By: Ilana Jucha

This article is by Moshe Uziel, Chief Data Scientist in Healthcare at umAI, responsible for helping clients build data intelligence into their operational processes. 

In this series of blog posts, I want to provide organizations with the insights needed to integrate data science innovation into their organizations. To kick off this series, we begin by looking at the key players involved in driving the process of data innovation through AI Solutions. 

The new era of Big Data has ushered in tremendous excitement and the promise of unprecedented growth. Companies are becoming incredibly energized to leverage their data to gain actionable insights and grow their operational engines. 

With the surge to adopt data-driven practices, organizations are scrambling to hire a Data Scientist to help them deliver growth. Their eagerness is warranted; today, AI is completely revolutionizing business. Harnessing expert knowledge to ride this tide of innovation is critical. 

However, it is at this point in the Data Science process where many organizations go wrong.  

The most common mistake businesses make is to think they can just hire a Data Scientist and ask them to deliver an AI engine without adequately adopting an organizational mindset that understands how to integrate this value. 

I have seen companies build out fully-fledged Data Science departments packed with the most experienced Data Scientists in the field, only to utilize about 5% of the solutions coming from these departments because they were not equipped to integrate AI innovation into their organization. 

Sadly, this is one way to throw away a lot of money and create a lot of frustration in the process of trying to obtain real value from AI. 

In my experience in delivering value to businesses through Data Science, the best way to ensure that AI Solutions become an integral part of operations is to foster an environment of collaboration between several critical organizational figures.

The Data Science Ecosystem 

The Data Scientist is really only one part of the AI-driven solution. Data Science requires the support of an ecosystem to thrive. 

So, what are the key roles in an organization needed to facilitate the data science process? 

The Stakeholder:

Job role: Typically the CDO

The stakeholder is responsible for bringing the idea of a Data Science solution into the organization. They are looking to develop an AI growth engine.  Within their role in the ecosystem, they identify critical business problems and seek business buy-in for AI solutions that will help address these challenges. The stakeholder sets out clear expectations on why an organization needs to adopt an AI solution to help them drive growth, setting the budget, KPIs, and overarching business strategy.

The Subject Matter Expert (SME): 

Job role: A Business Executive or expert for a specific vertical

The SME has a skilled understanding of the business and the chosen value to be brought into the organization. The SME will vary by industry; in insurance, the SME may be the claims manager, while in healthcare, they may be the doctor. With their depth of knowledge in the subject matter, they are seen as the critical touchpoint for understanding the needs that must be addressed through the AI Solution. 

The AI Project Manager:

The Project Manager is responsible for bringing the AI solution to life – they set out milestones for the development of a solution to help bring it to fruition.  In my opinion, if an organization is looking to build out an AI solution, they need a Project Manager to help effectively guide the developmental process. 

The Data Figure:

Job role: A Data Developer, such as a Data Engineer or a BI developer 

The data figure has a profound understanding of the company’s data and data history. They help connect SME knowledge to workable data and provide Data Scientists with AI-driven data sets or direct them to the right data enabling them to transform figures into solutions. 

The Data Science Manager: 

Job role: Typically the Chief Data Scientist

The Data Science Manager’s role is similar to that of an R&D Manager but within the Data Science field. They collectively work with the SME and the stakeholder to understand the business solutions needed. The Data Science Manager works on the development of budgets, operating plans and developmental tech environments. The Manager’s ultimate objective is to translate business challenges into technical targets and to help steer and lead AI initiatives in the right direction within set timelines and according to a specific methodologies.

The Data Scientist: 

The Data Scientist develops the desired solution. They must utilize their knowledge and expertise and the business insights from SMEs to develop AI solutions which address organizational objectives. While technical experience and expertise are crucial for their role, their ability to adapt to new environments is critical for ensuring success.

Collaboration is King 

Acceleration of data science requires companies to be agile. For organizations, it is essential to look within their company culture and understand how AI solutions fit within their organizations and how central players can help support the process.  

The key to this is to ensure effective collaboration and communication for all the actors in the Data Science ecosystem—the more robust the ecosystem, the better the AI-driven solutions and the better the business value. There are many ways to ensure effective collaboration between all parties involved, but we will save those insights for another post. For now, what is essential to understand is that true value can only be derived from solutions when organizations foster an ecosystem of AI innovation.

Looking to hire a data science company to help your organization drive innovation through data? Get in contact with us today!