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5 Things To Keep In Mind For Building Data Driven Application

In this fast growing World of Technology, Innovation is at the heart of every application development. Innovation in the development of the application is not just for big enterprises, but even startups invest a big portion of their money in products that turn out to be their solution to a lot of problems. Despite traditional applications, data analytics is at the center due to all new SaaS companies coming ahead to develop an amazing application for web and mobile. Doing so, these companies are not just keeping data analytics at the center but are also bring a revolution in the software industry.

What are Data Applications?

The data-driven application is the backbone of business owners and decision makers. These smart applications help the owners and decision makers with accurate data and insights without further assistance from software or BI tools.

Here are a few suggestions to keep in mind while building a data-driven application:

1. Thinking ”Insights” is the key!

End-user needs require proper research while developing an application. One thing you need to make sure is that the tool which you would develop should not just help you execute task but also give amazing insights to the end users basis the data collected. Doing so would surely improve the user experience. The main reason for developing a data-driven application is to leverage user data and delivering insights.  For example, A predictive 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.

2. Derive accuracy from learning loops

One of the most important thing that helps in developing a data-driven application is data learning loops. You need to question your own operating norms in the form of retrospectives. For example “What’s going wrong or not well in our current process? what can be done? and so on. Make sure when you develop a data-driven application that it should not only overcome challenges but also predict accurately.

3.  Going Beyond BI, to improve the experience

The data-driven application should not extend analytics arm. It should prescribe and predict, rather than just stop at data visualization or dashboarding. Moreover, the data-driven application should not be restricted to data but it should solve problems.

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 memory recall and positive word of mouth.

4. The horizontal and vertical strategy

Consider horizontal functions such as 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 the 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.

5. Success Stories

Data-intensive solutions for providing tailored customer experiences to end users are now trending among companies.

Talking about the consumer side, companies like Amazon are patenting their model which predicts what do customers want to buy to plan to ship in advance. Amazon’s anticipatory shipping algorithm is setting an example, allowing the data-savvy company to greatly expand its base of loyal customers. Companies such as Uber also use data and analytics from customer bookings and drivers to predict the demand and push drivers in nearby locations ensuring quick service. Companies such as Zillow are disrupting the real estate market with their marketplace. Zillow has developed a proprietary system that runs models in the R programming language. The R language enables Zillow to maximize flexibility and in turn, the predictive analytics help consumers make more intelligent real-estate decisions. Zillow has brought transparency for consumers, giving them the data and tools they have always desired.

An old school BI software is certainly helping a lot of mid-size companies to visualize data and transform it into meaningful insight. However, with advancement in digital technologies and humongous data which is growing rapidly, simple dashboarding or visualization solution isn’t enough. Traditional companies need to reinvent the wheel to remain competitive and offer innovative data-driven applications that facilitate nimble decision making. These apps should consider the pain points and apply intelligence to predict and prescribe over and above data visualization or analytics. This will surely be a game changer for SaaS companies in 2017.