When we pause to think about the technologies that are having transformative effects on business, initial thoughts often turn to the likes of automation, faster payments, Cloud solutions and even more emergent solutions, such as AI. While it’s true that these technologies can be catalysts for commercial growth and evolution, there is another which is seeing widespread uptake and changing the way organisations operate; business analytics.
What are business analytics?
For starters, it’s worthwhile pointing out that the concept of business analytics is not a new one. Automotive giant, Henry Ford, famously took the time to measure how long each component of his cars would spend on assembly lines in order to sharpen decision making for future projects. It was, however, a laborious and very much manual process. It was only when computing became ubiquitous across the commercial landscape that business analytics took on a new dimension and duly exploded in popularity.
Today, business analytics mainly involves the aggregation and interpretation of large volumes of mission-critical data which produces trends and patterns from which more informed decisions can be made. Mainly, but not wholly. Advanced analytics tools can influence not just decision making, but the outcome of customer interactions, so when a specific customer type is considering a purchase, the technology can fire back information which allows the business to modify the sales pitch.
Generally though, there are three key forms of business analytics:
- Descriptive:Interpretation of historical data to identify trends and patterns
- Predictive:Use of statistics to forecast future outcomes
- Prescriptive:Application of testing to determine which outcome will generate the optimal result in a given scenario
Which form a business chooses to deploy, is dependent on what they’re trying to achieve.
The power of analytics
To understand how analytics have become such a relied upon tool within the contemporary business’ solutions inventory, we need to explore the benefits it is proving to deliver.
Bigger revenues – Ultimately, this is what any business wants from an investment into a new technology. The evidence that those companies which are embracing data and analytics initiatives see significant financial returns is compelling.
Research undertaken in 2016 by American management consultancy, McKinsey & Company, revealed that businesses which invested in big data achieved an average 6% increase in profits, a figure which jumps to 9% on investments tracked over five years.
Validating these results, a recent study by the Business Application Research Centre (BARC) found that businesses which measured their returns following deep data analysis, reported an average 8% increase in revenues and a 10% reduction in costs.
Both these studies demonstrate a notable financial return that can emerge from an intelligent business analysis strategy. And as the big data and analytics market continues to grow, they are returns that are becoming more accessible to more businesses.
Enhanced operational efficiencies – As well as attention-grabbing financial gains, analytics can also be used to sharpen business operations.
Multinational professional services network, KPMG, recently reported on emerging trends in infrastructure, having discovered that many firms are now using predictive analytics to anticipate maintenance and operational issues before they become bigger problems.
For example, a mobile network operator surveyed by KPMG described how they use trends from their data, derived from analytics, to anticipate outages a full week before they occur. Armed with this information, they can prevent outages by timing maintenance more effectively thus enabling them to make savings on operational costs and ensure that assets are kept at optimal performance levels.
Better decision-making – Successful business endeavour hinges on the frequency of good strategic decisions. Business analytics provide the crucial data that makes such decision-making more likely.
To provide a working example, in 2018 ride-hailing giants, Uber, upgraded their Customer Obsession Ticket Assistant (COTA). The software utilises machine learning and natural language processing to help Uber agents improve speed and accuracy when responding to support tickets. The company used prescriptive analytics to assess whether the new version of the product would be more effective than its predecessor.
By deploying comparative testing, Uber were able to determine that the updated product led to a faster service, more accurate resolution recommendations, and higher customer satisfaction scores. As a result of these insights, the updated iteration of the software was deployed and not only was Uber’s ticket resolution process duly streamlined but they went on to make savings worth millions of dollars.
Final thought
Such is the proven effectiveness of business analytics tools, that they have become less a reference point and more a foundation upon which critical decisions are made and actions taken. As the technology of these tools grows in sophistication, the more insight and capability they provide to users.
For the contemporary, forward-thinking business, ignoring or even simply under-utilising data reserves can mean a stifling of potential or, in particularly competitive markets, even worse.