Business development requires testing hundreds of hypotheses and interrelated factors. Data Science is a data science that helps analyze large amounts of knowledge.
Data Science uses machine learning (ML) Big Data tools – a set of technologies for collecting, storing, processing, analyzing data. The experts in this field call the Data Scientist, the dataset that he works with is a dataset.
We can say that Data Science is a science at the intersection of mathematics, programming, analytics and even linguistics.
How it works
A machine learning specialist – a data scientist – develops an algorithm that, based on data, predicts and models trends, behavior, patterns. Algorithm algorithm parameters are automatically based on DataScience UA.
In simple terms, an algorithm is the calculation of values using a well-known formula, for example, y = 5x + 2.
In Data Science, the formula is not known in advance, it is derived based on the data obtained. For example, an algorithm can show how productivity changes during the working day and what human factors it depends on.
Data is data and facts
It is clear that the number of incoming calls at night is several times less than during the day, so fewer employees need to be replaced. What else will be needed in the system:
- The average number of incoming calls on each of the days of the week, months, seasons (in summer, for example, they will be less due to the time of holidays).
- The geography of the location of the customers of this technical support service (for example, when it is night in Moscow, in Vladivostok it is day)
- Other data such as employee salaries.
- Self-learning systems allow you not only to mechanically build a schedule.
Let’s apply the result
However, in my opinion, data analytics consulting company does not end with identifying patterns in data. Any science project is an applied research, where it is important not to forget about such things as formulating a hypothesis, planning an experiment and, of course, evaluating the result and its suitability for solving a specific case.
The latter is very important in real business problems, when it is necessary to understand whether the found science solution will benefit your project or not. What might be the usefulness of the constructed model in our example? Perhaps, with its help, we could optimize the delivery of coffee to the office. At the same time, we need to assess the risks and determine whether our model would cope better with this than the existing solution – office manager Mikhail, responsible for the purchase of the product.
There are also great opportunities in the area of misconduct and compliance. One study found that employees who steal or commit crimes negatively affect everyone else (other employees begin to copy this behavior). AI can view organizational network data (emails, comments) and identify stress areas, potential ethics violations, and many other forms of noncompliance risk, as well as highlight red zones for HR or compliance directors to they had the opportunity to intervene to prevent fraud.
(Among the suppliers in this area are companies such as TrustSphere, Keencorp, Volley, Cornerstone, etc.)