In this episode, we speak with Matt Battifarano. Matt is a data scientist focusing on transportation modeling. He first started his career as a data scientist at a startup called Bridj where they created a smart micro-bus platform for urban transit similar to Uber Pool. Currently he’s working towards his PHD at Carnegie Mellon at their Mobility Data Analytics Center. 

Top 3 Value Bombs: 

1. K.I.S.S - Keep it super simple. Do not over complicate the process, especially when just starting out prototyping.. 
2. The importance of understanding the purpose/history of methods in AI/ML
3. There are many challenges to developing autonomous vehicles - one in particular is creating an environment where you can test it safely. Unlike many AI/ML tests you cannot perform A/B tests with end users.

In this episode, we speak with Tarush Aggarwal. Tarush is the founder of 5xdata, where he helps companies build a strong data foundation with self-service BI to enable the business. Prior to starting 5xData he was one of the first data engineers on the analytics team Salesforce and helped scale the data team WeWork from 5 to 100+.

Top 3 Value Bombs: 

1. Ingest all your raw data into a central location and build your data models on top of that. 
2. When organizations are first building out a data platform, the first item they should focus on is building out a self-service BI tool.
3. The use case for data lakes may be on the decline with the ability to separate storage and compute within data warehouses

In today’s episode, we will speak with Peter Voss and discuss the current landscape of AI, the next wave of AI called Artificial General Intelligence, and how organizations today can level up their chatbots to create satisfied customers. Peter Voss is a Serial Entrepreneur, and Pioneer in Artificial Intelligence. Who coined the term ‘AGI’ (Artificial General Intelligence) with fellow luminaries in the space.

At the age of 25 Peter, IPO’ed a company he started that grew to over 400 people. Since then he’s been focusing on AI and recently launched aigo.ai an intelligent cognitive assistant that delivers highly intelligent and hyper-personalized conversational assistants at scale for the enterprise – 

Top 3 Value Bombs:

1. AI solutions today are considered “narrow”. Artificial General Intelligence is the next wave where AI solutions will be more autonomous.
2. Chatbots can either frustrate customers or satisfy them. Create chatbots with a “brain” that can automatically kick off processes to meet their needs. 
3. Graph DB’s are critical to enabling artificial general intelligence

In today’s episode, we will speak with Kapil Surlaker,  the vice president of engineering at LinkedIn. Kapil has been with LinkedIn for over 10 years and has played an instrumental role in shaping the data architecture that LinkedIn is built on top of. In this episode, we cover a wide range of topics surrounding data architecture from:

•  How metadata is captured and served up
•  Future-proofing the data architecture
•  The shift from on-prem to  Azure
•  How LinkedIn monitors the quality of there data in real-time

In today’s episode, we will speak with Chris Bergh, a pioneer in the DataOps landscape and the CEO at DataKitchen, a DataOps Platform that Simplifies Complex Data Toolchains and Environments

Top 3 Value Bombs:

1. DataOps is not just DevOps for data
2. Any organization can get started today and start implementing DataOps practices. Start small and prioritize quick wins. 
3. The people/process is just as important as the tools used if not more so when implementing DataOps. 
   
   

If you enjoy this episode and want to learn more, please head on over to DataKitchen.io to download your free copy of the DataOps Cookbook. 

In today’s episode, we will speak with Manny Bernabe and discuss the current landscape of AI, how to get started implementing AI solutions and what organizations should be doing today to set them up AI success in the future. Manny is the founder of BigPlasma.ai and has 10+ years of experience creating and deploying AI & Machine Learning solutions and products to industries from financial services to semiconductor manufacturing. He helps innovation leaders build AI & analytics products/services while avoiding R&D failures.

Top 3 Value Bombs:

1. If your organization want’s to be in business in 10 years, then you need to start prototyping/creating AI solutions 
2. AI should be top of mind when designing modern data architectures. 
3. Start collecting data today for the questions you want to be able to answer three years from now. 

In today’s episode, we will speak with Susan Walsh and learn why organizations struggle with creating and maintaining high-quality data and the steps she takes to resolve data issues. Susan Walsh has nearly a decade of experience fixing your data and founded the classification guru. Susan is a specialist in data classification and data cleansing. She is passionate about helping you find the value in cleaning your dirty data and raises awareness of the consequences of ignoring issues through our blog, webinars, and speaking engagements.

Top 3 Value Bombs:

1. One reason organizations struggle with having high-quality data they don’t value data as an investment but rather a cost.
2. You must be able to track the lineage of your data to the source-of-truth, it’s critical to resolving data quality issues.
3. Don’t just fix bad data, identify the root cause and resolve the issue there. 

In today’s episode, we will speak with Jesse Anderson and learn how to run successful big data projects and how to resource your teams. Jesse is a big data expert at Big Data Institute, who’s worked with startups to Fortune 100 companies. He has taught over 30,000 people the skills to become data engineers and is published in prestigious publications such as The Wall Street Journal, CNN, BBC, NPR, Engadget, and Wired. 

Top 3 Value Bombs:

1. It’s not easy to retool your traditional DW team to support big data technologies
2. A general ratio you should have is, 2-5 data engineers for every 1 data scientist
3. The importance of having three data teams and having them staffed properly which include operations, data engineers, and data scientists

In today’s episode, you will hear from the co-founders of eXate Peter Lancos and Sonal Rattan. eXate streamlines, automates and simplifies the processes of storing, interpreting, and extracting value from data assets. It democratizes data privacy for organizations by providing a simple, embedded platform that automates the technical enforcement of data policies.

Top 3 Value Bombs:

• DataSecOps is the collaboration and automation of policies enforcement from the various teams across the organization; Dev, Legal, Security, Governance, Risk, Compliance, Data Stewards, and Data Owners.
• Data policies should be centrally managed and automatically applied, you should not be reliant upon having a federated distributed way of applying data policies.
• Privacy-enhancing technologies will allow you to minimize the use of personal data without losing the functionality of the data