Data Transformers Podcast

Episode Title :

Cybersecurity and AI working together to make systems safe with Dr. Madiha Jafri

Episode Summary:

Artificial Intelligence and Cybersecurity are large and complex domains each by themselves. When you combine both of them, it could be overwhelming. Dr. Madiha Jafri, Associate Fellow at Lockheed Martin for AI & Cybersecurity navigates these domains to make systems safer. In this episode. Dr. Jafri articulates how AI can help speed up cybersecurity threat detection. She also talked about her journey starting from cryptography to nanotechnology to AI. The discussed addressed challenges such as data engineering, relevance of Ops, and navigating the plethora of technologies to find that needle in a stack.

02:00: Madiha role at Lockheed Martin as an Associate Fellow. 14 years at Lockheed Martin. Started in cybersecurity and worked in it for 10 years.Lockheed is the largest defense contractor in the US.

04:42 (Headliner till 6:25): What role does AI play in cybersecurity? Madiha’s starting focus was cryptography. AI as a tool is good at making things faster. AI can speed up detection of threats.

06:30 (Headliner till 09:30)): Cybersecurity and AI work together. Some hurdles are data engineering.. Second hurdle is the availability of technologies. Finding out which is real and efficient is a challenge.

11:00: Data literacy and data culture. They are both extremely important in any organization but each organization is different.

14:00:  Madiha’s passion is engineering and design. Her role recently requires strategy work. Strategy of how to get the required data and get it into the hands of people who need it in a secure way.

18:00: Role of Ops in AI in cybersecurity. Data Ops, MLOps. Madiha’s take is that we tend to overcomplicate things. Nothing unique about MLOps and Ops in general. These Ops need to happen.

21:00: Madiha career combining cryptography and nanotechnology. Original role involved cryptography. Another project came up in the nanotechnology space. Able to work together in many areas.

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 : To improve data quality, start at the source – Jacklyn Osborne

Episode Summary:

Very often, organizations focus a lot on data cleansing after the data has been captured. But any incremental effort spent on focusing on data quality at the source will reap long term benefits. In this episode, Jacklyn Osborne, Data Quality Control executive at Bank of America, talks about the importance of data education to frontline employees so they can also be stakeholders of data quality. Jacklyn talked about the role of CDO, skills necessary for CDO, and how CDO can be empowered by where they sit in an organization. With deep expertise in the financial industry, Jacklyn talked about data monetization and treating data as an asset in financial industries.

03:36: Top 3 data challenges; (1) Expanding scope (2) Ill defined CDO role (3) Data democratization

04:49 : Necessary skills for a CDO. Needs to be inquisitive. Ask questions. Problem solving by nature.

06:30: Value of data in financial industry. Is it only because of compliance or is there a data monetization beyond compliance? Yes, there is a value.

09:05: Where should CDO sit in an organization? CDO should sit closest to the business.

11:30 (Headliner): First hand experience of a CDO role moving closer to CEO role. Accountability and ownership will increase and as a result, business believes data work is for them. And Data will be seen as an asset.

14:51 (Headliner) – Advice to financial institutions on data aspects. (1) Start small and scale (2) Align to a use case.

16:32 (Headliner): How do you know what is the right data quality? Starts with data production. Are we producing the right data to start with. And then you set controls in place as data moves along.

19:42 – If the data producers understand the value of the data, they are more inclined to improve the quality of the data at the source.

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 : Data Ops should be part of everyone on the Data team – Christopher Bergh

Episode Summary:

Data Ops is about working with everyone who deals with Data to deploy data related projects together. It is not just one person’s job. Christopher Bergh, CEO of Data Kitchen has embarked on Data Ops journey much earlier than the industry was asking for it. Nowadays, everybody including Gartner is talking about Ops, Data Ops, Dev Ops, ML Ops, X-ops etc. But Ops should not be a single person’s job. It should be 10% of every team member’s job to think about Operations. Just like Deming prescribed in a manufacturing process, it should be part of the system and framework.

01:35: What is Data Ops? It is about making data related folks (engineers, scientists etc.) work together to deploy the projects

03:00: Difference between Dev Ops and Data ops. They are similar in concept with Dev ops focusing on applications and Data Ops focusing on analytics. The profound difference is in the scale of teams involved.

08:37: X-ops. Gartner is focusing on X-ops with Model Ops, Data Ops, Dev Ops etc. All these concepts are about working with the system as opposed to working on individual parts. Deming philosophy.

13:00: Companies put processes like checklist meetings, stage-gates so they don’t have major issues. Once issues are found, people work all hours to fix them. Instead , we should have a system in place to fix production issues in production. Also, we should have a system to see issues before customer sees them.

18:49: Need for building a system and framework is very important. Instead of just asking a lowly paid release engineer, all the team members should be responsible for Ops. 

20:59 (Headliner): Formed a Quality circle and entered each error in a spreadsheet. Love your errors. After 6 months, it got better. 

26:00: Data scientists are dissatisfied because they are unable to make a business impact. Looking for perfection is not the best strategy.  

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Chris Bergh: https://www.linkedin.com/in/chrisbergh/

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 : Combining the passions of Data and Teaching with Laura Ellis

Episode Summary:

Laura Ellis, IBM Cloud systems Architect, always wanted to be a teacher. A recognition here and an award there in Computer Science got Laura interested in computer science and later a job with IBM. Laura combined her passion for teaching with DB2 and toured the world training others in DB2. As the business intelligence started picking up in 2013, Laura completed a part time MS in Predictive analytics and switched in data science. Laura realized that the organization needed people with other skills in data engineering, data wrangling etc and adapted. Laura started Little Miss Data as a personal project to combine data science with her personal passions such as Peloton R and teaching kids about data science. Laura believes that the future trend is about data security and ethics.

1:40: Passion for data. Data found Laura. Luck and encouragement. Computer science award in college for a project. Little Miss Data is about teaching people about Data. Ended up getting an interview for a comp science job. Found a job teaching about DB2 / Data warehousing worldwide.

05:00: MS in Predictive data analytics. Why? BI was exploding around 2014 so decided to get a part time MS in predictive analytics at Northwestern.

09:00: Reasons for data analytics explosion around 2014? Availability of data. Availability of compute. People started looking for insights rather than just charts & reporting.

11:00 (Headliner around 13:00): Why predictive analytics? Wanted to learn about statistical modelling. Also, people were asking questions about predictions and differences in data.

14:30 (HEADLINER) : Why are good data scientists leaving the field? Misalignment on expectations. Data scientists don’t expect to do data wrangling and data engineering. Started working on making sure that good data was in good hands. Expectations may be that company needs data engineers, data communicators etc.

18:42 (Headliner): What skills are needed for data scientists? A good level of subject matter of expertise. Gotchas are not in how the models are. Should spend a lot of time understanding the business.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Ramesh consulting website: https://DigitalTransformationPro,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 : Overview of MIT CIO Symposium with Allan Tate

Episode Summary: Allan reviewed MIT CIO Symposium and how the organizing committee pivoted in 2020 and 2021 to make it a virtual community. Allan also went over his own professional journey and how he came to be the Exec Chair of the symposium.

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 : The Data Diva talks about Data Privacy – Debbie Reynolds

Episode Summary:

Data privacy has come up on the trending topics recently because of Whatsapp policy changes by Facebook and news about Clubhouse app’s request for contacts list on the device. Debbie Reynolds explained the intricacies of data privacy, consent, and convenience. Debbie’s contention is that privacy can be used as a business advantage to acquire more customers. Debbie also talked about privacy risk index that she has come up with in association with Privacy & Cookies based in London. This episode ended with Debbie’s anecdotal story about her nick name Data Diva.

1:56: Privacy Risk index is a way to measure a website’s risk index based on how they manage the cookies. Debbie partnered with the privacy and cookies team of Lawrence and Robert to put together this index for Fortune 1000 companies.

05:01: Privacy is the right of an individual to be able to to control their information in some way. Debbie works with organizations to use privacy for business advantage.

05:41 (Headliner):  Facebook changed Whatsapp privacy terms and that caused lots of confusion. That also illuminated the fact that people are more aware of privacy. Customers will make choices going forward about privacy.

07:45 : People are not even aware that phone apps will be tracking even when they are not using the apps. Apple, starting with iOS14, is giving more options for users not to be tracked.

10:23 (Headliner): Privacy versus convenience. Companies make it convenient to make things less private with things like 80 page contract. Instead, it should be as convenient to keep things private as well.

12:55 (HEADLINER): Companies like Clubhouse give the app freely and in return ask for access to all the contacts for example. There are other apps where they download contacts without even asking.

16:47 (Headliner): Consent has some limits. The way clubhouse does is get contact list so they enable you to send invites.

19:32: The origins of Data Diva, the nick name. Debbie gave herself the nickname Data Diva at a networking event. Even though she was bashful at first, Debbie saw very positive reaction to it.

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.