Removing uncertainties in big data applications

Removing uncertainties in big data applications is the reason why big data adoption is slow - here are a few pointers to get started!

Big data applications can be used for many public and private operations. While some believe that big data can only be used for specific purposes, they are used in different industries ranging from predicting epidemics outbreak in healthcare to advancing grading systems in education. However, despite their immense value to different industries, there is still uncertainty over the use of big data. The uncertainty arises due to a lack of confidence in the value of big data from certain third-parties. These uncertainties often obstruct organisations from undertaking major data analytics applications and prevent them from reaping the benefits.

What do we mean by ‘uncertainties’ in big data applications?

The term ‘uncertainty’ can be used in both technical and business terms. From a more technical stance, ‘uncertainties’ in big data refer to sampling issues, differences in data collection devices or variance in environmental conditions. However, for this article, we look at the business uncertainties people have about big data analytics applications and its ability to dispel problems. While people are becoming more aware of data analytics thanks to IoT and 5G, there are still many people who don’t know about it. For sure, business-minded people might have heard that analytics can predict the company’s position for the next few years, but they do not know how it works, which is the root of all uncertainty about big data applications.

Removing uncertainties in big data projects

So what is the best way to dissuade uncertainty about big data applications? The best way to reduce uncertainty on big data platforms is to increase understanding of the technology, drive success and achieve stakeholder objectives. While every business person can’t get a deeper understanding of the technical processes behind big data applications, corporate individuals must have some basic knowledge of how data analytics platforms work and generate value for the company in question. This is where data analytics consultants are beneficial to an organisation, for they are in the best position to outline the basic mechanisms of big data analytics in a way that laymen could understand, without overwhelming them with technical information.

Communicating technical processes is all well and good, but businesses will only have their apprehensions removed when they see data analytics platforms deliver actionable, deliverable insights. Organisations will only trust or use a solution they have invested in if they know they will deliver results. Of course, along with the solution, there must be a clear, concise explanation of how the result was achieved. In my experience, all uncertainty about a solution is removed when an organisation gives clear, concise explanations on how the result is obtained. Hence, it is best to perform operations within a solution scoping exercise to ensure that data analysts can give clear, but concise explanations on big data applications.

Uncertainty in big data applications can only be truly eliminated if the analytics solutions can solve the problems an organisation is looking to address. The scope of the problem should be a realistic one to ensure that the desired outcome is attained. What is the desired outcome? Ensuring that the solution addresses the problems stakeholders have.

If data analytics solutions can deliver the desired outcomes to specific problems stakeholders have, then it will go a long way in removing uncertainties about big data applications. Knowing how to navigate and remove this uncertainty is one of the key skills stakeholders and data analysts need if they wish to incorporate big data applications into their organisation.

Will working with a data consultant help?

Working with a data consultant goes a long way in helping organisations overcome uncertainty in data analytics. Data consultants are perfectly suited to this role due to several reasons, including technical expertise and their position within the organisation. Data consultants have a thorough understanding of the data analytics platform, having used it to complete different projects, making them the ideal focal points to explain the workings of the data analytics platform in achieving organisational objectives. Their position in the company also makes them perfectly positioned for this function.

Unlike full-time employees, consultants are not caught up in the politics of the organisation because their paycheck is not dependent on how their bosses feel about them. So they have little reason to do nothing else but perform their duties. Working with a data consultant is one of the best ways to overcome uncertainty in big data applications because of their experience and position within the organisation.


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