Design Thinking is typical, a solution-based approach towards solving problems. It is most useful in dealing with different kinds of complex issues which are unknown, by understanding the human needs involved, and by adopting a hands-on approach in the testing and prototyping.

To tackle different problems, even if it is for big data analytics solutions or other kinds of big data solutions, you need an efficient sort of design thinking approach towards the problem.

Applying the design thinking when you are building the advanced data analytics solutions along with Big Data analytics solutions, both for the internal company teams as well as the consumers always places a priority over what is to be deemed necessary for a human interaction perspective. This boasts out what technically is a possibility and adds value to the organization.

To get you well acquainted with some of the best practices for design companies for the companies developing new Big Data Solutions, here are five critical aspects of design thinking for Big Data Solutions.

Start by identifying what the users need

When you are designing the solutions which leverage the massive amounts of consumer data, the first thing is to determine what the user need. While profitability plays a role in a business decision all the time, it is still essential to take a step back and focus on the end-user. While you are designing solutions, it is always important to define what the end users need.

In the process of design thinking, the decisions should be influenced by the people who are going to implement these solutions. Even though businesses might need a certain kind of functionality, it doesn’t mean that they should right away go with the idea. Also, before they even approach any Big data consulting services for simplifying the design thinking processes, adopting a user-centric approach is a logical strategy. The pressure of pleasing leadership always persists, which can be a hurdle in the process of design thinking. However, if you satisfy your users in the end, good results would naturally follow.

Avoid Tunnel Vision

When it comes to design thinking, a step by step process is always essential to follow. Thought out levels usually uncover the needs of the users. When approaching this process, it is still necessary to have a holistic view rather than focusing on one particular use case. Try avoiding the tunnel vision and be receptive to all the ways of approaching a single problem, which would eventually save the resources. This also saves your time from approaching Big data consulting services unnecessarily. However, if you do feel the need to contact one, do it. Sometimes, the process of design thinking becomes more complicated than expected, which leaves you with no other choice.

Define your problems

This stage helps your team gather the best ideas to establish the features and functions which are going to allow them in solving problems and resolve issues without any difficulty.

At this stage, you can start progressing towards ideating where you can ask questions, which would help you find out the perfect solutions, or at the very least, discover a specific kind of solution to your problems.

Understand how vital domain expertise is

It’s quite apparent that you would face challenges in the design phase of any big data project. These obstacles must collaborate with the subject matter experts to gain knowledge about a particular topic. You also need to realize that it is quite essential to be aware of the processes in place for a specific company within the context, be it healthcare, financial services, or even entertainment.

The key is the ideation process, as it allows the teams to work with the subject matter experts to identify what the business or domain needs. On the other hand, have an open mind towards any setback as it opens up the possibility of breaking down barriers and uncovering new features, which you previously wouldn’t have sought in that specific domain knowledge.

Test your solutions

Right after analyzing everything in the process of design thinking, you can try out a few of the answers you have thought of along with your team. This is the final stage of course, but it is still an iterative process, and the results which are generated here could often be redefined.

It is also essential to consider what the consequences of the decision would be, and how the people would think, feel, and take the decision. Always keep an open window for alterations and refinements or at the very least, you can even approach specific big data services to help you out in this process.


Design Thinking should always be a collaborative process that a team needs to work out. Focus on your end users, understand the workflow, and come up with solutions, which would be beneficial in the long term. You can also rely on domain experts or practice different big data analytics solutions to ensure maximum success with this process.

Author's Bio: 

Cuelogic is a tech company with offices in US, India and Australia. We provide leading edge startups across the globe as well as with Fortune 500 enterprises. We are having expertise in Offshore Software Development, Cloud Computing Services, Big Data Services, IoT Services and Data Analytics Services. Our partners include high growth startups, SMEs and Fortune 500 enterprises.