Data analytics | Interview with Dave Schrader

Dr. Dave Schrader is an expert in Data Warehouses, Business Intelligence, Data-Driven Strategy and Former Marketing Director for Big Data at Teradata Corporation.

Dr. Dave Schrader is a popular conference speaker and has assisted numerous Teradata customers in understanding how to strategically use both traditional and big data to create deeper customer analytical insights. He collected best practice case studies for improving call centers, the web, as well as industry-specific points of contact like retail stores, banking ATMs, and mobile devices.

He joined Teradata in 1991 and held various positions in both Engineering and Marketing. Previously, he held engineering management positions at Servio Logic where he led federated and object-oriented database projects.

Schrader holds a Ph.D. in computer science from Purdue University, has published in the areas of customer management and business intelligence, and is a popular worldwide speaker at conferences on how companies can gain a competitive edge by using technology.

How do you define Big Data and which would you say are more important today – traditional or Big Data?

Big Data is tough to define since it’s been stretched to mean almost anything. The “Vs” – volume, variety, velocity, veracity – are one set of terms, but not that useful since even traditional data can add up to huge volumes and real-time velocity from source to the data warehouse can be seconds instead of batched overnight. These scales are relative.

I tend to focus on “variety” as the key differentiator – capturing data types that contain useful information (like voice), transforming that into traditional data (like text), and combining that with other traditional data to improve predictive models.  My quickest example to explain the concept is someone yelling and cursing at a call center agent. It’s possible to digitize the voice call, then analyse it for volume and do word spotting for angry words. Those scores can be added to attrition models.  So overall, I think of Big Data as an addition to what people already do with traditional data, not a replacement.

How would you describe your work with companies in the field of data analytics? How and where do you help them?

I help them understand what they could be doing with data (big and small) to make better business decisions throughout the enterprise.  I think data analysis improves three functional areas. Sometime I help the customer-facing side of the business, showing them how to gather and analyse customer clues to build better acquisition, growth and retention campaigns.  Sometimes I work with corporate management to build systems to help run the business better with advanced Key Performance Indicators (KPIs) and real-time dashboards to monitor what’s happening and get on top of issues faster.  This can include working with division heads or financial analysts on corporate performance.  The third area is the back office which can include supply chain analytics as well as B2B (business to business) workflows to use data to improve productivity and increase product or service quality or decrease defects.

People who have attended my workshops and talks always value most the fact that I have so many case studies (more than 300) which show exactly how other companies have achieved success with data analytics.  I also talk about the pitfalls so people don’t repeat them.  It’s a good way to help attendees accelerate the adoption of analytics when you can show people successes to emulate and failures to avoid!

How can companies get business value from their data and who inside a company should do it?

Value comes from deeper insights. Often I begin with measuring “what you are doing now” and then show how other companies have achieved better results through deeper and faster insights. Using my 3 categories from above, the value for the Chief Customer Officer is in better models for customer acquisition, growth and retention. The value for Executives or CFOs is better insights into the levels of productivity and profitability as well as an appreciation of how speed of seeing problems or opportunities faster than others yields a competitive edge.  For the Supply Chain Officer, identifying production problems, understanding variances (Six Sigma applications), as well as improving speed of production and measuring how upstream partner supply chains impact your business are all areas where data analytics can help.

What would you say is the biggest challenge companies face today when it comes to Data Analytics?

Moving up the maturity curve requires leadership first and foremost.  That opens the doors to a data-centric culture, a “making-decisions-by-the-numbers” culture which is a key driver of profit.  Beyond leadership, I would say that most companies have pockets of good analytics capabilities but they are often not enterprise-wide. Those companies thereby miss opportunities for cross-functional, holistic end-end problem-solving compared to their competitors.

Which of the current trends in Data Analytics field are of great interest to you? 

I think we are finally past the over-hyping of Big Data and now can focus on the incremental value, the economics of data collection and use.  For example, going back to my opening example – how much better are those retention models because you can compute and add new scores for a customer who is angry? 5%? 25%?  This helps justify the investment.

The areas I find of most interest are time series analytics and better visualizations that help analysts communicate better with their business partners.  Being able to “see” patterns is a key technology area that can accelerate insights as well as improve communication of insights so others in your company take action.

And where is most of your work focused today?

Since I’ve retired, I’m giving a lot of talks on college campuses, mostly in the Business Schools but sometimes for Computer Science or Statistics departments.  I give Big Data talks, sometimes Humanitarian Uses of Data talks (students are idealistic!), but lately most of them have been on Sports Analytics.  Already in 2017, I’ve given 45 talks at 20 universities to more than 1000 students, faculty, coaches and trainers.

This is a very hot area, with many opportunities to apply what we know from business to sports.  There are 3 areas.  Fan analytics is much the same as CRM – what factors drive ticket purchases or season ticket renewals?  What’s happening on social media that impacts the business operations of a sports team?   For the “back office”, the focus is on team and tactical operations for coaches.  What combinations of players are best against this particular opponent?  What plays will work or not work?  And the 3rd area is strength/fitness/conditioning, working with trainers. How hard to practice?  How to predict player injuries?  What’s the impact of sleep and nutrition on player performance?

I find all these questions to be interesting and since pro and (in the USA) college teams are starting to hire full-time analytics people, helping students build skills and do capstone projects in the sports area is my current passion.