For many, a visual aid assists in creating a mindset of where you are going. We believe strongly in visual aids being essential to a transformation journey regardless of the industry. This is why we have created a data-driven pyramid.
We describe literacy as what is required for people to be able to participate in a conversation about data. In order for that to happen, there needs to be education across the organization to help individuals have a common language. Think of it as similar to individuals needing to be educated about financial information and how to read financial documents from the organization, in order to have an intelligent and progressive conversation. It is the same with data. Teams need to have a base-level understanding in order to participate in those conversations. This is something that is a requirement inside any organization that wants to make more data-led decisions.
We describe fluency as the ability to understand and process data insights. Not everyone in your organization needs to have this capability, but it is helpful if this skill is inside the organization. This specialty goes beyond literacy and requires the team members to assemble, source, and process different information sources together that will lead to business insights.
When you get to the data translator stage, these individuals can facilitate communication between data science teams with technical proficiency and business consumers of various data science project results. This is needed because data scientists and sometimes analysts don’t have the business understanding and need people that can translate between the business needs and the data requirements. If this role doesn’t exist in the organization (can be external or internal), it can often cause misalignment and can lead to data that doesn’t solve real business problems or give any insights.
(Citizen) Data Analyst
Data Analysts are individuals who analyze data, but it is likely not their primary role. They fit into the category of “do-it-yourself data analytics”. They are learning about how to improve data across their organization but aren’t technically data scientists, with formal training. They are looking for insights to help them better lead the business. They may spend their time building dashboards and creating visualization tools – this could be in their department or more company-wide. Additionally, this role can be hired outside the organization.
(Citizen) Data Scientists
Data Scientists are well trained. They use predictive modeling and other techniques to create intelligent models about the business. A lot of data scientists are programmatically strong and use these skills to take data and turn it into forward-thinking models. They use machine learning or AI to process an organization's data. This role can be either inside or outside the organization. As you may gather, this role needs a translator into the business insights and needs so that it produces models that are helpful to move the business forward.
We hope this article is helpful in describing the different types of roles and needs that a business has as it enters its journey into a data-driven culture. For more information on educating your teams in data literacy OR assistance in augmenting your team schedule a no-obligation call with our team HERE.
Data and analytics leaders are critical to the performance of any company. These roles are not just crucial at specific points in time; they are essential to the long-term success of any business. These roles are expected to play a growing role in the future of almost all companies. Promoting data fluency and engaging more individuals in the data discourse is crucial to the job. The importance of data and analytics leaders is increasing because of new challenges. They help drive performance by ensuring data is relevant and accessible and analyzing it to uncover insights that can be used to improve business processes across departments and geographies. This article will discuss the importance of data and analytics leaders in today’s digital world.
Chief Data Officers (CDOs) are the new hot job title in data science. As companies scramble to hire CDOs, they see this role as essential to their future. After all, a chief data officer is the highest-ranking data scientist within an organization. They are typically responsible for developing and implementing strategies for managing, analyzing, and monetizing data from multiple sources. To help you understand the challenges CDOs face as this newer role is added to many companies, we’ve compiled this list of common mistakes that CDOs are making right now, along with advice on how to avoid them so that you can hit the ground running as soon as possible.