Exploring Transferrable Skills Within the Field of Data Management

Exploring Transferrable Skills Within the Field of Data Management

In a competitive labour market, the value of transferrable skills is often overlooked. We spoke with Marcelo Leite to learn more about the current data management landscape, including which transferrable skills are beneficial for those either looking to newly transition into the field, or for established data workers looking to branch out.

As a Data & Artificial Intelligence Specialist, Marcelo specializes in support organizations on strategic planning for data related projects, including data governance, data management, and data analytics. He is also a professor in Cloud technologies and Data Analytics, author, and produces content for his YouTube channel, @Marcelointech.

What kinds of trends are you seeing in data management today?

Today the key success factor for a data management project isn’t just technology. When I originally started teaching in 2011, I was instructing highly technical courses for specific tech certifications. But a few years ago, I repositioned from technical programs to MBA courses. All of the data classes I teach now are based on strategy and foundational concepts needed to successfully execute projects.

Of course, specializations in specific technology are still needed, but I have found that a focus on connecting business needs and goals to data strategy is a more effective approach. With artificial intelligence, the data technologies are becoming even more intelligent and automated, so educating decision makers can have a greater impact than continually investing in technology skills.

The market for data has grown significantly in the last few years, and we have a huge gap for professionals in all areas of data. It’s a very interesting area of tech to work in and I encourage more people to explore the various opportunities. A lot of people don’t realize that career opportunities in data go beyond specialized technology skills.

What skills do you think align with a career in data? How would someone know whether they might be suited to a data related role?

In the current market, there are four primary roles in data organization: Database Administrator (DBA), Data Engineer, Data Scientist, and Data Analyst.

A DBA, database administrator, manages data infrastructure and who has access to data assets. This role is more connected to the IT area and may be part of the IT team. The DBA is a technical person that learns about technology used in implementation, but they don’t really do coding.

A Data Engineer is also a more technical role, using data to create data pipelines. This role involves connecting data, transforming systems to organize data in the proper way, and creating models for data consumption. A Data Engineer performs technical processes and coding, so it’s recommended that someone have a technical background for this role.

A Data Scientist uses math and statistics to apply data. Where the engineer has organized and stored data, the scientist will start to store it in clusters, and apply statistics to classify or forecast. This is also a technical role, but it’s more connected to the business. Sometimes a data scientist isn’t in IT, they’re on the business management side with good communications skills – discussing requirements with business stakeholders, and then experimenting and applying algorithms to gain insights.

A Data Analyst is the least technical data role, reviewing insights, identifying potential benefits/solutions, and communicating them to stakeholders. But ultimately everyone in an organization should be a Data Analyst. All business leaders in IT, HR, Marketing, Sales, anyone who is a decision maker – they all need to have data analysis skills.

We’re experiencing a skills shortage in the industry, what do you think organizations can do to minimize the impact of this gap and ensure their projects still succeed?

Studies have been done on how many data analytics projects fail, or don’t provide any returns for a business, and it’s a frequent outcome. The industry skills gap is a major reason for this, along with the massive, ever-growing amount of data an organization must keep up with. This is why data analysis should be a skill that is emphasized across the organization. Businesses need to teach employees to make decisions according to available data.

We see it all the time – organizations will have a huge data team, but inside the other departments people are using spreadsheets and calculating things manually. They don’t have data confidence, control, or governance, and sometimes decisions are made based on perceptions of incorrect, incomplete, or out of date data.

When a data strategy is aligned to business needs – which means that more members of the technical data team understand business needs, and vice versa, more stakeholders understand data analysis – then an organization can create a data driven culture. In this culture an organization uses technology that serves the business and implements decision making based on data across the company.

What do you think data workers can do to make themselves as valuable as possible?

I think a broad understanding of business operations and needs combined with the ability to learn and understand technology applications is what makes people stand out in this market.

I’ve just written a book called Microsoft Certified Azure Data Fundamentals. It gives readers all required knowledge for a fundamental's certification from Azure. It starts with the foundational concepts, and covers the history of data, how we evolved from databases to modern implementation, how a data strategy needs to be aligned with business needs, and technology. The content is organized for someone who doesn’t have a technical background, then gets more specific with technology.

Are you interested in exploring new possibilities, or transitioning to a career in an area of data management? Get in touch and let’s chat.