Business Intelligence: Understanding the Past
Business Intelligence is the most classic data-based discipline. It is most similar to working with Excel – in fact, it is totally normal to find BI positions in Excel. Business Intelligence is based on the creation of command boxes – also known as dashboards – which represent the current or past state of the business; examples include sales percentages, seasonal evolutions, and tables of best-selling products. Business analysts are great at creating these types of summaries and taking advantage of them to guide future decision-making.
Some of the most popular programs in this field are PowerBI, Excel, and Tableau.
Data Analytics: Making Sense of Data
Data analytics is perhaps the most difficult aspect to define. It is of intermediate accessibility, serving a wide variety of professionals, from the most code-oriented to those in specialized programs. Data analysts’ roles range from data collection to modeling, through transformation and summarization. It is common to find data analysis linked to the concept of insights, which can be translated as keys. Data analytics seek clues or better understanding of data to draw conclusions that were not obvious before.
Apart from the programs used for Business Intelligence, it is common to find analyst positions that employ Python languages, R, and SQL.
Data Science: Anticipating the Future
Data science goes a step further than classic data analytics, and begins to use machine learning models to make predictions. The biggest difference between data analytics and data science that is that the latter deals with events that have not yet occurred and the ways that data can be used to anticipate them. For example, data can help predict the type of product that a customer will buy when accessing your website; it can predict the probability of cancer, given a patient’s genetics. It is usually described as the intersection of Computing, Statistics, and Business.
Data science is mostly linked to programming languages, especially Python, and to a lesser extent R and SQL.
Data Engineering: Organizing the Data
Data engineering is the most technical of all. Associated more with “the how” rather than “the what,” data engineering is based on the principles of extract, transform and load (ETL), which is a summary of the process of moving data for later exploitation. Data engineering professionals oversee the structuring of databases or warehouses, ensuring that the data is stored and used efficiently and safely. It is perhaps the “least friendly” dimension of data for end users, as it is closer to more classical computing than analytics.
To the aforementioned Python and SQL, it’s necessary to add some classic languages such as Java or the lesser-known Scala.
Now, the delicate concepts – AI and big data. These aren’t delicate because of their low validity – they are actually highly valid – but because of their indiscriminate use in public conversation.
Artificial Intelligence
The concept of Artificial Intelligence is also more nuanced, and in many cases, misused. In most media, it is used to describe processes or algorithms that are actually Machine Learning. The border is a bit blurry: in fact, in many cases, ML is considered a sub-field of AI. In short, the primary difference between the two is that Intelligence comprises autonomous creativity or decision making, while the vast majority of Machine Learning algorithms respond to very specific tasks.
For the general public, it is reasonable to confuse Machine Learning with Artificial Intelligence, since in both cases machines produce results autonomously. However, at a technical level, Machine Learning is a field with a great future – and presence – in the vast majority of businesses, while Artificial Intelligence is still far from being widely applied. The best-known examples of ML currently are autonomous driving or voice assistants.
Big Data
Big Data could use a brief clarification. The term refers to all processes that require unconventional methods or technologies to be executed, since private computers, for example, do not have enough power. Big Data commonly refers to increased volume, variety, and speed of data generation.
So, the term Big Data is used when the amount or type of data requires special treatment, usually in distributed architectures in the cloud, such as Microsoft Azure or Amazon Web Services. To be more specific, it is preferable to start using expressions such as “data science solutions in a Big Data environment,” or “Big Data for business analysis.”
In conclusion
Understanding the six major data technologies is a critical starting place for every leader. Whether you’re implementing a data culture, undergoing digital transformation, or just want to keep up-to-date with digital trends, it’s essential to know what these six technologies mean and how they are being utilized in the marketplace.
