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5 tips for designing big data apps

5 tips for designing big data apps

There is no doubt that big data is the future. By 2020 it is estimated that there will be 4 trillion gigabytes of data in the world, and revenues related to big data are expected to exceed $187 billion by the end of this year alone. If this is not enough to convince you that a lucrative future lies ahead in the world of big data, then we are not sure what will!

But what about designing applications that harness the power of big data to empower business and even save lives? Big data applications are a major force across a number of industries from healthcare to agriculture. Even online dating uses big data to improve the matches and success rates of its users.

As a result of this, big data technology is in demand, as are those who can design applications that work for and with it. Here are five tips from Finerton to you, on designing big data apps for the future.

 

1. Don’t mistake big data for anything else

Developing and deploying a big data application is very different from working with any other kind of system. Companies working in the big data realm do not typically offer, or use off-the-shelf solutions, preferring to sell tools such as analytical softwares, database management systems, and data cleaning solutions that work together in specific ways. As such, those developing apps will need to work closely with their client to create something completely bespoke. Get ready to knuckle down, roll up your sleeves and prepare for a long, and sometimes arduous process.

 

2. Be flexible

Traditionally management is very clear about what it wants and expects from a project, for example, the interface of a webpage. When it comes to big data projects however, the water is somewhat murkier as the goal is often just exploration. A company will mine large amounts of data with the hope that they will discover ‘something’ useful amongst it that will help increase revenue or streamline processes. With big data projects, possible benefits and goals are usually uncertain and only become apparent as the project grows.

 

3. Think about long term ROI

Sign offs by managers are usually based on how much money they are set to make out of it, or at least an understanding of potential pay-off. A common type of cost-justification methodology is ROI which is where the potential value is stacked against initial outgoings. With big data however, this can be hard to quantify, especially in the short term. Big data doesn't come with any guarantees and many firms actually stand to lose money (at first) on their big data projects. But as time goes on, rewards become more apparent as more use for the results of labour is discovered.

 

4. Start small

As the Internet of Everything begins to take shape around us, even more data is set to be gathered. This does not mean however that every client has trillions of gigabytes of data that needs processing. As a developer, it is key to create an app with a limited proof-of-concept that can demonstrate its effectiveness on a small scale, before being deployed on a larger scale. By starting small, both businesses and programmers are able to become more familiar with the technology as well as build on their knowledge and experience.

 

5. It’s an art not a science

When compared to a typical IT project, a big data project is definitely more about art than science. Developers need to be sure that their systems are flexible and facilitate easy use by clients. One key way to meet this need is to construct sandboxes which are practice environments where users and data scientists can play with data by using tools, languages, and environments that they are already familiar with.

Tools such as faceted search are useful as well These tools allow the classification of different information elements along multiple paths, otherwise known as facets. This means that data can be ordered and accessed in many different ways, rather than in just one predefined method. Annotation tools are another good feature that should be included in big data systems. This allows employees to add their own insights and interpretations of data, and then to send them to coworkers for comments. These are critical interactions in terms of evaluation and often lead to “eureka” moments where new insights into business operations are gained.

 

Authored by the Finerton.com News Team – April 2019
Images by Unsplash.com

Last modified on: April 29, 2019

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