Shapelets, a platform specialising in Data Science, lists the features that an analytics platform must have so that data scientists can take their work to the next level.
Data has become one of the most important assets that companies have today as it allows them to make better decisions for their businesses. That is why it is so important to have tools that facilitate their analysis and to have a team of data scientists capable of extracting the most relevant information because "the volume of data collected by companies is increasing, which forces businesses to develop systems that allow them to find the most relevant data", according to Shapelets, a Spanish platform specialised in Data Science.
However, not all companies manage to get the most out of their data scientist teams, as professionals often work with different platforms that complicate the agile extraction of conclusions from the data, as well as its communication. In this sense, Shapelets points out that "it is important to have centralised management of data analysis, allowing all professionals to use the same tools and use the same standards so that the information gathered is useful".
With the aim of pointing out the main needs that a platform for data scientists must meet, Shapelets lists the following five essential features that any data analysis tool must meet:
Scalability. For a platform to be useful to the data team, it must be scalable as the business advances and as the number of professionals working with the information grows, or simply as the volume of data being worked with increases. To achieve this, the platform must support a large number of simultaneous users and be able to increase data ingestion, processing and storage capacities in an agile manner.
Collaboration. An analysis can be all the more enriching when more people work on its development and have access to its conclusions. Therefore, it is important that all professionals working on the platform have appropriate access to all data and resources whenever they wish.
Integration. It is essential that the platforms used by data scientists can be adapted to new resources that facilitate their integration. In this way they will be able to access new tools that appear on the market or that come from new academic developments, avoiding the use of obsolete tools.
Easy usability. Another key element to look for when starting to use a platform is that it can be installed and learned to be used quickly and easily. When selecting a service, it is important to assess that it can be used immediately, without compatibility problems between systems and that its adoption will not involve major difficulties.
Autonomy. In order to extract value from data and communicate it within the organisation without any barriers, data scientists must be able to individually create the spaces and environments they need without having to rely on other profiles within the company.