Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

#244: Navigating Data Quality: Insights from the Chief Operator of Data Quality Camp

This week on the Data Futurology podcast, we host Chad Sanderson, the Chief Operator of Data Quality Camp. Over the ten years Sanderson has been involved in data, he has held key roles in companies including Convoy, a late-stage freight technology company, and Microsoft, where he worked on the AI platform team. Sanderson’s experience with these companies made him realise that there was a need for a platform where data specialists could come together and discuss strategies for maintaining high-quality data in their organisations. His group, Data Quality Camp, has since attracted nearly 8,000 members, and has become a real meeting place to discuss everything from the technical implementation of a data strategy, through to helping members find work in an increasingly dynamic and disrupted workplace environment. On the podcast, Sanderson highlights the strategies he has seen to deliver high-quality data environments, some of the traps and pitfalls to avoid, and how data specialists can better engage with and gain buy-in from the other lines of business within the organisation. For insights direct from someone at the heart of the data quality conversation, don’t miss this in-depth conversation with Chad Sanderson. Join the Data Quality Camp on Slack (https://dataquality.camp/slack) Connect with Chad: https://www.linkedin.com/in/chad-sanderson/ Thank you to our sponsor, Talent Insights Group! Join us for our next events: Advancing AI and Data Engineering Sydney (5-7 September) and OpsWorld: Deploying Data & ML Products (Melbourne, 24-25 October): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

#243 Mastering DataOps and MLOps: Building a Strong Foundation for Success and Future Growth

At Data Futurology’s OpsWorld conference in March, a panel of experts came together to discuss the importance of getting measurements, processes and methodologies right to drive DataOps and MLOps across the organisation. The panel consisted of Katherine Fowler, Head of Business Transformation at L’Occitane Australia, Amar Poddatooru, Head of Data and Technology at Australian Ethical, and Emyr James, Head of Data at Resolution Life and moderating the discussion was Andrew Aho, Regional Director, Data Platforms at InterSystems. It became a far-reaching discussion that started with methods to define and measure the ROI of data and analytics initiatives and how to get those projects off the ground. The discussion moved on to overhyped technologies in the data space, and then looked forward to what is on the horizon for the years ahead. As the panel discussed, there is a lot of interest among consumers in some innovative technologies, including ChatGPT. This is in turn driving a lot of interest at the executive level at rolling out solutions that use these tools. However, without the right foundations in place, and without proper concern for the privacy and regulatory risks associated with these tools, they will cause the data team more headaches than they’re worth. This panel discussion is essential for understanding how to structure a foundation for data success, be disciplined in deploying the available resources across the data team, gain executive buy-in, and then steadily build the practice up. Enjoy the show!  Thank you to our sponsor, Talent Insights Group! Join us for our next events: Advancing AI and Data Engineering Sydney (5-7 September) and OpsWorld: Deploying Data & ML Products (Melbourne, 24-25 October): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed 2:07: Felipe introduces the Measurements Thought Leaders panel and moderator, Andrew Aho. 3:48: How do you define and measure data and analytics ROI? 7:21: A discussion on metrics that help get data initiatives off the ground. 9:41: How a data leader needs to focus on the data platform, and articulate both the “big picture” view and the details. 12:35: As more organisations adopt ops, processes and methodologies, what challenges might people anticipate arising, and how can those be addressed? 17:24: What can data professionals do to help solve the change management challenge? 18:34: What are the challenges and impact of upcoming “silver bullet” technologies like ChatGPT? 20:16: What is currently overhyped in the data space (and why)? 24:03: What can we as data scientists do to ensure that we’re looking at the right risks and drawing accurate conclusions on what is right for the business? 26:13: If the goal is to focus on data science, how can we also keep experimentation and creativity going? 29:49: How do you estimate the value of change to get executive buy-in? 31:18: What upcoming developments and trends will emerge over the next five to ten years?   --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

#242: Tell me about the future of AI… Here Be Dragons?

This week on the Data Futurology podcast, we welcome Orla Glynn, Executive – AI, Reporting, Insights and Automation Configuration at Telstra. Glynn leads one of the biggest groups of data specialists to drive innovative AI and analytics across the company. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

#241 - Building AI systems with quality, holistic data

At the recent Advancing AI event in Melbourne, we were privileged to have a presentation by Vinay Joseph, the Pre-Sales Lead for IDOL at OpenText in APAC. Vinay gives an overview of the features of IDOL and how they can help data science teams bring automation and AI to the use of unstructured data. He presents a wide range of case studies and use cases. These include how law enforcement and the military, right through to news organisations and political campaigns might be able to use the data to draw real-time and in-depth insights that would otherwise be inaccessible. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

#240: Overcoming the challenges facing modern data engineering teams

This week on the Data Futurology podcast we host Paul Milinkovic, the APAC Regional Director for the leading data integration platform, StreamSets. Milinkovic joins us to share his insights into data engineers' challenges and the pipelines they manage and maintain. One statistic really highlights just how challenging work environments have become for data engineers: 76 per cent of organisations have a pipeline break at least monthly and for 36 per cent, it's weekly. Rather than contributing strategically to their organisations, engineers split their time between diagnosis and repair, and building new pipelines. This costs the organisation, as half the time the engineer isn’t being used strategically. It also leads to cultures of over-working, burnout, and high levels of churn within the data engineering team. Another challenge data teams struggle with is competing priorities. When multiple lines of business need pipelines developed, teams often need to triage to accommodate priority tasks, and this affects overall company outcomes. Being able to help organisations deliver a low or no-code environment that is highly visual and accessible to non-data specialists has been a critical benefit for organisations that have adopted StreamSets. Milinkovic then shares two case studies where StreamSets has helped with overcoming these challenges. In one, a bank achieved a seemingly impossible task – becoming compliant with looming Consumer Data Act requirements within four months. Then, a second bank was able to leverage StreamSets to its data to detect and thwart $9 million in fraudulent activity in a single month. For more deep insights into overcoming the challenges facing modern data engineering teams, tune into the podcast! Links Website: https://streamsets.com Follow on LinkedIn: https://www.linkedin.com/company/streamsets/ Whitepapers:  https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops  https://go.streamsets.com/230214-lifting-the-lid-on-data-integration-UGLP.html?utm_me[…]turology&utm_campaign=eg_lifting_the_lid_on_data_integration What we discussed: 00:00 Introduction  02:22: Felipe introduces Paul Milinkovic.  03:38: Milinkovic shares his background and his history with data at various levels and applications.  06:04: Milinkovic overviews StreamSets – when and why the company was founded, and what its core capabilities are.  09:04: What are the main issues that StreamSets helps data engineering teams solve?  12:57: How does StreamSets address traditional data pipeline design and build challenges?  12:33: What are the benefits of having a solution that is visual and accessible to non-technical users?  22:51: One of the common questions with the self-service approach to data is governance. How can that be handled while still allowing full flexibility?  26:46: Data engineers care a great deal about the quality and accuracy of data and the platforms that it sits on. Milinkovic explains why it is so important that they have the tools to be able to deliver that to the organisation.  31:24: What is the financial impact of data engineering teams spending as much time fixing pipelines as they are?  33:49: Milinkovic shares some case studies and use cases to highlight the value of StreamSets’ approach to data engineering. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message