Spending on data analytics is rising, and CMOs report that one of their most effective applications of predictive analytics has been to leverage consumer data to support intuitive hypotheses. With all the buzz about big data, recent studies have shown somewhat conflicting results regarding the extent to which data or intuition is used in decision-making. Now, a new study [pdf] from PricewaterhouseCoopers (PwC) indicates that few senior executives rely most on data when making big decisions. Even so, many believe that data analysis is bringing into question the credibility of intuition or experience.
The study is based on a survey fielded by the Economist Intelligence Unit (EIU) among 1,135 senior executives from around the world, more than half of whom are C-level executives or board members. Most respondents – roughly three-quarters of whom hail from companies with at least $1 billion in revenues – expect to make big decisions either every month (44%) or every 3 months (35%) this year, with a plurality 30% saying their biggest decision will be opportunistically timed (based on an opportunity that cannot be ignored). With the most important of those decisions being related to growing their existing business, some 30% of respondents estimate that the likely impact of their most important decision will be at least $1 billion, in terms of their organization’s future profitability.
So how are those decisions being made? According to the study, 49% of respondents agree that “data analysis is undermining the credibility of intuition or experience,” while just 21% disagree. At the same time, when asked which of a selection of inputs they placed the most reliance on for their last big decision, 30% of respondents pointed to their own intuition or experience, while 29% relied most on data and analysis and 28% on the advice or experience of others internally. In other words, a majority relied most on experience, whether their own or someone else’s. (The study does not break down those responses by size of the decision, but the vast majority of respondents said their big decisions are worth at least $50 million down the road, so they’re not small…)
Separately, the study finds that about one-third of executives describe their decision-making process as “highly” data-driven. Notably, these respondents are more likely to note significant improvements in big decision making over the past 2 years.
The authors note that “in reality, however, experience and intuition, and data and analysis, are not mutually exclusive,” noting that the real challenge is figuring out how to integrate the two. Research has differed to some extent on which area is dominant in this interplay:
- A recent survey found that while more executives based their management decisions on data than intuition, a majority would re-analyze the data if it contradicted their gut instinct; while
- A more recent study found 61% of US business decision-makers believing that human insights should precede hard analytics when making decisions.
That latter piece of research also suggested that the biggest barrier to more data-driven decision-making is an insufficient analytical capability. In fact, one-quarter of CEO respondents to the PwC and EIU study reported that they lack the skills or expertise to make greater use of big data, and 52% said they have previously discounted data they don’t understand. (Non-C-suite respondents were less likely to agree on both counts.)
While those signs point to troubles with data-driven decision-making (and the study notes that “data and datasets are already biased even before human beings start analyzing it”), the tide seems to be turning. For example, more than 8 in 10 respondents believe that they have a sufficient pipeline of talent to be analyze the quantity of data they collect. Moreover, the top 3 changes to big decision-making during the past 2 years, per the study, are:
- The number of people involved in the decision;
- The use of externally-sourced data; and
- The use of internally-sourced data.
So what should decision-makers lean on when making decisions? In the opinion of the study’s authors (the Economist Intelligence Unit), “data and analysis should enhance intuition and experience,” and while it’s becoming common to test different scenarios through data, “management intuition and experience will remain critical for interpreting the results.”
- Respondents were more likely to describe their big decision-making as somewhat (51%) rather than highly (32%) data driven.
- Asked to rate various aspects of data and data analysis to big decision-making at their organization, respondents were most likely to rate the sufficiency of data (72%), the insight derived from data analysis (67%) and the reliability of data (65%) as excellent, while being least likely to rate the timeliness of data (54%) as being excellent.
- Executives are most often using data and analytics to optimize the value of customers to their organization and their mix of products and services.
- Some 46% of respondents believe that relying on data analysis has been detrimental to their business in the past, while 27% disagree.
- Respondents see the chief barrier preventing more use of data and analytics in big decisions to be the quality, accuracy, or completeness of the underlying data not being high enough.
About the Data: In May 2014, the EIU surveyed 1,135 senior executives, over half (54%) of whom are C-level executives or board members. This sample also includes 50 senior representatives from government and the public sector. Respondents come from across the world, with 28% based in Europe, 35% in North America, 24% in Asia-Pacific, and the remaining 13% from Latin America, the Middle East and Africa, although most (72%) companies in the sample operate in more than one region.
A total of 18 industries are represented in the survey. Around 10% of respondents come from each of the following industries: banking & capital markets; technology; and energy, utilities & mining. The majority (74%) of companies reported annual revenues last year of at least $1bn, and no company had annual revenue below US$250m. The ownership of companies in the sample is evenly split between publicly-listed companies and private, family-owned or state-owned enterprises.