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Choosing the Right Experts for Your Data Project

When planning a data project, choosing the right experts is critical to the success of the project. In our previous blog post, we spoke about how data can drive value for your business. In this post, we are going to discuss the three main data expert roles and how to pick the best for your business’s project.

Dimitris Bertsimas, the associate dean of business analytics at MIT Sloan, calls data “the most important aspect” of analytics. “Finding the right data, cleaning it, and shaping it so that it works toward your need is a skill that takes experience and understanding,” he says. In our previous blog post  we spoke about how valuable data is. In this blog, we are discussing how data engineers, data/business analysts, and data scientists are needed to fully leverage data.

Working with data can mean many different things; some of it is maintaining the structures and mechanism for capturing data accurately and in a timely manner; and others require specialized skills to make insights come to surface. When you understand what your business needs from your data, you can pick the most appropriate and cost-effective data skill for your projects (if you can't have all roles on your team). Here's a quick glance at some common/typical data tasks.

Leveraging data as an asset requires the following key skills and processes:

  1. Data analysis: Interpreting trends and patterns to extract meaningful insights from raw data - such as trends in sales in a store or brand that can be broken down by time, item, etc. to gain insights.
  2. Data visualization: Presenting data effectively through charts, graphs, maps, and infographics to improve understanding - such as a line chart presenting sales data over time shown on a dashboard.
  3. Machine learning: Capability built into software and applications, often with AI, to learn from data and make predictions or decisions without being explicitly programmed - such as automatic triaging of alerts from sensors that may indicate different levels of issue severity. 
  4. Statistical analysis: Analysis of numerical data using statistical methods such as regression analysis and descriptive statistics to derive actionable insights - for example, statistically determining the likelihood that a customer will purchase something based on previous purchases (or buying behaviors of similar types of customers).
  5. Data mining: Combining different types of information and data to uncover hidden patterns in large datasets to derive actionable insights - such as mining user data to determine who best to advertise your company’s newest product to.

To better understand what each data professional can offer to a business, here is a quick introduction to each role.

Data Engineer

Data engineers prepare and structure data for analysis. This process involves collecting, cleaning, transforming, and organizing data to gain insights or answer business questions. Data engineering is critical to any organization's data strategy, enabling organizations to store and use their data for various analytical tasks effectively. It is also the process of creating a data architecture that meets an organization's specific requirements.

Let’s consider a user profile on a website. A data engineer would find effective means to store the data from the “capture point” (a form on your website a user completes). On a busy website there will be a lot of user data entered into the form. The data engineer would build a transformation system to gather the information each user enters into the form and organize it into a set of data a data scientist could work with, i.e., the demographics of users, locations, etc.

It is common to immediately think of data scientists when considering anything related to data. Yet, data scientists don't perform all these tasks on their own. Other data professionals, like data engineers, data analysts, and others are also involved in this process. Together, these data professionals serve as a bridge between raw data, business intelligence (BI), reporting, and predicting based on data. 

Data Analyst

Data analysts leverage data to identify and assess business opportunities, evaluate and solve business problems, and provide guidance for making better business decisions. Through tools such as data visualization, data analysts can make data-guided suggestions to improve efficiency, effectiveness, and profitability. 

Also often called business analysis, data analysis is used to study customer behavior, examine market trends, optimize processes, and develop new products or services. Data analysts often work closely with business leaders to make the right decisions based on collected data.

Data Scientist

There are many ways companies and organizations can use data. Data scientists execute a variety of methods to help businesses make data-driven decisions. They take the data the data engineers have collected and organized and find ways to use it in practical applications to achieve business goals. 

Data scientists may make predictions for future needs or behaviors (e.g., forecasting how much inventory to hold based on previous shopping seasons) or explore data for insights not yet known to the business (e.g., determining what factors do and do not contribute to above tank storage corrosion rates). By creating statistical models, data scientists can determine how many online reviews for a product are fake or the likelihood loan applicants will default on a loan.

Assembling a data team

What's most important to point out is that data teams should be created and organized based on business objectives and needs. For example, suppose the objective is to increase customer loyalty. In that case, every member of the team would have a unique role to focus on understanding customer behavior, developing and deploying predictive models, and providing insights for marketing and sales teams. To do so, the data engineer would be responsible for structuring data, the data analyst would look at overall trends, and the data scientist would predict behaviors and explore data using methods beyond those utilized by the data analyst.

Other objectives may include improving operational efficiency or reducing costs, which require different data team structures and skill sets. However, if the goal is to generate reports from data sources quickly, then an organization may prioritize roles such as business analysts and data visualization specialists over data scientists or data engineers. Ultimately, an organization should evaluate its own goals and budget before deciding which roles are necessary to meet its objectives and select the team skill set accordingly. 

In closing

Many teams are just starting on their data journeys and external advisors can be a good way to get some experience and a solid headstart with data. Selecting the experts to assist with your data project can help you take advantage of the benefits data projects can create for your business. Without clear information derived from clean data, your business cannot make truly informed decisions. 

ChaiOne is here to provide knowledgeable experts with the right skills to achieve your data goals. We can help drive value for your business by helping you master your data.