Article Snapshot

Just as an intelligence quotient or IQ measures a person’s ability to reason and solve problems, a business intelligence quotient (BIQ) assesses how well a business analyses and draws insights from available data to inform decision making, drive strategies and improve performance. Most businesses already collect plenty of data – often so much that they only ever tap into a small percentage – and we’re continually told how business intelligence can help teams and strategies to become more data-driven. But massive pools of data don’t automatically translate into effective and actionable strategies any more than a massive tub of assorted Lego bricks automatically transforms itself into a model house. Someone has to decide what to “build”, which “bricks” are needed and how to connect them together. So, transforming data into business intelligence still requires a human element. Regardless of the tools a business might use to crunch numbers into analytics, someone still has to know which questions can be answered by the available data (and the value of those answers to the wider business). They also need to interpret the findings without skewing the results with inaccuracy, incompleteness, personal bias or assumption. Then there is the further challenge of how to unambiguously communicate the results to the various stakeholders and decision makers throughout the business – most of whom may struggle to interpret the significance of the various charts, graphs and numbers without applying their own inaccuracies, biases or assumptions. So a good BIQ also requires the ability for a business to interpret or translate the data in a way everyone can understand so as to avoid conflicting opinions about what the results actually mean. It is this human factor of BIQ where many businesses – and their employees – currently struggle.

How can business intelligence help?

Most businesses already understand how business intelligence can help not only inform decisions but also reveal new opportunities that may otherwise remain lost in vast data pools. Customer data can help to profile typical customers (and their journey) with increasingly sophisticated detail... Web data allows businesses to better understand and optimise the performance of websites, apps and other digital properties ... Sales data can drive product, feature and pricing improvements... and so on. However, wanting to become more intelligent doesn’t make it so. A person’s IQ isn’t necessarily related to how many facts they can retain – knowledge – but also how they process and apply information to solve problems or inform decisions; what we might refer to as wisdom. Similarly, a business’ BIQ doesn’t necessarily improve by capturing and crunching more data, no matter how many web analytics tools or sales dashboards it may have. All of these tools and datasets require analysis, investigation, interpretation and reasoning – data science – to understand what they are telling us – a level of data literacy most employees, even most managers, just don’t have. While businesses are eager to become more data-driven and scientific about their decision-making and strategic thinking, that doesn’t mean everyone who finds data creeping into their job description automatically becomes a data scientist. And this assumption that almost anyone can and should work with data can place serious limitations on a business’ intelligence quotient.

The data literacy challenge

Data science and analytics require a level of data literacy that most people just don’t have. US company Qlik recently commissioned “The APAC Data Literacy Survey” and found that only one in five Australian employees (20 per cent) believe their data literacy skills are strong enough to meet the growing demands to use data in the workplace. Meanwhile 65 per cent of Australians say that they now have to deal with a higher volume of data within their job than they did three years ago. More than a third reported feeling overwhelmed when working with data and 41 per cent admitted frequently resorting to “gut feel” over informed insight. Clearly, a good Business Intelligence Quotient relies on not just the quality and depth of data but also the data literacy and analytical skills of the people entrusted with turning it into business outcomes. Businesses need to find more creative and flexible ways to supplement the various teams with the necessary business intelligence or data science skills when required.

Business Intelligence Analysts

The mistake a lot of businesses make is viewing BIQ as an extension of the IT department’s data management role. After all, they’re responsible for the technologies through which the data is captured, stored and accessed, right? Yet the IT department may be no better equipped to interpret the data or design effective dashboards than anyone else. This would be like confusing the role of the builder with the architect – the skill sets may overlap but differ in fundamental ways. That’s why many businesses are turning to a Business Intelligence Analyst capable of understanding how the available data within a business can be mined more effectively to solve problems and designing analytical and reporting frameworks to turn the raw data into meaningful insights. A Business Intelligence Analyst can also assess the quality of existing data and help a business develop greater data hygiene around how data is captured and managed. More importantly, a BI Analyst might also identify where there may be holes in the data – key data points not currently captured that, combined with other data, could potentially provide a clearer picture of what is really going on.' The very nature of the BI Analyst role means they need to work flexibly across the entire organisation, unbounded by teams, silos or business functions. It’s also important that the BI Analyst doesn’t simply fall into a BAU pattern, as that also has the potential to restrict their scope to working within routine parameters and established data sets – what is already known. Instead, the BI Analyst should constantly be searching for previously unidentified trends, anomalies – discovering what isn’t already known – that might then prompt further questions leading to new opportunities.

Key Takeaways

  • The majority of Australian employees lack data literacy skills, negatively impacting a Business’ Intelligence Quotient (BIQ).
  • A Business Intelligence Analyst has the specific skill set to help a business extract maximum value from its data.
  • With the right skills, a business can rewire itself to “think” and reason more effectively.
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