The benefits of augmented analytics are clear, but there are several factors need to be considered before procuring one for your enterprise. Source: Shutterstock

The benefits of augmented analytics are clear, but there are several factors need to be considered before procuring one for your enterprise. Source: Shutterstock

What to consider when picking big data analytics tools

DIGITAL age has brought immense value to data, and practically every company is seeking to monetize the treasure trove of data that is in their possession.

Enterprises have realized that advanced data analytics has a direct correlation to optimal business performance and accordingly business intelligence platforms are integrated with core business process via functions like mobile analytics, augmented analytics, dashboard extensions, and APIs.

Augmented analytics will be the next wave of disruption in the data analytics market, according to Gartner.

It will leverage AI technology to transform how analytical content is generated, presented and consumed, and allows data and insights to be on the same platform rendering the need for switching between applications, a thing of a past.

For instance, embedded analytics in a CRM would provide sales personnel customers account data and insight in real time which could potentially determine the next step with the customer.

The benefits of augmented data analytics have been made very clear, but the real question is should enterprise procure the solution from a different provider or develop it in-house?

The answer depends on several factors – the current state of operations, business priorities and resources at hand.

Financial cost aside, here are some other factors that need to be taken into account in deciding the proper analytical tool for companies.

Visual analytics

Visuals serve as a powerful tool to highlight and present valuable insights. For a platform to generate insightful visuals, it must have a robust analytical capability.

A competent analytical platform will integrate AI capabilities to shed light into different dimensions of the data and at the same time flexible to retrieve required information or answer any query in real time.

Speed of deployment

Most of the businesses cannot develop an analytical tool and may run into challenges to deploying it speedily. They may even have limitation to what they produce, and with the limited resources at hand.

The more seamless the process of integrating the solution, they better it would be to the company.

Addressing needs

It is essential to keep in mind that solutions need to solve a widespread problem and add value to the business. The tool should not only be useful for the short term pinch but align with long-term goals of the company.

Ideally, any data analytic solution that is picked should markedly improve the performance of the company, which means the company should determine the areas of operations it wishes to optimize and look into solutions that will address specific needs.

Agile and scalable

Analytical solutions should be designed to grow with the business, instead of a pre-planned timetable. Companies should look into the pay-as-you-grow strategy which allows them to scale the services based on the needs.

The flexible purchase options also will allow for optimal costing and efficient use of resources.

In a nutshell, companies that are serious about growth may no longer have a choice whether or not if they want to integrate augmented analytical solution to their businesses, but how and which analytical stack.

But most enterprises, however, need carefully consider a solution that fits not only their business goals and budget but also how fast they can start benefiting from the integrating the technology.