In their quest to extract insights from the massive amounts of data now available from internal and external sources, many companies are spending heavily on information technology tools and hiring data scientists. Yet most are struggling to achieve a worthwhile return. That’s because they treat their big data and analytics projects the same way they treat all IT projects, not realising that the two are completely different animals.
A big data or analytics project can’t be treated like a conventional, large IT project, with its defined outcomes, required tasks and detailed plans for carrying them out. The former is likely to be a much smaller, shorter initiative. Commissioned to address a problem or opportunity that someone has sensed, such a project frames questions to which the data might provide answers, develops hypotheses and then iteratively experiments to gain knowledge and understanding. We have identified five guidelines for taking this voyage of discovery.
1. Place people at the heart of the initiative
The logic behind many investments in IT tools and big data initiatives is that giving managers more high-quality information more rapidly will improve their decisions and help them solve problems and gain valuable insights. That is a fallacy. It ignores the fact that managers might discard information no matter how good it is, that they have various biases and that they might not have the cognitive ability to use information effectively.
The reality is that many people – including managers – are uncomfortable working with data. Any information-based initiative must acknowledge that. It must place users – the people who will create meaning from the information – at its heart. It should challenge how they do or do not use data in reaching conclusions and making decisions, urging them to rely on formal analysis instead of gut feel. And it should question their assumptions about customers, suppliers, markets and products.
2. Emphasise information use as the way to unlock value from it
Initiatives designed to extract information from existing systems or new sources of data must acknowledge how messy – and complex – that process is.
People don’t think in a vacuum; they make sense of situations on the basis of their own knowledge, mental models and experiences. They also use information in different ways, depending on the context. An organization’s culture, for instance, can frame how people make decisions, collaborate and share knowledge. Moreover, people use information dynamically and iteratively. The steps of sensing a potential problem or opportunity, deciding what information is needed, and then gathering, organizing and interpreting it occur in cycles.
Analytics projects succeed by challenging and improving the way information is used, questions are answered and decisions are made. Here are some ways to do this:
Ask second-order questions: Instead of setting out to create a system that can help sales professionals easily answer the question, “What stock should we place on shelves today?” an initiative might begin by asking, “Is there a better way to decide how we replenish stock?” By posing second-order questions – that is, questions about questions – the project assumes that decision-makers could improve the way they operate.
Discover what data you do and do not have: Avoid being bounded by easily accessible data and systems, which are based on particular assumptions and logic about how the business should be run. While they may have been correct in the past, those systems most likely have not kept up with a continually evolving business and competitive environment.