In order to make sure that the advanced application in Analytics stays relevant for business operations, the data scientists can be seen working in collaboration with employees who have expertise on their business data.
Experts in data manipulation
Data scientist have expertise in data manipulation and in the building of models for analysis or running against it. However, they may be missing in their skill set the relevance and the meaning of a given data from business, and this is pushing the teams of data science to form linkages with the business analytics people and other employees who can then help them with a better understanding of the data that they need to analyze.
Get them all together
Having ties closely to the business data gurus can also help in the reduction of the work data scientist have to do in preparation work for the data upfront. Data preparation usually ends up eating up a huge chunk of the data scientist’s time, but if they leaned on already curated chunks of data sets which are created by people who work in business analysis frees up the data scientist, so they are better able to focus a lot more on the analytic duties part of their work – and this guarantees that they are actually working on the data that is germane for the business operations. This is in line with the ultimate goal that everybody had set out to achieve as a team. And that is to ensure the work of advanced analytics done by the data science team well aligns with the actual business requirements and the issues the business is facing.
“We always make sure that there is a representation of business (during the process of analytics) because there is no achievement in the manipulation of data for not a good reason, “said someone who follows data science very closely. “Our mission does not mean that we have to chase data just for the sake of data”.
Another person we interviewed said that their company typically pairs up the data scientists on a role with people trained in linguistics. Linguistics people are helping in building algorithms for NLP for ingestion of structured data. Data Scientists eventually prepare it to do analytics along with helping out the other scientific leadership-based areas like regulatory compliant certification and the chemicals, so that they can pinpoint the work done in analytics on a given specific business problem.
They work as a single entity. This interdisciplinary collaboration brings a wide spectrum of skills together, which includes experts in both traditional scientific methodologies and data science. Along with having the focus on analytical applications on relevant concerns, aggregating the various teams also helps in speeding up the process of analytics.
So, as you can see that data science cannot and should not work in isolations. They frequently need to team up with other disciplines. They, however, do possess some core skills that they use on their jobs on a daily basis. So, if you want a piece of the pie you will first need to get comfortable with those core skills. We recommend you get the right Data science course to get started on your journey.