These days, even small businesses are getting on board with the idea of using business intelligence (BI) to give their sales and operations a boost. Gone are the days when the analytical and technical barriers to entry made BI a lofty dream for all but the largest of IT departments. Especially with the advent of cloud computing, companies of all sizes and across industries are finding it easier to get the answers they seek and get ahead of customer needs.
BI is the work of analysing the organization’s data to identify and drive actionable change for the business. It can apply to all disciplines and can impact areas ranging from revenue to operational efficiency. Mining Google Analytics for consumer behaviour data is an example of how the Marketing team can use BI to their advantage.
Analytics comes in different forms
Not all slide decks are created equal. The analytics inside them can come in different flavors from just describing historical events to forecasting future trends and suggesting action.
The International Institute for Analytics buckets analytics into three types: Descriptive analytics collects data and reports on what’s happened in the past or where things stand today. Predictive analytics takes that descriptive data and applies it to the future. Then there’s prescriptive analytics, where you build models off historical data in order to champion optimal courses of action. As the last form of analytics is often the most complicated to conduct properly, it can require a collaborative effort across individuals and teams for proper planning and execution.
Living in the age of Analytics 3.0
Traditionally, BI was most often applied to business operations in order to make better internal decisions. But the analytical model is seeping into the very products and services companies provide.
According to the Harvard Business Review, “If your company makes things, moves things, consumes things, or works with customers, you have increasing amounts of data on those activities. Every device, shipment, and consumer leaves a trail.”
By this definition, Analytics 3.0 has the potential to impact businesses both large and small as well as across industries.
Just to recap, here’s HBR’s timeline of the history of business intelligence:
Analytics 1.0: 1950s - early 2000s
Analyses were mostly descriptive in these days. Transactional data on sales calls, leads, and purchases were all recorded and compiled in order to give managers and directors some numbers on which to base decision-making. But the process of simply extracting, cleaning, and manipulating the data was often time-consuming. So much so that end users could really only ask a few questions at a time.
Analytics 2.0: mid-2000s
The rise of the big tech firms in Silicon Valley saw the advent of big data and a need to move out of relational databases. Enormous datasets were being mined for information and they weren’t just coming from inside the company. Businesses started combining their own data with external information as well. Government census data and search query data were all fair game to come up with even more complicated models and insights.
Around this time, we saw a rise in data scientists joining the workforce not just to help craft analyses but to impact and change the business as a whole.
Analytics 3.0: today
The next big era is one that takes prescriptive analytics and applies it to the business’s external operations. An example of this is when a delivery company uses data from digital maps and telematics devices installed in delivery trucks to change the routing of the trucks.
But a delivery company is often a large company. The same principles of BI could be applied to the local boutique store interested in getting the answer to questions like the company’s market share or how many sales staff they’ll need to reach the upcoming target. Companies of all sizes can benefit from applying this kind of analytical rigour to their questions.
Big data isn’t just for big business. Businesses of all sizes and across industries are warming up to the idea of what BI can bring to the meeting room. Some of the guesswork is taken out of the decision-making process and the staff gets more time to focus their attention on the bigger picture.