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Ironside’s Take30 with a Data Scientist series was typically targeted towards business leaders, with topics focused on strategy, including use-case development advice, de-risking AI with Data Science-as-a-Service, and ways to overcome common barriers to AI adoption. We also covered technical concepts like Model Evaluation and Feature Store Development. On top of that, we took several deep dives into technology partners including IBM Watson Auto AI, AWS Sagemaker Studio, Snowflake and DataRobot. Finally, we had a couple industry spotlights where we explored common use cases in Higher Education and Insurance.

Several attendees have shared that these sessions bridge the gap between the technical world of Machine Learning and that of their business, which in turn has helped them to know how to bridge that gap within their own organizations. For technicians, it has helped them to understand how to talk to the business and draw out use cases and help the business adopt solutions. For the business leaders, it’s helped them know what to ask of the data science team or what to look for in building a team. 

Overcoming the Most Common Barriers to AI Adoption (2/25/21)

Because so many organizations are in the early stages of AI Adoption, this is likely the most important topic to CIOs and business leaders in the Data Science series. This session discusses the challenges with people, infrastructure, and data that every organization faces and offers sound advice on how to overcome them.

Is Data Science-as-a-Service Right for your Organization? (5/19/20)

AscendAI, Ironside’s Data Science-as-a-Service, provides many benefits to organizations that are in the early or mid-stages of AI Adoption. Learn more about Ironside’s offering and how it could reduce your time to ROI to as little as 12 weeks.

How Snowflake Breaks the Chains Holding Your Data Science Team Back (9/10/20)

We hosted a number of Technology related secession with Partners such as Snowflake. This session dove a bit deeper than Data Science Best Practices: Feature Stores. Other Technology related sessions include Watson Studio, AWS Sagemaker, and a data enrichment session with Precisely, titled More Data, More Insight: The Value of Data Enrichment for Analytics.

Data Science work requires infrastructure that is scalable, cost-effective, and with easy access to multiple data sources. Snowflake provides this and much more to a data science tech stack. It also integrates easily with other machine learning platforms like DataRobot, AWS, and Azure. Snowflake is particularly valuable for data sharing with external data sources.

Leveraging Data for Predicting Outcomes in Higher Ed (6/30/20)


We hosted an industry-related session sharing how Higher Education is leveraging machine learning in very creative ways; this ended up being one of our top attended sessions for the Take30 series. In this webinar, we reviewed some of the ways that higher ed is using machine learning such as enrollment management, space planning and student retention. We also discussed some of the use cases that are helping universities cope with the challenges and nuance of COVID-19. We also hosted another industry specific session on Insurance.

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As we continue our Take30 with a Data Scientist series, we’ll continue to partner with experts in Machine Learning technology to offer demos and successful solutions as well as strategic sessions for business leaders. We also hope to spotlight some of our clients this year and the exciting AI driven applications we are developing for them in Retail, Insurance, Higher Ed, and Manufacturing. Coming up on May 20th, we will be hosting an industry focus for Banking. 

We’d love to have 1-on-1 conversations to discuss any challenges you may be facing with AI adoption. Please feel free to sign up for a spot with Pam Askar, our Director of Data Science.

The Ironside Take30 webinar series premiered on April 16th, 2020, with the goal to share expert dialog across a variety of data and analytics related topics to a wide range of audiences. The series has three primary dialog categories, each hosted by a BI Expert, Data Scientist or Data Advisor. In the past year, we’ve shared best practices with over 200 companies ranging from Fortune 50 to small businesses. Our success has been measured by participants returning and telling a colleague; on average, each unique company attended over six Take30 sessions. 

While some Data Advisor sessions are more technical, the focus is on describing concepts and tools at a less detailed level. We want to give people a sense of how rapidly the data and analytics environment is changing. To that end, the Data Advisor series worked with our partners, including IBM, AWS, Snowflake, Matillion, Precisely and Trifacta, to bring demonstrations of their tools and discuss the impact of their capabilities. We talked about the rapid expansion both of data and of the solution space to move, structure, and analyze that data. 

Most importantly, we had a special series on the Modern Analytics Framework, Ironside’s vision for and approach to a unified approach to insight generation that puts the right data in the hands of the right people. Regardless of your industry, your tools, or your use cases, you need a way to keep your data, users, and processes organized.

“What do you need in your data warehouse?” used to be the chief question asked when thinking about data for analytics. That time is past. Now, a data warehouse is just one possible source of analytics. Most organizations have so much data that building a warehouse to contain all of it would be impossible. At the same time, data lakes have emerged as a popular option. They can easily hold vast amounts of data regardless of structure or source. But that doesn’t mean the data is easy to analyze. 

And just as there’s no longer a single question to ask about structuring data, there’s no longer just one voice asking the question. Data scientists and data engineers are among the many personas that have emerged as consumers of data. Each has their own toolset(s) and preferences for how data should look for their purposes. 

All of this diversity demands a more distributed approach to ingesting, transforming and storing data – as well as robust and flexible governance to manage it. All of the topics covered in the past year of Take30 with a Data Advisor touch on these points, and on Ironside’s goal to help you make better decisions using your data.  Here are five of the 27 Data Advisor sessions we hosted this year: 

  • Modern Analytics Framework: Series Summary  (7/30/20-9/1/21) – This 6-part series covers all aspects of Ironside’s Modern Analytics Framework: overall concepts, assessment and design, governance, identification of user personas, implementation, and usage. If you are looking to upgrade your existing analytics environment, or creating one for the first time, this is an essential series, and one that Ironside will be expanding on in 2021.

  • Snowflake as a Data Wrangling Sandbox (6/3/20) – Snowflake is a tremendous cloud database platform for data storage and transformation. Its complete separation of compute and storage allows for many usage scenarios, and most importantly for easily scalability based on volume of data and consumption. Nirav Valia (from Ironside’s Information Management practice) presents on one common Snowflake use case: using Snowflake as a data wrangling sandbox. Data wrangling typically involves unpredictable consumption patterns and creation of new data sets, as an analyst seeks to discover new insights or answer new questions by manipulating data sets. Snowflake’s power and flexibility easily handles these types of activities without requiring up-front investment or significant recurring costs. It’s easy to create transformations, let them run, then let the data sit until it is needed again. (If you are interested in Snowflake, also consider our later Take30 Snowflake: Best Practices (9/24/20), including commentary from a Snowflake engineer.) 

  • What is Analytics Modernization? How can Data Prep Accelerate It? (with Trifacta) (2/4/21) – Toward the end of our first year of Take 30s, we held a panel, hosted by Monte Montemayor of Trifacta, around data prep and accelerating analytics modernization. As I mentioned earlier, there is a tremendous amount of data available today – but getting it analytics-ready is a huge challenge. Tools like Trifacta (known as Advanced Data Prep, or ADP, in the IBM world) are extremely useful for giving analysts and business users the ability to visualize and address data quality issues in an automated fashion. This is useful for data science, dashboarding, data warehouses – any place where data is consumed. (If you are interested in Data Prep, check out IBM Advanced Data Prep in Action (7/8/20) and Data Wrangling made Simple and Flexible with Trifacta (5/6/20))

  • A Data Warehousing Perspective: What is IBM Cloud Pak™ for Data? (5/27/20) – IBM has created a single platform for data and analytics that works across cloud vendors and on-premise. If you want to be able to shift workload between local nodes and the cloud easily, this is the solution for you. In this Take30, we provide an overview of the technologies that make Cloud Pak for Data possible, and how you can take advantage of them. (We also have a session Netezza is back, and in the cloud (7/23/20) discussing Netezza, one of the many technologies available on the Cloud Pak platform)

  • A Data Strategy to Empower Analytics in Higher Ed (7/1/20) – Occasionally, we have the opportunity to host an industry-specific Take30, and where possible, we have clients join us. Northeastern University joins this Higher Ed focused Take30 to discuss their approach taken with Ironside in developing a multi-year roadmap. This was geared towards increasing the “democratization of data and analytics” by establishing the organizational foundation, technology stack and governance plan necessary to grow self service throughout the institution. Our discussion highlights the particular challenges of a decentralized, highly autonomous structure, and shared the value of a data science pilot in the admissions area executed during the strategy engagement to generate tangible results.

2020 was a unique year. At Ironside, it gave us the opportunity to reach out to customers in a new way – one that we are continuing into 2021. We look forward to more detailed sessions on the Modern Analytics Framework, and on trends and tools that we see gaining prominence. 

After a year of delivering these sessions, we’ve realized that customers are not only looking for specific solutions, but for a sense of where the analytics world is going. Which cloud platforms make the most sense? What transformation and data wrangling tools are the most useful? Should I redesign my warehouse or just add a data lake? We look forward to exploring those and other questions with you.