But a great skill of a professional in this area is also to generate automation and efficiency with a great impact. Which generates great savings for companies.
With the exponential increase in data, data science has advanced at a very high speed in recent years. Companies are still unable to keep up with all the benefits that this area produces. Thus, although it is possible to develop disruptive innovations, there are a number of simple opportunities for a professional in the area, but which, when implemented, can bring enormous gains.
It is no wonder that the demand for this professional is french email address list in all segments, and is no longer a role only for large companies.
Therefore, we will present here some actions that a data scientist can perform and that will bring efficiency to the company where they work.
Automation of repetitive tasks
Automation of repetitive tasks
Machines must do machine work so that humans can do human work. This is a phrase I often use to encourage people to look at their daily lives and notice what can be automated. The problem is that someone who is not familiar with the possibilities of automation will always think that they have already optimized everything they could.
If you are a manager or entrepreneur, I challenge you: Call a data specialist to look at your company and point out which areas could be improved and what the impact would be? You will be surprised!
A process such as transferring information from your CRM to financial software can be automated. Or, you can generate initial support via chatbot to reduce the workload on sales staff. Or you can program automatic responses based on the grade of a selection process. In short, several tools already exist for this purpose, such as Zapier , Google Sheets, among others, which provide APIs so that specific programming can be carried out.
Simply generating a WhatsApp link with a standard response so that your support team can contact customers may seem like a small amount of time savings in an iteration. But when we have to contact 40, 50, 100 people every week, this automation has a huge impact.
Although it is not within the natural scope of work of a data scientist, they will generally find it easy to perform these actions, as they end up having a programming and business vision.
Development of clear KPIs
Development of clear KPIs
Having KPIs defined for a team or company is essential for team engagement and autonomy in execution. However, defining a goal can be much more complex than it seems. The first challenge is knowing what to look at? With so much data available, we tend to get lost and want to measure everything as the main thing, which is bad.
We need to find KPIs that are truly relevant and that can be monitored. Furthermore, a subtle error can send efforts down the wrong path. An old story tells of France, for example, when it was frightened by the increase in rats in the cities, causing various diseases. The government's measure to reduce the number of rats was to pay the population for each dead rat. How can we read this KPI? The more dead rats, the better. In conclusion, several people started raising rats to kill and sell to the government, increasing their population even more.
Therefore, a professional look at data and KPIs can be essential to avoid misdirecting the team and wasting the company's resources and energy unnecessarily.
Efficient decision making
Efficient decision making
We need to make decisions all the time. And what do you evaluate in these moments? Yes, feeling is important, especially for experienced professionals who already have models in their subconscious that they don't even recognize but that work. However, with the increase in variables, we need to rely on data to make the best choices. And here at this point, as important as having the data is that the data is correct. Because worse than not having data to make a decision would be being certain about incorrect data.
The quality of data for decision-making is therefore related to the assertiveness of the data, the interpretation and even the time in which it is made available.
Real-time data visualization tools such as Power BI, Qlik , Tableau , among others, have entered the market with a vengeance, as they enable easy visualization of data through dashboards and dynamic reports, enabling user interaction.
But for them to work, a prior structure that processes the data and knows how to ask the right questions is essential.