We are all aware that big data plays a pivotal role in your business and marketing decision by providing reliable insights, empowered by Artificial Intelligence or Machine Learning. It is a dream for every enterprise to have such technology helping to analyze what is happening and what is going to happen within its market and consumers. It is beautiful, where you can only look at your monitor and be inspired by opportunities to gain more profits.
To reach that dream, enterprises are looking to build the team consisting of packs of IT enthusiast and experts in handling massive data exposure. Those people are being called as Data Scientists and Data Engineers who are assigned to read, analyze, maintain and present your data in a silver plate.
However, many companies are still in doubt with their functions and differences due to a lack of experiences and vocabularies in this science of data. Failing to prepare this adequately, in the very beginning, can doom your enterprise’s big data efforts. Let us tell you a little secret about them from scratch.
What are Data Scientist and Data Engineers?
Data Scientist is someone who makes big data understandable by extracting raw data, processing, and delivering it with the insights for analytics to solve business problems. Contrary to that, Data Engineer is someone who provides high-performance infrastructures and tools to deliver insights from raw-data sources which are then utilized by Data Scientists.
Simply, Data Scientist depends on Data Engineer. Both skills from these players are critical for the data team to function properly. The slogan of “It takes two to tango” applies to both of them in order to leverage your big data values. It is highly improbable that you will be able to get a single individual who is both a skilled data engineer and an expert data scientist. Therefore, you will need to build a team, where each member complements the other’s skills. And it is critical that they work together well.
What Data Scientists and Data Engineers Do?
We can’t deny that there is a significant overlap between data engineers and data scientists when it comes to skills and responsibilities. To see it clearer, let’s look at the different job desks between these MVPs:
|Data Scientists||Data Engineers|
Thus, recruiting Data Scientists without Data Engineer means leaving the former in the uncomfortable—and expensive—position of either being forced to dig the data inappropriately. Or either recruiting the latter meaning to have a pack of data with a blank purpose to do. Neither option is a good use of their capabilities or your enterprise’s resources. Therefore, know more to save more with them.Despite its different definitions, skills and responsibilities, they are members of one team where each member complements the other’s skills and it is critical that they work together effectively. They need to be well envisioned and resourced to prevent inefficient cost due to their expensive rate.
Here in Dattabot, we have all the experts of Data Scientist and Engineers to support you in unlocking the value of your data for your challenging business.