How to integrate a data analysis brick into your app
Data analysis has therefore become an indispensable part of applications. An application that does not offer its users this type of analysis is obsolete.
Why do you need analytics in your application?
Today's users expect to be able to use their data across all their tools.
These modern uses can involve simple analyses (the weight of documents, the most used keywords, ...) or more complex analyses (the percentage of people who opened a document, the percentage of people who clicked on this or that link while comparing it with the previous week's data, ...).
Concrete examples of the needs to have analytics in your application
- In bank account applications, there are graphs breaking down categories of expenses and income, allowing individuals to better manage their budget.
- Similarly, LinkedIn offers its users data analysis to show which keywords people used in order to find them, as well as the percentage of visits to their profile at different times of the month...
The different available options for integrating analytics into an application
Option One: Develop the data analysis platform in-house
Advantages:
- Complete mastery of the functionalities made available.
- Complete control of the technological stack.
- Customization adapted to your graphic charter.
Drawbacks:
- You need to create and manage all your systems
- System for querying and calculating indicators.
- System for generating graphs and their rendering.
- Data and jurisdiction management system to provide the right access to the right people.
Option 2: Use an existing analytical tool already on the market
Advantages:
- Saves time because there is no need to develop everything.
- Allows you to benefit from the tool's other features for other uses.
Drawbacks:
- An analytical tool and its application that is not designed to be integrated together, creating performance and efficiency problems by making the application heavy and not very responsive.
- An economic model that is not adapted to this type of use, is expensive for cursory use by users.
- Training of teams in the use of the analytical tool.
- Integration rarely in accordance with the visual aspect of the application.
Third option: Using NODATA, a hybrid solution designed for this particular purpose
Advantages :
- Optimal allocation of resources to maintain a pleasant and efficient application.
- Time-saving because there is no need to develop everything.
- Graphic customization for pixel-precise integration into the application.
- Adapted pricing: payment only per use, no overcharges when not in use.
Drawbacks :
- Training of teams in the use of Nodata.
- A configuration phase is necessary beforehand to make the most of the resources.
Conclusion
NODATA is the most suitable solution for this application because of its various advantages over its modular integration in an application.
It is a software created by data experts who have already been confronted with this kind of problem, it is based on years of research and experience in the field of data analytics.
NODATA enables application developers to integrate advanced analytical functions into their applications in a simple, fast and secure way at a reduced cost.