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Guest name: Dr. Bonnie Holub
Guest title: Senior Data Science and Analytics Leader, Award-winning Storyteller
Organization: TeraData
Summary: In this episode, Bonnie emphasized the importance of storytelling with data to make analytics impactful in organizations. One of the challenges organizations deal with is implementation and operationalization of analytics for speed and scale. Bonnie talks about the need for focus in that area. Bonnie also talked about her experience in climbing up the ladder to be one of the few senior executives in the tech world. Bonnie also described the ways to balance business objectives and technology implementations.
Youtube link: https://youtu.be/w7ARTlqRF5Y
Topics discussed in this episode:Role and responsibilities (starting 2:13): Practice head of Data Science in North America with principal and senior data scientists reporting from Chile, North America, Canada etc. Cover many industries such as financial services, healthcare, telecom, and tech.
Leading and trailer adopters of technology (starting 7:07): Heavily regulated industries such as financial/insurance/healthcare are somewhat trailing compared to technology and telecom. And there are companies in each industry whether be manufacturing or retail that are trailblazing even if the industry may be trailing.
Pandemic impact on organizations (starting 9:34): BOPAS or buy online and pickup at store saw a huge increase. Companies like instacart leapfrog quickly.
Key success factors in data science (starting 11:54): Data integrity is key. Operationalization is very important. Upto 80% of a data scientist’s job may be data munging/data wrangling to make sure that data integrity is in place.
Day to day roles of senior data executives (starting 13:51): (1) Conversations with clients (2) Being a player coach working with colleagues (3) Working with partners for an integrated approach to solution
Bonnie’s professional journey (starting 20:04): Started with Honeywell labs, had an opportunity for them to pay for Ph. D in Artificial Intelligence. Later had an opportunity to work with a startup help them establish. Worked with a health insurance company. After few more stints ended up with Teradata. Great thing was learning at every step of the way.
Importance of storytelling with data (starting at 24:49): Started in high school by competing in a tournament. In the professional world, was initially apologetic about storytelling. But after attending a professional seminar and a sales training, realized that all of them were talking about storytelling. Connecting the outcomes with helping families get better coverage or finding better financial support is more meaningful.
Significance of being a senior female leader (starting 29:19): Interesting story that Bonnie’s journey started with her attending a college which was an all male college until the prior year. So most of the experience was working with men from the beginning. In the corporate world, she got opportunities to speak and thought to herself that she has these superpowers because of who you are and what you are capable of. Have been exposed to supportive environments mostly.
Trends in the industry (starting 35:10): Analytics has taken off in the last few years. Example is that 2017 Superbowl was sponsored by an Analytics company. Data literacy is growing. Focus on operationalization is very important. And the need to scale is also very important.
Resources mentioned in this episode:Books: Michael Lewis’s Big Short, Moneyball; Brent Dykes : Effective Storytelling
Data Transformers Podcast
Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.
Episode Title: Why this is the last podcast on Data that you should listen to.
Episode Summary: What is the need for one more podcast on Data? How will the Data Transformers podcast be different from other podcasts? Who is the podcast meant for? Who is being interviewed? This episode goes into all the What, Who, Why, When, Where, and How of the podcast.
Youtube link: https://youtu.be/z1fVM6YjFCM
Topics discussed in this episode:Technology Trends - Ramesh (starting 1:22): (1) Intelligent Transformation with Artificial Intelligence as main front. (2) Focus on decisions driven by analytics (3) Drive towards a data literate organization
Technology Trends - Peggy (starting 3:30): (1) Focus on data privacy (2) Need for a good data governance program (3) Democratization of technology in general and analytics in particular
Challenges (starting 7:43): Organizations refusing to accept that automation is needed to deal with processing of large volumes of data. Industry now has tools to efficiently automate many processes such as data discovery, data classification etc. Regulators and auditors are forcing companies to adopt automated processes. There is no going back.
Pragmatic approach to solve business problems using technology (starting 12:37): Find a tangible business problem to solve in a department, introduce technology to solve there by learning the process and challenges of scaling it.
Skills upgrade for employees (starting 15:48): Organizations also need to take into consideration that employees feel the pressure to upgrade their skills and knowledge. If organization is not embracing new technologies, employees will be forced to look elsewhere. That is also another factor to embrace new technologies.
Concerns about adopting new technologies (starting 19:43): Some industries (ex: financial svcs) are hesitant to adopt cloud computing as they are concerned about exposing data especially as it relates to regulation and compliance. Similar situation in some high tech industries as well.
Data quality issues (starting 21:02): One of the most pressing and important items is the data quality. As data is the underpinning of any technology be it analytics or AIML, organizations need to focus on data quality. Unfortunately many don’t take it seriously.
Low hanging fruits for organizations (starting 24:44): Organizations need to find low hanging fruits to justify investments in data quality. Some low hanging fruits are process automation. The ROI is easily justifiable in process automation. Of course, regulation/compliance can also drive efforts to clean up data.
Where to start? Tops down or bottoms up? (Starting 27:52): Should data strategy be driven tops down or bottoms up? Tops down is a long drawn out process but much more enforceable with proper education and training. A bottoms up approach from a lighthouse project can also be effective in showing results quickly and using that to spread in the organization.
Data Transformers Podcast
Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.
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