An analysis of the use of market intelligence data by senior business leaders – the development of a new model (ICSAR) for the identification and implementation of specifically focused data

Abstract

Big data, analytics and data science are terms that have come to represent a growing focus on decision making built on the foundation of market intelligence data. The enthusiasm for this form of evidence-based decision making has grown with the ability for businesses to better track their customers, competitors and market. Strategy firms such as McKinsey and company have also added to the discussion by highlighting the potential for data to improve business efficiency. News headlines such as 'Big data: The next frontier for innovation, competition, and productivity' (McKinsey and Company, 2011) and 'Data Scientist: The Sexiest Job of the 21st Century' (Harvard Business Review, 2012) are two examples illustrating the optimism for data use in business activities. The ability to better track customer and markets has resulted from the development of technology and the transition to more digital services. For example, a growing number of businesses offer their services and products based on a subscription model through the internet. Software-as-a-Service is one example of this. With many products now available in the digital space, there has been a corresponding increase in the volume and variety of data sources available to business leaders. For example, software services hosted in the digital space mean enhanced customer behaviour insights because digital forms and ‘clicks’ can be monitored and analysed. Marketing departments now have an enhanced ability to conduct rapid testing of video marketing content through social media that is faster and cheaper than testing two different television commercials. The move to more digital and mobile-based services is a phenomenon that has occurred in all industries and has given business leaders access to more data sources than ever before. In theory, this should support better decision making because the amount of information has grown rapidly. However, academic studies have shown that overwhelming levels of information resulted in poorer decision making ability. Industry analysts have also extensively commented that the large variety of data sources have made it more difficult to know which data sources to use when making decisions. These points raised questions about how business leaders were selecting from the growing variety of data sources and what factors influenced that selection process. From there, the question was raised about how data was being used in decision making. Answering these questions holds significant potential for businesses. Understanding limitations to data use and applying this knowledge in a structured way has the potential to ensure data is used objectively and holistically in decision making. The result is that businesses are better able to take advantage of market intelligence and extract the greatest value from its organizational knowledge. This research studied what data sources were used by business leaders, how the data was used in their day-to-day projects and what factors led to the selection of a data source over another in the decision making process. The research was an exploratory approach using a mixed methodology that included in-depth interviews, a survey and a case study. The research deliberately focused on senior business leaders to ensure the research participants were at the level that was most likely to be in a position to make decisions. The research found that there was a varied approach to data use with multiple factors being involved in how data was used. The first finding was that most business leaders used a variety of data sources. However, data sources were selected based on a hierarchy that was specific to each individual business leader and data sources were not used consistently. The hierarchy was subjective and was based on several factors shown in the second finding. There was not a standardised approach to the use of any single data source meaning a data source like surveys could be used for behavioural tracking by one business leader and for logo feedback by another, for example. This highlighted the need for organisations to educate business leaders on the best data source for answering different business questions and to put structure around how data sources were used. Second, the research showed there were four types of influence involved in selection of data sources. Those four influence types were organisational demographics, personal experience with a data source, time-based needs and project requirements. These four factors led to the subjective selection of data by business leaders. For example, a business leader was more likely to use a familiar data source even if there was a non-familiar data source that would have been more accurate. Additionally, business leaders were found to forgo accuracy in favour of a data source that was available more quickly. This highlighted the need for a framework that minimised the subjectivity involved in choosing a data source and encouraged objective data use. The third finding was that there was mix of data maturity levels and that most organisations did not have an integrated approach to data use. The possible cause of this was that many organisations lacked data leadership to ensure that data use in decision making was structured and holistic across the business. Instead, this study found silos between teams that resulted in duplicated or contradictory use of data and individual data sources used inconsistently. This highlighted the gap between the potential of market intelligence and the lack of organizational structures to support effective data use. It also showed the need for organisations to invest in data use structures and frameworks to complement data collection investments. These findings showed that businesses seeking to capitalise on the growing number of data sources needed to examine whether business leaders were using data effectively. The finding that there was a degree of subjectivity in the selection of a data source suggests businesses needed to invest in a decision making framework that ensured a data source was used objectively and based on its ability to meet the project needs. This led to the final section of this research which was the development of the ICSAR model for data use. The ICSAR model was designed by the research author as a five step framework that provides business leaders with a structured approach to selecting and using data objectively in decision making. The model was created based on the research findings in order to support business leaders to enhance their data use and to avoid the subjective influences. The design also promotes objective data use by tying research insights to organisational learning and is cyclical to ensure insights are continually developed