How to build analytics platforms - Part 1: UI & Teams
Reading time: approx. 2min
What does a modern data science platforms need to offer companies real added value?
Currently, new, innovative platforms are sprouting up on the market again and again – implemented with technical competence and ideally suited to the respective analytical approaches. But the question arises: Is that enough? Is it enough to develop software that allows reliable analysis and delivers clean results? Or do other factors exist that are just as important for companies to be even more successful with them?
The masses have unbelievable potential for corporate success
While thinking of analysis or data projects, one cannot get around thinking of a certain user group: Data scientists. Their task is to develop the actual analysis scripts and algorithms. But why should a company limit itself to that? Often, departments such as sales and marketing, production / manufacturing or human resources also have good ideas for realizing use cases with the corresponding data. Or they exactly ask the right questions, which can then be answered with data support. Now you can ask yourself: Should the company want that? We are sure: YES!
Only through the interaction of different groups, companies can achieve the best results in the long term. This is the big challenge that modern analytics platforms must face today and also in the future: The possibility for different groups to participate in the development, planning and implementation of data science projects. Each group contributes its own views and expertise. The modern analytics platform makes it possible to create a holistic data project that answers critical questions or uncovers previously undiscovered potentials and correlations. This point is becoming increasingly important.
CONCLUSION: Ideas for data projects can come from all user groups – if you give them access to the platform, you will generate more projects that contribute to the success of the company.
To make it work, platforms must be operable
If the upper point shows the “What” – then this point shows the “How”. If analytical platforms are to be implemented as profitably as possible in companies, it must be possible to operate them relatively simply. Remember operating systems: UNIX – users love its shell – but only with a graphical, intuitive interface the masses can enjoy the advantages of operating systems. Thus, analytics platforms have to be designed like this. If you want to access more than just absolute code cracks, the focus must be on clear menu navigation and the easy use. Only in this way, the above-mentioned user groups can contribute own projects or questions in order to increase the success of the company.
A corresponding user interface is therefore necessary and must be intuitively comprehensible. Therefore, companies must take this factor into account when selecting analytical platforms if data science is to be used comprehensively.
CONCLUSION: Only with an intuitive user interface, the company-wide use of analytical platforms can be achieved.
In the next part: How important an intelligent role and rights concept is when scaling data science projects.