Many large organisations have the data to pull off sophisticated marketing strategies, but only if they avoid the common pitfalls that limit the potential. In this episode I interview Tejas Manohar on the huge – and typically unexploited – potential for data-driven marketing and personalisation. Tejas is co-founder and co-CEO of Hightouch. Hightouch is a reverse ETL platform that helps organisations synch their data warehouses with business facing tools and technology. Their products are used by big name corporations like Warner Music, Chime, Spotify, NBA, and PetSmart. In this wide-ranging conversation Tejas and I discuss: What a reverse ETL platform is and why we need itWhy Tejas is bullish on turning data warehouses into marketing enginesThe key steps marketers should take to implement personalization effectively using existing company data and platformsThe pitfalls and common mistakes businesses make in data-driven personalisation and how to avoid these, and much more.Tejas on LinkedIn: https://www.linkedin.com/in/tejasmanohar/ Tejas on Twitter (or is it X?): https://twitter.com/tejasmanohar

In a recent conversation with data warehousing legend Bill Inmon, I learned about a new way to structure your data warehouse and self-service BI environment called the Unified Star Schema. The Unified Star Schema is potentially a small revolution for data analysts and business users as it allows them to easily join tables in a data warehouse or BI platform through a bridge. This gives users the ability to spend time and effort on discovering insights rather than dealing with data connectivity challenges and joining pitfalls. Behind this deceptively simple and ingenious invention is author and data modelling innovator Francesco Puppini. Francesco and Bill have co-written the book ‘The Unified Star Schema: An Agile and Resilient Approach to Data Warehouse and Analytics Design’ to allow data modellers around the world to take advantage of the Unified Star Schema and its possibilities. Listen to this episode of Leaders of Analytics, where we explore: What the Unified Star Schema is and why we need itHow Francesco came up with the concept of the USSReal-life examples of how to use the USSThe benefits of a USS over a traditional star schema galaxyHow Francesco sees the USS and data warehousing evolving in the next 5-10 years to keep up with new demands in data science and AI, and much more.Connect with Francesco Francesco on Linkedin: https://www.linkedin.com/in/francescopuppini/ Francesco's book on the USS: https://www.goodreads.com/author/show/20792240.Francesco_Puppini

An estimated 80 to 90 percent of the data in an enterprise is text. Sadly, this rich information is mostly neglected for analytical purposes. Textual data is typically full of information, but also very complex to interpret computationally and statistically. Why? Because textual data is both content and context. The same words and sentences can have very different meanings depending on the context. Textual data is truly a goldmine, but how can we mine it without being digital superpowers like Google, Microsoft or Facebook? To answer this question and many more relating to interpretation of textual data, I recently spoke to Bill Inmon. Bill is the Founder, Chairman and CEO of Forest Rim Technology and author of more than 60 books on data warehousing. He is often described as the Father of Data Warehousing due to his pioneering efforts in making data and data technologies available to organisations across all industries and sizes. In this episode of Leaders of Analytics, we discuss: How Bill became the Father of Data WarehousingThe history of data warehousing and the most exciting developments in this space todayThe typical challenges holding us back from extracting value from textual dataThe concept of the “Textual ETL” and it’s benefits over other text data storage and analytics approachesWhy NLP is not the best approach for textual data analyticsThe biggest opportunities for textual analytics today and in the future, and much more.Connect with Bill: Forest Rim Technnology: https://www.forestrimtech.com/ Bill on LinkedIn: https://www.linkedin.com/in/billinmon/

In my opinion, any organisation with respect for its data should have a Chief Data & Analytics Officer (CDAO) as part of their C-suite. Although the CDAO role is still nascent, business leaders across many industries are starting to appreciate the need for a data and analytics voice at board and executive level. So, what does a CDAO do? How should they spend their time to balance strategic influence with operational delivery of data products? To answer these questions and many more related to the principal analytics role, I recently spoke to Kshira Saagar, who is the Chief Data Officer at Latitude Financial. As the CDO at one of Australia’s largest consumer financial services firms, Kshira is responsible for the end-to-end journey of data through the organisation, from extraction to value creation through data products. He leads a large team of Data Scientists, Data analysts, Data Architects, Data Engineers, Machine Learning Engineers, Data Warehouse Developers, BI Developers and Data Governance experts, who are responsible for bringing the company’s data and analytics strategy to life. In this episode of Leaders of Analytics, we discuss: What a week in the role of a CDAO looks likeHow to secure strategic support and executive sponsorship for analytics projectsWhat’s required of CDAOs and their teams to foster a data literate organisationHow to structure data and analytics functions for successThe future of the CDAO role, and much more.Learn more about Kshira at https://www.kshirasaagar.com/