Data-Driven Applications

Data driven applications are smart applications as they help decision makers and business owners with data and insights without the need of resorting to a third party software or BI tool. These applications can predict and prescribe by analyzing data using algorithms.

Outlined below are a few DataSev’s considerations and suggestions when building a data driven application:

Developing application requires a lot of research around the end user needs. If you plan to build a tool, make sure the tool does not just help execute the tasks but also give amazing insights to the end user basis the data collected. This will improve the user experience and recall drastically. The main logic of data driven applications should be leveraging user data and delivering insights. An example is a predictive analytics platform that helps marketers determine the likelihood that a given online customer will make a purchase using algorithm and machine learning capabilities. This goes beyond the usual customer relationship data management.

Data learning loops can be helpful for developing data driven applications. Question your own operating norms in the form of retrospectives. “What’s going well or not well in our current process? How should our process change next time?” and so on. Make sure that the data driven application is built to not only resolve challenges but also predict accurately empowering decision-making. The predictions should fare at most times.

The data driven application should not be an extended analytics arm. It should not just stop at data visualization or dash boarding, but also rather prescribe and predict. Prescribing the next steps after evaluating data sets would add a lot more value in real-time decision-making. Moreover, the data driven application should also not confine itself to data. It should focus on problem solving. While a typical big data company focuses on data, a BI platform on analytics, the next generation application should blend the two of it and additionally prescribe and predict. While building an app, make sure that user experience is at the center. Nimble decision making without manual thought process creates a memorable recall and positive word of mouth.

Consider horizontal functions such as health, sales, hr, and finance and try to build data driven applications that suite to their needs irrespective of the industry. On the contrary, look at vertical strategy and develop industry specific application. Some industries such as healthcare and retail where there is an extensive data collection, it is important to have an app that can interpret data and enable better decision making.

A lot of companies are now using data intensive solutions for providing tailored customer experiences to the end customer or business.