5 Considerations For Building Data Driven Applications

22 / Feb / 2017 by kinshuk jhala 0 comments

Innovation is at the center of application development. A lot of established companies as well as startups are investing big money in product ideas that have the potential to solve business challenges. While traditional applications are still in place, new age SaaS companies are developing amazing applications for web and mobile keeping data analytics at the center. These companies are turning the table to bring about a revolution in the software industry.

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 considerations and suggestions while building a data driven application:

1. Think “Insights” at the core

Developing application requires a lot of research around the end user needs. If you plan to build a tool, make sure the tool doesn’t 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. Agileone for 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.

2. Derive accuracy from learning loops

Data learning loops can be really 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 not only to resolve challenges but also predict accurately empowering decision making. The predictions should fare at most times.

3. Go beyond BI, prescribe to improve experience

The data driven application should not be an extended analytics arm. It should not just stop at data visualization or dashboarding, but 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.

4. Consider 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 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. Research about the success stories

A lot of companies are now using data intensive solutions for providing tailored customer experiences to the end customer or business. Talking about the consumer side, companies like Amazon are patenting their model which predicts what do customers want to buy to plan shipping 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 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 a meaningful insight. However, with an 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.

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