The firms that are leading into the future are those who are leveraging data to find opportunities and implementing that information to differentiate themselves. Business leaders realise that data holds the key to competitive advantage, whether they are looking for patterns in financial transactions to detect fraud; using recommendation engines to drive conversion; mining social media posts for customer voice; or brands personalising their offers based on customer behaviour analysis.
To extract value from data, a diverse collection of skills and people in various positions are required. In this blog, we’ll look at how data engineers, data analysts, data scientists, business analysts, and business intelligence (BI) analysts help firms tap into massive volumes of data and transform it into meaningful insights.
A data engineer is the first step. Data engineers create, manage, and make data available for business operations and analysis. Data engineers extract, integrate, and organise data from a variety of sources inside the data ecosystem. Data repositories are used to clean, convert, and prepare data, as well as to store and manage it.
They made data available in formats and systems that could be used by multiple business applications as well as stakeholders like data analysts and data scientists. A data engineer must have a strong grasp of programming, systems and technology architectures, and both relational and non-relational databases and data storage.
Let’s take a look at what a data analyst does. A data analyst inspects and cleans data in order to derive insights, detect connections, locate patterns, and apply statistical approaches to it so that companies may make choices. Analyze and mine data, as well as display data, to understand and communicate data analysis conclusions.
Analysts are the folks who answer inquiries like, “Are users’ search experiences with our site’s search feature typically positive or bad?” or “What is the general public’s opinion of our rebranding initiatives?” Is there a link between sales and one product versus another?
Data analysts must be proficient in spreadsheets, writing queries, and creating charts and dashboards using statistical tools. Programming abilities are also required of modern data analysts. They must also have excellent analytical and storytelling abilities.
Let us now consider the function of data scientists in this ecosystem. Data scientists examine data for meaningful insights and generate prediction models using machine learning or deep learning models that are trained on historical data.
Data scientists answer queries such, “How many new social media followers am I expected to get next month?” or “How much of my client base am I going to lose to competitors in the next quarter?” or “Is this financial transaction uncommon for this customer?”
Data scientists must have a basic understanding of mathematics, statistics, and programming languages, databases, and data modelling. They must also possess domain expertise. There are also business analysts and business intelligence analysts.
Business analysts use the work of data analysts and data scientists to examine potential business consequences and the actions they should take or propose. Except that BI analysts do the same thing. Their attention is drawn to the market dynamics and external influences that affect their company.
They give business intelligence solutions by organising and monitoring data from various business operations as well as studying that data to generate insights and actionable intelligence that help enhance company performance.
To put it another way, data engineering is the process of converting raw data into useable data. This data is used in data analytics to provide insights. Business analysts and firm intelligence analysts use these insights and forecasts to drive choices that benefit and develop their business.
Data scientists use data analytics and data engineering to anticipate the future using data from the past. It’s fairly unusual for data professionals to begin their careers in one of the data positions and then complement their abilities by moving into another function within the data ecosystem.