Enabling Data Science to Speak the Language of the Business

Director Jay Ruffin talks with Senior Consultant Matt Williams and Principal Yonas Yohannes about how data science has changed in the past few years, and how data scientists can better understand and address the needs of the business.

Show Notes

Welcome to the Business Excelleration Podcast with Jay Ruffin, Digital Transformation Strategist at the Hackett Group. This podcast serves to provide listeners with various conversations from top experts on how to avoid obstacles, manage detours, and celebrate milestones on their road to success. Jay guides today’s conversation with guests Yonas Yohannes and Matt Williams as they chat about the gap between business and science. Learn more about data adoption and data science as they explore models, tools, and mechanisms to close this gap.

The world has been recently transforming at an exceptional rate. These experts discuss transformation from a data science standpoint and how this realm has changed. First, they address how advanced data science is becoming more essential. Additionally, we hear that this field has become more complex and difficult to implement. Yonas and Matt encourage listeners to ask specific questions. How can you enable the data team to speak in business terms? What makes data practitioners different from IT? Learn how to facilitate consumption and leverage data, while building the rules and skills necessary. Listen as they share about the importance of a separation of responsibility and product development disciplines. Data science from product development standpoint takes on a new perspective and meaning. Through storytelling and prioritization, Yonas and Matt touch on new tips for their listeners.

Touching on the flip side of data science, learn to look at the consumer. There is a communication gap between data and science. How can we consume data in an optimal way? With a need for a common currency to communicate, learn how to transform the data science practice itself and adopt proper taxonomy. Building structured ways to provide data to stakeholders is key. Differentiating roles in data science will help to address other technical aspects and generate necessary insights. Utilize a prebuilt tool with mechanisms and models so that data science is not using up crucial time.

As this episode draws to a close, we hear about product lifecycle training. Yonas and Matt wrap up the conversation with their last tips and thoughts for those looking to begin this journey in enabling data scientists. Data science is not trivial, discipline is required.

Timestamps:  

• 0:55 – Jay introduces today’s guests, Matt Williams and Yonas Yohannes.  
• 1:43 – What have been the impacts of Covid-19 from a data science position?  
• 5:21 – What sets the data practitioner apart from general IT?  
• 10:24 – What is the best way of looking to differentiate role and responsibility?  
• 12:08 – Looking at data science holistically.  
• 14:33 – What does it look like to equip both the data practitioner and the business?  
• 16:27 – How would a CIO jumpstart the process of enabling the data practitioners within their team?  
• 24:00 – Closing thoughts 

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