When it comes to data, the terminology can be complicated and we can get lost in the fine print. However, it doesn’t have to be all that difficult. Let’s take a closer look at the differences between data engineering, data science, and data analytics
Data engineering is the process of managing data from its generation to make it available. Data science consists of using data to predict the future, for example, through machine learning. Data analytics is the process of extracting knowledge from data.
Although these concepts might be similar, knowing the difference can help you get the help you need to make the most out of your data and business. In this article, you will learn the little differences and uses between the three of them. Let’s get down to it!
What Is Data Engineering?
Data engineering is responsible for helping us with data management, from its generation through complex processes to make it ready and available.
Data engineering focuses on the BigData environment, which is nothing more than circumstances in which we have large amounts of data, large flows of data, or large amounts of data types.
It is natural to have the feeling that in the early stages of our startup, thinking about BigData is just too much. Contrary to what you can imagine, not thinking about BigData puts you behind your competitors.
Implementing data engineering and planning for BigData from the beginning can give you an edge and help improve your product in the long run.
As you can see, thinking about BigData from the beginning is an element that can determine between being successful or not. Data engineering helps us organize all this and have a healthy, scalable system that meets the needs of a BigData environment.
What is Data Science?
Data Science refers to using data to predict the future through various techniques. The most widespread and used method is machine learning.
Many entrepreneurs doubt whether data science and machine learning can be useful for their startups. It is key to understand the different ranges of services available to make an informed decision.
Nowadays there is a wide variety of ready-to-use machine learning services that could serve some needs. However, the only way to understand the reach of the solution needed is to define the predictions that will be helpful for both client and startup.
Once these elements are defined, you need to decide the emphasis you want to give to creating predictive models. If the purpose is to use them, then an expert needs to step in. A data scientist will help you determine whether third-party services are enough or if you need to create your own modeling and prediction system.
The earlier the planning of the data implementation, the better the results in the long term. Click here to learn more about data-driven companies and the benefits of data!
What is Data Analytics?
Data Analytics is the process of extracting knowledge from data and trying to understand the past.
Data analytics is the most known service because it uses traditional statistics and modern mathematical methods. However, data analytics no longer stops there. Today, this service can be a valuable product to identify data-driven business models even for startups that have little to do with data.
Instead of providing raw data with no information, data analytics is about processing that data and providing insights. Data can be fundamental to your startup if you have the right people translating it for you.
As you have seen, data services can vary considerably depending on your needs! Use this information to choose the right partner and the right data expert for your product or service.
At Bixlabs, we have a dedicated team of experts who can take care of your data needs. We have worked with both startups and larger companies to implement data into their projects and give them a boost.
Thinking you can benefit from it? Contact us today!