Raw and unstructured data hides potential that is useful for business. But extracting them and using them correctly is a difficult and time-consuming task. Let’s clarify what this gives companies and how to improve business efficiency.
Data Science is closely related to Big Data and data analysis. All these technologies involve the processing of large amounts of information and drawing conclusions based on it. However, if Big Data and data analysis help to study historical information, then Data Science offers to look into the future (for instance, to predict the demand for goods and services).
Data Science tools have a very broad application and will be useful not only for large corporations. The business processes of each company generate a large array of unstructured data every day, and if you understand them, you can find new growth points.
The use of Data Science is not limited to any industry or functionality of operations, its application is possible:
Determination of customers who may refuse to cooperate with the company
Analysis of the intensity of sales channels and their future efficiency
Analysis of medical tests and laboratory data
Optimization of the supply chain by calculating free storage capacity
The introduction of Data Science into the company's business processes is not very different from any other automation. All stages are quite similar and include the following.
Initial planning and evaluation to help define the target state of the system.
Creating data models. This will require either the company's own accumulated statistics or access to open data. Array processing may require an API for users to simplify data acquisition, handling, profiling, and visualization. The most important thing here is to choose an optimal tool that will have a user-friendly interface and meet all the functional requirements of the model.
Data Model Evaluation. Only with high model accuracy can you get worthwhile data that will justify the investment.
There are a lot of tools for analyzing data and building models, so when choosing, you should pay attention to the following functionality.
Convenient interface with the ability to do collaborative work. The development of the model is divided into several stages, and all participants should have convenient independent access to the system at any time.
Flexibility. The ability to integrate with other systems is always a plus, as it facilitates the formation of arrays and their interpretation.
Stability under high loads. Analytics can expand, which means that the platform must behave stably when working with large heterogeneous arrays and a large number of simultaneous users.
Speed of implementation and adaptation. It is the most time-consuming process. So, the easier the implementation of the service is, the closer the start of analysis is.