PwC says data analytics are transformative

Investing in information technology has always been an important decision for executives to take－and 41 percent of business leaders surveyed say they will need complex analysis using machine algorithms before deciding important issues, according the report by PwC.

The survey was based on interviews with more than 2,100 company decision-makers and leaders across 15 industries including the healthcare sector, banking and capital markets and the communications, energy, utilities and mining and technology sectors.

The three most important big decisions facing executives are launching new products and services (31 percent), entering new markets (17 percent) and investing in IT (15 percent), the survey said.

Highly data-driven companies are ready to compete. Companies are looking to grow in new markets and 48 percent of them describe themselves as highly data driven.

A significant role for machines is emerging so that 41 percent of leaders say big decisions will require analysis using machine algorithms.

"Data can be an extremely underutilized tool, and a company's capability to access the right data, at the right time, and then look at it through the right lens, can make or break a bottom line," said Dan DiFilippo, PwC's Global and US Data and Analytics Leader.

Companies are beginning to understand the power of forward-looking predictive and prescriptive analytics, but there are still 29 percent of companies who use predictive analytics.

"Leaders are stuck at a crossroads, with a small amount of decision-makers polled stating that they're just trying to survive in a state of disruption. This survey demonstrates the often unrealized value of data to lower the inherent risk in decision-making, and sheds light on exactly how companies can reassess the data they have to become stronger and increasingly competitive," said DiFilippo.

The survey encourages companies to ask the right questions that will lead to data solutions. Companies need to not just collect big data, but question their sphere of discovery, understand how to find the questions worth asking (before looking for the answers in the data), build an awareness of who is responsible for making decisions and taking action and really think about how to track outcomes to determine if the best approach is being taken.