The number of industries, where Big Data gains much traction, has ballooned in recent years. Incumbents are ready to invest at their fullest in developing and designing data analytics solutions to maximize their future profits and to streamline business processes and analytics.
According to International Data Corporation forecast, commercial purchases of data-driven hardware, software, and services are expected to maintain a compound annual growth rate (CAGR) of 11.9% through 2020 when revenues will be more than $210 billion.
While the hardware and service solutions are large of a one-size-fits-all character, data applications must be designed so that they can comply with the unique demands of a certain company or enterprise. So to become a game-changer in your industry, you have to get custom data application software developed and designed.
This time around, it’s all about Big data cases, so let’s dive deeper into it.
Where can bigdata apps be used?
Business applications, specifically designed to handle large-scale data sets and help perform data analytics are increasingly utilized in many industry areas. These areas include:
1.Finance & banking.
Early fraud warning
Market activities monitoring.
Pre-trade predictive analytics.
2.Media and Entertainment.
Audience needs prediction.
Engagement and retention enhancement.
Predictive and preventive crime analytics.
Legal and tax compliance.
Product quality monitoring.
Delivery and procurement strategizing.
Patient data tracking.
Personalized lab test administrating.
Medical data visualization.
Historic and demographic data orchestration.
There are just a few business domains, where data apps are by and large used nowadays to address specific industry needs. What’s more, web and mobile applications designed to assist insurance, education, retailing and eCommerce spheres are also within the list. For instance, there are certain UX design trends for fintech app solutions that drastically improve customer experience and elude performance bottlenecks.
5 factors to consider before designing a big-data app
1) Define your app’s users.
When you are about to have a robust data application designed to perform data-driven tasks, firstly identify its end-users. If you have a large-scale business, the data-driven app must access different types of information required for various departments. A properly designed user interface should provide a series of options depending on multiple users’ business roles.
2) Zero in on your app’s usability.
Handling huge amounts of data may be overwhelming at times. Even if software developers ensure the efficient app’s architecture, this is where designers should jump into play to provide actionable UI/UX design solutions. Vast datasets require high-performance rates rather than all these bells and whistles, which are so commonplace for everyday non-business web and mobile applications.
So keep your UI design simple and minimalistic, empower it with well-designed data tables, thus offering a number of meaningful options to your app’s users.
3) Optimize app load times.
Business apps need to be designed so that they can show greater speed when it comes to dealing with data tasks. Ensure your app’s design solution leverages all the advanced technology insights. It will enable users to timely access the data they need.
4) Ensure your apps’ scalability.
Evidently, your business keeps growing to expand its footprint on the market. With this in mind, your pp must not only demonstrate great performance rates here and now, but also in course of time as your company continues to expand its presence.
5) Tap into data visualization design.
To utilize visualization techniques while designing business-specific apps should not be underestimated. Modern UX design trends encourage app creators to present sophisticated data sets in form of simplified yet comprehensive tabular data, interactive graphs, and charts. This UX design approach makes it far easier for end-users to gain valuable insights and provide viable data analytics results.
I hope you’ve enjoyed this wrap-up review on how to design a decent big data app. Do not hesitate to contact us and get more findings on the matter.