Although more and more How to companies are collecting data, not all of them are able to make the most of it. In fact, companies are often unable to extract all the information they could from the data they store, since the true value of the data does not lie in the collection capacity or volume, but in the value that is extracted from it.
Shapelets , a Spanish platform specialising in Data Science, explains in a press release that “in order to extract the full potential of data, companies must be able to perform a complete and correct analysis of it. Only a good interpretation of data helps businesses to extract useful information that can show trends and provide relevant information for decision-making, the creation of new products or the development of innovative services.”
Thus, to successfully implement
Data analysis in a company, it is necessary to create an environment in which data scientists can be efficient. An objective for which Shapelets collects the following tips:
Use analysis tools that are compatible with each other . One of the biggest problems when analyzing data is the time it takes for scientists to access the information. In many cases, access to the data depends on a superior who must authorize said access, which causes delays in the analysis. But not only that, but also, usually the tools used by the teams use different languages or environments, which also delays the analysis. Given this, it is important that when implementing a data science team, efficiency is sought by using compatible tools and inefficient tasks are reduced.
Distribute the information obtained from data analysis to all areas of the company . Sometimes the results Job Function obtained from data analysis are not integrated into decision-making processes because collaboration between departments is not developed. To avoid this, it is important for business teams to know the processes carried out by data scientists and the results they can obtain, so that their work is better understood and the real value it can offer to the company is given.
Control and optimize the time
Due to the use of different tools, teams spend a lot of time on support and maintenance activities. This obviously reduces the time spent on data extraction and analysis. If IT managers have to spend most of their time solving problems and updating environments, the time left for data scientists to carry out their work on the tools is less than desirable.
Having the tools to achieve the right level of analysis at a reasonable price . Sometimes, models or Brazil Email List autonomous learning systems are used that are not scalable and cannot be implemented in applications. This poses a challenge and a delay when performing analysis or simply does not allow them to do so. Therefore, it is important that companies evaluate the methods they are going to use before implementing them and verify that they are scalable to improve efficiency.
