage Bank at Citi where he is culturally and structurally transforming the way that customer insights are generated and translated into business impact.

Before joining Citi, Cosner spent two years with Google leading business analytics and operations teams in their Localization/Internationalization department to drive customer insights, operational efficiencies and expense management best practices. Prior to Google, Cosner drove innovation and cultural transformation initiatives at Bank of America, Kinder Morgan and the U.S. Army.

Cosner is a veteran with eight years of active duty service with deployments to Iraq and Kosovo. He attended the United States Military Academy at West Point and earned his MBA from the Fuqua School of Business, Duke University.