Companies might be acing in their artificial intelligence (AI) and Big Data initiatives, but their focus remained on being data-driven rather than being business-driven. In a rush to win the race to digitalize big data, firms tend to neglect the use cases often.

Big Data and Analytics Transforming the Auditing Process

Companies are aggressively pursuing AI and big data to become increasingly data-driven, but in the process, they miss out on giving importance to creating a long-term sustainable business model. As per the Harvard Business Review, 77% of executives consider the adoption of AI and big data as a significant challenge. Even if companies focus on being data-driven, the process of adopting the change and implementing it in the business operation might be daunting.

Firms lack a strategic plan or a long term business direction other than digitalization of data accumulation and storage. Even if the focus is on converting the business into a data-driven model, it is essential to position the data to derive impactful business results from them.

Many organizations face the same challenge. An inherent problem with the adoption of enterprise technologies is that firms just can’t plug them into the system. Instead, most analytics, AI, and big data projects go through a series of iterative testing and processing until there is a consensus among data scientists, IT, and business users regarding its implementation. Some of these projects make it to completion while some don’t, and the risk is that the project work becomes a data-driven exercise and not business-driven.

To stay aligned to business-driven goals, firms need to avoid the trap of turning into a data-driven organization instead of a business-driven company. To ensure this, firms need to stick to the below pointers:

  1. Identify business cases before investing in technology

Identifying and referring to relevant business cases is very important. If a company can’t visualize cost reduction, work environment, measurable revenue, or customer satisfaction benefit from the analytics, then spending on technologies like IoT or AI is equivalent to wasting the budget.

  1. Getting out of the pilot mode

For the last couple of years, big data, analytics, and AI projects were an exception when it came to producing measurable business results as they were new technologies being run in experimental pilot modes. But, now all these technologies have reached maturity, and the management of firms expects them to produce tangible business results just like other transactional data systems.

  1. Communicate methodology and project status

Even if big data projects are considered to be in a mature mode to produce results, this doesn’t change the truth that they are more challenging to implement than the transactional data systems.

Data Is the New Oil, Even for the Oil and Gas Industry

AI and Big data analytics use algorithms that have to be refined until they reach at least 95% accuracy before placing it in production. Consequently, there is a different approach to big data projects and testing regarding it reaching an acceptable level of accuracy. This repetition can give firms the impression that a big data project isn’t running well because of the continual retesting and modifications in the adoption process. Because of this, CIOs and prominent data leaders need to understand the differences between transactional and big data testing methods. The management of firms needs to understand the process of technology adoption and carefully align it to overall business success. Big data projects should always be business-driven from inception.

It can never be enough to collect, process, or curate data for data’s sake, expecting that the business use cases will follow. Keeping business goals in mind is essential, as firms need to focus on prioritizing business goals.