New local research investigated the extent to which data analytics is influencing managerial decision-making – and it seems many top executives still prefer to use their intuition.
By Nazim Taskin, David Pauleen, Ali Intezari and Shane Scahill.
‘Big data’, ‘data analytics’ and ‘data science’ may be the latest buzz words, but for many business people, these new frontiers remain overly technical and complicated.
New research conducted by the Management Analytics and Decision Making Research Group at Massey University investigated the extent to which data analytics is influencing managerial decision-making – and it seems many top executives still prefer intuition.
Despite this, the survey showed exponential growth in the use of big data, with 87 percent of the participants saying their use of data analytics for decision-making had increased.
But to put this in context, the survey also revealed that only one in five managers said they use data analytics for decision-making on a daily basis. Perhaps even more telling was the one-quarter of managers who confessed they had only a modest knowledge of what big data is, or what it can do.
Many senior executives still distrust data
There were several reasons for the slow uptake of data analytics among New Zealand businesses, including concerns about its reliability.
Nearly two-thirds of managers said they had no confidence in big data, relying instead on their intuition and experience to make decisions. They were concerned about issues such as data quality, reliability, relevance and data access.
There was also a clear difference between the attitudes of senior and middle managers. Managers who favoured using analytics were generally less senior managers who were not in a position to use big data insights for strategic company decisions.
These managers reported difficulty in getting the support of top-level management to invest in, and use, data analytics tools.
Overall, only one-third of managers were moderately or actively involved in technical analysis, suggesting the majority of managers are not yet effectively leveraging the benefits associated with big data use.
The study also found those involved in higher level decision-making, such as top executives, were more likely to value their own intuition. These executives often relied on other managers within the organisation to generate big data insights, and while they were happy to use data analytics to confirm their own intuition, they also ignored insights if they conflicted with their intuition.
The perceived value gap
What senior managers and top executives considered of value was also very different. For example, mid-level managers are more likely to evaluate insights in terms of how they facilitated business processes, while top-level executives evaluated big data outputs against the company’s bottom line.
Top executives didn’t always understand the longer-term value of insights if they did not have immediate economic benefit. The key way to overcome this impasse is to acknowledge these prejudices and engage in open communication. Investment in analytics skills training for decision-makers is also important.
We asked managers to reflect on positive and negative experiences they may have had in applying analytics during decision-making in the past.
While 85 percent could recall positive experiences, just over one-third also reported negative experiences using analytics. It was also true that managers who incorporated analytics to a higher degree had more positive past experiences.
Unsurprisingly, the more managers experienced positive outcomes, the more trust they had in data quality and source reliance, and the more confident they were to use analytics when making decisions.
How to become a data-driven organisation
There are several steps that New Zealand businesses can take to develop a data-driven culture. Overall, the survey highlighted that strong leadership, a data-driven culture and a stronger understanding of the value of analytics were needed at the highest levels of management.
In order to build managerial confidence in using data analytics, business processes should initially be set up at an operational level, such as data entry.
Every participant in the process from data collection, analysis to top-level decision-making, needs to have a certain level of analytical skill and field knowledge (contextual knowledge) to facilitate data analytics practice.
Equally important in building the confidence of top-level management is getting relevant data in a timely manner.
Top level executives will also need to have enough knowledge to be able to judge the relevance and value of information given by analytics when making decisions.
Our research suggests that overcoming the knowledge deficit among executive management can only be achieved by these managers receiving training that they can put into practice. Only then will businesses fully realise the value that big data and analytics offer.
Our findings also raise the question of whether the managerial skillset should be extended to include analytics training and knowledge. While business analytics have become a key component of many New Zealand organisations, many are not getting the most value from it. This is largely due to the lack of technical understanding of analytics within top tiers of management, many of whom continue to rely largely on their judgement or intuition when making important business decisions.
On a positive note, it is clear that affirming experiences help to build confidence and trust when applying analytics as a tool for decision-making.
It is therefore essential that top level executives equip themselves with the skills to use analytics tools, interpret the outputs, and make real-time decisions in order to accomplish business objectives and remain competitive.
Dr Nazim Taskin, Professor David Pauleen and Dr Ali Intezari are researchers with Massey University’s School of Management and Associate Professor Shane Scahill is a researcher with Auckland University’s Faculty of Medical and Health Sciences. All are members of the Management Analytics and Decision Making Research Group at Massey University.
Big data, broken down
In simple terms, big data refers to huge amounts of data collected from various sources and analysed in real or near-real time. The data can be collected through wearable devices, social media, sensors, and other mobile devices. The data can be structured, which means it can be stored in traditional databases, or unstructured in the form of videos, images, text and digital files.
Analytics is a broad term that refers to all types of data analysis. Big data and analytics are two terms that have been used together more recently. Together they usually refer to using advanced analytics on large amounts of data collected from different sources and in different formats and structure to gain insights.
A typical example of big data is healthcare data collected from different devices such as electronic medical records, wearable devices and even mobile phones; data from laboratories, clinics and hospitals; and data on individuals from insurance companies. This kind of data can be used for anything from assisting doctors in clinical decision-making to making Ministry of Health decisions about regional funding.
Another example from the field of marketing is collecting multi-national consumer data and generating multi-source marketing data through the integration of population census information, product types, and regional industrial profiles. Insights from such data analytics can result in more targeted international, regional or even local marketing campaigns.
The effective use of big data requires data scientists and analysts who can not only collect data from different sources, but also extract meaning from the data through sophisticated analysis. This involves combining and visualising analysed data in ways that add value to the business and presenting it in ways that managers can understand and use.
By Dr Nazim Taskin