If you look at the biggest companies in the world, they have very different data characteristics. Some are based on massive data processing and analysis capabilities (online search and advertising being most obvious.)
But consider an upmarket hotel chain. Having great data is one thing, but no-one on reception to check you in, a lumpy pillow and an empty mini-bar are business killers, irrespective of having a real-time report, globally mapping every un-made bed.
Many major corporations were built up from a single store. Their founders spent their time on products, people and customers. Their business lives depended on getting it right, fine-tuning and selling the old-fashioned way; then repeating. But that was then, and this is now.
So how can businesses today, with the opportunity to record and analyse every data point in real-time, support but not lose sight of their real mission, namely, to serve, delight and retain the customer in a profitable way?
Management information (MI), business intelligence (BI), enterprise performance management (EPM), insight, analytics, data science, enterprise data management (EDM), big data - call it what you will - these are all names for collecting, storing and analysing information.
Key to this kind of analysis is cross-comparing something with something else. You could cross-compare one internal system against another: did sales change because all my staff went on holiday? Or you could cross-compare one system against external data such as: did all my staff go on holiday because the weather was really great?
Both analyses would be considered backward looking. Looking back can help to understand why things occurred, and hopefully to stop bad things next time (or allow the good things to flourish.) However, with some thought, experience and possibly help from a computer you can forecast the future.
Selling more when it rains (umbrella anyone?) or struggling to get products to stores due to busy roads (bank holiday weekend) should influence how the business is run. Forward-looking analysis can be tricky, the further into the future you go, the harder it can be.
Be careful which variables you link together to make a decision. Fact: the more ice creams sold, the more shark attacks there will be. So to stop shark attacks, simply stop selling ice creams? Of course not; the reason for both ice creams and sharks is probably down to hotter weather.
It’s a brave CEO that lets computers start making data-driven decisions. It might be the norm for high-frequency share trading, but do we really want to let a computer change prices, change the in-store lighting or order 10 tons of beans? There are obvious challenges. We need good data so that decisions are based on accurate, timely and complete information.
Every company has the usual data quality challenges, master data challenges, a lack of training and governance to some degree. The best companies, however, spend time on these challenges and make it part of the culture to do so. Data quality will never be perfect, but going the extra mile to help people to get it right (and ensure there are less opportunities to get it wrong) typically bears fruit.
Greater transparency?
Employees in general are very important, whether your organisation is a heavily data-driven organisation or not. Giving away the company silver by making information too widely available is bad news, but making sure people know what is happening is important.
If they don’t know the results of their efforts, they can’t know if they’re doing it better than before. All businesses should listen to the people on the front line; it’s they who are sometimes the voice of the customer. Some customers want to stay anonymous. Let them.
The price of data-related technology has gradually reduced. Disk space and memory has never been cheaper. So it’s now feasible to load in and analyse pretty much everything, from structured database files and market research to unstructured video clips, social media and hand written contracts.
In the old days you had to take a subset and make assumptions that it was representative of the whole. Those days are gone. Now you can find the niche, make things localised, tailored and specific to an individual customer.
Depending on your point of view, standards and knowledge have gone up, but data loss is more widely reported and your data is in a system connected to the internet (through firewalls if you’re smart, but so are the hackers). The cloud may be safer, faster and cheaper than your own data centre, but do the sums add up?
Think carefully, decisions can be hard to reverse. And how about gut instinct, using a pair of eyes, and just listening? Many mechanisms for running a business are still the same as they’ve always been; give the customer what they want, in a better way than everyone else, and develop your people (profitably).
Customers are becoming more demanding, savvy and price conscious. The best businesses know their customers and what they want and need. The story is relevant for public and third sectors as well; these organisations still have customers, who still expect good service; it’s just paid for in a different way.
The explosion of social media means your customers are telling the world about your business, where before it was just their friends and colleagues. But this kind of interaction can also make it easier to find out what they want. You don’t need a big expensive computer to crunch all this data, but it can make a big difference if done well.
Most online businesses have grown up recording things that traditional firms will take years to catch up with. For some it will be a struggle to capture everything needed to get the right answer. Deciding what to measure rather than what data is available is key - the top-down approach. If data is not available, innovate. Use proxy data or collect and track in a different way. Ensure there is a way to get analysis into the hands of decision-makers, otherwise what’s the point?
In the best companies, business intelligence is part of the culture. In companies that have intelligence, employees know how their piece of the jigsaw is doing, what the customer likes and doesn’t like, why things are what they are and what they can do to improve things. They know quickly when something is going well or badly and they act. Even if it’s a computer that told them and makes the change.
