Data Transformers Podcast

What is Responsible AI and Ethical AI?

‘

Episode Title : What is Responsible AI and Ethical AI?

Episode Summary:

Can Artificial Intelligence help society as much as it helps business? Is this the golden age for AI but only for certain sections of the society and not for all?  We need to establish ethical standards in dealing with artificial intelligence – and to answer the question: What still makes us as human beings unique?

Artificial Intelligence (AI) technology poses serious ethical risks to individuals and society. David Van Bruwaene explains how we can deal with these risks more effectively if we approach them using tools from applied philosophy.

Why is there a need for a company on ethical AI? (01:30): There wasn’t much focus on ethics and governance when David started working on AI. Most of the work was engineers digging into deep learning and hedge funds looking at sentiment analysis for purchase behavior but not much focus on whether recommendations were proper or not. David thought there needs to be a focus on discussion on the ethical implications of recommendations.

Who defines the fairness of AI (08:00): Finance deals with model risk management is procedurally handled especially with what is laid out by governments. Similarly, bias risk can be handled by rigorous documentation, reproducibility, auditability. Example of gaps in resumes and not considering gaps because of pregnancy for example. First degree is identifying risks with data scientists.

Who is accountable for Responsible AI?(11:30): Board should be ultimately accountable. . Need separate test data sets & Challenger data models. Model risk report should be an outcome. Companies should have Chief Model Risk officer.

Applicability of Fairly AI framework (13:00): Framework is horizontal. Problem is budget and willingness. Different departments will also have risk levels. Example Marketing is considered low risk whereas loan departments will be considered high risk.

What can you do with historical Bias in Data (17:00): There may be technical fixes but they could be costly and time consuming. First look at data pre-processing steps. The better way to deal with is in-prcoessing steps of removing information about protected status.

Should there be more regulations in AI (22:00): Regulations come when business fails. All big companies are investing in Responsible AI.

AI implementation and success in AI (25:00): Financial institutions are investing heavily. Insurance and healthcare are also investing. There are plenty of roadblocks starting with training data. Access of training data to outside consultants when they are brought in.

USEFUL LINKS

Twitter:

https://twitter.com/DataTransforme2

https://twitter.com/peggy_tsai

https://twitter.com/rkdontha1

LinkedIn:

https://www.linkedin.com/in/davidvanbruwaene/

https://www.linkedin.com/company/69241922

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.

Collaboration is key to formulate and implement Data strategy

‘

Episode Title : Collaboration is key to formulate and implement Data strategy

Episode Summary: A data strategy and data policies resulting from the strategy should be a collaborative approach. Diane explains the process in which London Stock Exchange Group (LESG) went through the survey process to collaborate with stakeholders to get their feedback and in the process elevate the level of data literacy. The episode also covers how Diane started as a data modeler but took advantage of the opportunities thrown at her and worked hard to become a CDO. With respect to trends, Diane emphasized the need to take advantage of latest tooling to improve visualization in analytics. Finally, Diane discussed her perspectives on being a woman in technology field and her advice to others.

Formulating a community driven data policy (02:00): Diane set out a survey with 3 parts and received 70% return. First set of questions was about the awareness of policy and this actually made respondents more aware of policy. Second part asked them to rate the policy from 0 to 5. The 3rd part asked them to respond and the comments were coded. With 600 comments, major themes emerged. So a CDO should base their decisions on the feedback of people who are going to be impacted.

Diane’s professional journey from a data modeler (10:00): Success comes through hard work and opportunities. With Freddie Mac and Fannie Mae, there were many opportunities around the 2000s to fix many issues and keep the business running. And that opened up many opportunities for Diane. And the second opportunity outside of Fannie Mae to start a department from scratch. The opportunity involved lots of travel including international travel.

Advice to aspiring individuals (14:24): Hard work is given. Networking is important. Keep updated on skills. Take advantage of the training. Know what not to learn. End of the day, it’s each individual’s job to be accountable and responsible for their own development. 

Current and future trends (16:00): Now organizations have a lot of tooling options. As visualization is a very powerful way to help make decisions, any tooling that helps with visualization is key.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

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 : From a Data Modeler To Chief Data Officer

Episode Summary:

This episode with Diane Schmidt is an inspiring story of how to grow in a data career. Diane started her journey as a data modeler and gradually grew to become the chief data officer of London Stock Exchange. Diane is a student of Data and the episode covers all aspects of data analytics, data governance, and data strategy. 

Consolidation in 2020 to focus on execution in 2021 at LSE (03:00): Not all organizations are all at the same state on digital transformation and London Stock Exchange is ahead on a digital transformation but needed someone to put a wrapper around all initiatives. Diane joined LSE to do that.

One step backwards but 10 steps forward (07:30): Took a job as a data modeler and a pay cut back in the ‘90s. But that paid a lot of dividends over the years. 

Data in the backburner to the forefront now (09:00): Management used to forget to invite data management folks in the past as they are the backoffice. Now, Data is front and center. People really acknowledge the importance of data nowadays. 

Why technology takes initiative instead of business (12:00): IT takes leadership when there is a leadership vacuum on business/data strategy.  Ideally, technology should be solutioning to the business strategy and business requirements. But when there is a vacuum/gap on biz strategy, technology takes the lead with good intentions. 

CDO as a Chief Requirements Officer (CRO)(14:00):  CDO journey should be seen as a maturity journey. Depending on where an organization is, different skills of a CDO are needed. It may not be the same for every prg or the same stage. Dr. Richard Wang and Dr. Lee’s article lays out a good structure for looking at this.

Similarities & Differences in Data prep Vs. Data Analysis across companies (19:44): Similarities across different companies that Diane worked are about data prep (70 to 80%) time versus data analysis (20-30%). So the challenge is about efficiencies and effectiveness of data accountability. People, processes, technology challenges are the same. The differences are about the actual data (mortgage vs. stocks).

Data student even as a CDO (22:37). Diane got her Collibra data steward certificate recently when she was in a senior position. Reason is that Diane is a data student, humble to know she doesn’t know everything about data, and eagerness to know about her audience.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

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.

From Liberal Arts to Data and Analytics strategy advisor

‘

Episode Title : From Liberal Arts to Data and Analytics strategy advisor

Episode Summary:

The episode covers the professional journey of Jill Dyche from a liberal arts background to data strategy consulting founder to being an author of 4 books on data and analytics. The theme of her work as well as the books has been to extract the business value of data and technology. Jill has been able to combine both her passions of shelter dogs and analytics and apply analytics to deriving insights to increasing the adoption of shelter dogs.

Starting with a Liberal Arts degree (01:15): Starting as a technical writer at Honeywell has served Jill very well. Writing helped Jill become proficient in technical matters but her liberal arts education helped her become a good communicator of technical subjects.

Art of Storytelling (04:30): Storytelling was hot 4 years ago. There was this movement to make all data scientists storytellers tellers. But data science is a team effort. It is a collaborative effort. So if a data scientist is great at data science and having another member who can communicate well is OK.

Baseline consulting start (07:00): Came out of Teradata and decided to do something with data. Started Baseline Consulting to focus on data and analytics. And there were not many people focusing on analytics at that time. It was a fun ride.

Combining passion with profession (11:15): Jill is passionate about rescue dogs and finding a place for their rescue dogs. By digging into data, Jill and her team found insights about why people leave dogs and why certain dogs won’t be adopted. Interestingly, as they found trends, the trends changed as people acted on the trends. Today’s trends will be tomorrow’s outcomes.

Business value of Data & Technology (18:00): The earlier the organizations start collecting data and analyzing the data, the better off they’ll be. Jill’s books have focused on the business value of data & technology. The ebook that Jill is focusing on currently touches animal welfare.

Organizational development and culture (20:00): The ever-green topic for future would be Organizational development and organizational culture. Data and Analytics are great platforms but nowadays most senior management is grappling with having the right organizational design to adapt to technologies.

Neuro-diversity (22:00): Gender diversity, racial diversity, and sexual-orientation diversity all lead to diversity of ideas and discussion and that is neuro-diversity. And the diversity of ideas and discussion from different life experiences makes the professional experience also richer. And there are a lot of positive case studies.

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.

The value of Strategy and Data Culture in organizations with Jill Dyche

‘

Episode Title : The value of strategy and data culture in organizations with Jill Dyche

Episode Summary:

Data mature companies tie their corporate objectives to data and analytics initiatives. It is no longer sufficient to focus on revenues and costs but leaders are looking at enhancing brand value with analytics. Given the higher purpose of data and analytics, strategy and data culture are critical in organizations. With the advent of AI, organizations need to reinvest in data and skillsets to forge ahead.

Data Quality (04:00): Companies don’t have the luxury of loading up data and figuring out later as in the past. Use cases have become central to the data and what data to access and analyze..

What (data) mature companies do well (05:30): Maturity is in delivery. True maturity is in cadence of actual value delivery. Ideally link data and analytics to the corporate objectives. It’s no longer sufficient to just increase revenues etc. but to enhance the brand and further the corporate strategy. Data and Analytics have a higher purpose than before.

Impact of Artificial Intelligence (08:30): Artificial Intelligence will impact organizations both internally and externally. Internally, companies need to reinvest in data and reinvest in skillsets. Data is its own specialty so companies with data offices and data management are ahead to take advantage of AI.

Culture eats strategy for breakfast (11:30): Company’s culture to a large extent defines the success of data initiatives in their organizations. One of the questions to ask to assess data culture is to have them describe a project that is universally successful at your company. If the answer to this question is very diverse from different stakeholders than it may mean that the success metrics are not clearly defined.

Start of strategic consultations (15:30): Companies typically engage Jill on 2 fronts (1) Companies have hit the wall and seek advice on how to move forward (2) Executive staff needs training or organizational structure because of an inflection point. Typically a ‘C’ level executive approaches Jill for some external injection to unravel internal hiccups.

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.

Three Most Important Legs of Information Management

‘

Episode Title : Three Most Important Legs of Information Management

Episode Summary: The episode starts with a focus on the 3 legs of information management which are data quality, data sensitivity, and master data management. Later, the discussion focuses on advice to aspiring data management and data governance professionals about most resourceful web sites, conferences and certifications. The episode concludes with a discussion on data governance best practices which start by identifying top 10 reports.

3 legs of Information Management (02:28): There are 3 legs of Information management and they are (1) Data quality (2) Data sensitivity (3) Master Data Management. On top of 3 legs are data governance and data stewardship.

Data Governance in the cloud (06:45): Data governance in the cloud has not been resolved yet. Companies are still struggling with the concept of putting data in the cloud and managing metadata while doing so.

Resources for data management/data governance (08:48): (1) Dataversity courses (2) Data Management Book Of Knowledge (DMBOK) has a great framework (3) Open source data management are great resources. CDMP is a great certification. Enterprise Data World (EDW) is a great conference as well.

Data Governance best practices (16:23): (1) Start with Top 10 reports (2) Focus then on data sources, data integrity that make up those reports. (3) Start governing the critical data that drives these reports. And keep going to the next level of reports.

Resources mentioned in this episode:

Dataversity.Net

Dama.Org

Data Governance with a focus on Data monetization with Kevin Ladwig

‘

Episode Title : Data Governance with a focus on Data monetization with Kevin Ladwig

Episode Summary: Organizations used to ask what and how when it came to data governance and now they have progressed to asking why. To a large extent this is driven by an intent to monetize data. Gradually we are also seeing data monetization officers in organizations. In order to be really successful with data governance, organizations need to look at capabilities from tops down and also assess current state from bottom up.

Why instead of What & How (02:00): Organizations are asking why instead of what and how now. Customers are Finance nowadays.

Data breach cost (05:00): All regulations have a cost associated with it. State regulations and European regulations etc. have costs associated with data breaches.

Data monetization (10:36): Data monetization involves Increase revues, reduce costs, mitigate risks.

Approach data from capabilities (14:30): In order to succeed in data governance, organizations need to come top down from capabilities to set a structure but also need to come from bottoms up (products, sales etc.)  to understand the data landscape. Assign stewardship to capabilities.

Data Governance practice (20:37): Any engagement starts with a current state assessment : Enterprise data model, Enterprise reports, Followed by scoring and propose next steps

Data culture (23:53): Getting an organization to be data aware requires lot of creative thinking.

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.