Applications have come a long way. They were originally the purview of only critical activities, such as finance (managing the money) and manufacturing (building the product that brings in the money), and focused on improving efficiencies – making fewer errors, eliminating waste, and automating tasks. Today, nearly every activity in a typical workday involves working with applications; most of which are now deemed critical to the organisation. Over the past 30 years, we have come to understand these problems reasonably well and solutions are now abundant.
Or are we just beginning? Just as it began to appear that the main business problems had been understood, we saw the introduction of big data – including higher volumes of data; a wider variety of data types (e.g. unstructured data, log files, and video); and data coming in real-time – which presents new opportunities for increased efficiencies. But before we look at big data with our ‘efficiency glasses’ on, let’s take a new perspective on the use of that data.
While more marginal improvements to efficiencies will undoubtedly be squeaked out, the real opportunity for achieving large business improvements today is not only about improving efficiencies, but also about doing things better by making smarter decisions. It might have seemed you were doing that already, but the real potential is still waiting to be realised. For all the data, applications, and analytical tools that treasury departments already have, their teams are lucky to be getting even 20% of what is possible. This is because their analytics are disconnected from the business processes that need them and, more importantly, the people who can make smarter decisions in those processes.
Research suggests that adults make 35,000 individual decisions a day. While many of those can be mundane – such as what to eat – a good portion are work-related. Let’s assume half of those decisions are work-related (an eight-hour working day divided by 16 hours of wake time) and multiply that by the company’s number of employees and you can now understand the magnitude of the opportunity. All those micro-decisions will add up to a tangible competitive advantage.
Empower employees to make smarter decisions
To help employees make smarter decisions, place analytics in the context of where the decisions occur. Decisions happen in the applications that people use every day. Embed analytics in the fabric of those applications and the smarter decisions will start immediately. Add in the extra information that big data promises, and those analytics and applications can power smart decisions to deliver larger business impacts than the marginal ones that focus merely on efficiencies. In fact, employees use embedded analytics every day and may not even realise it. It’s when you don’t realise it that you know you have achieved maximum impact.
For example, when is the last time you bought something and did not read reviews? Amazon provides the consumer a shopping experience that embraces nearly unfathomable amount of data in all forms. The first thing the online shopper is likely to look for is the overall average rating, usually expressed on a scale of 1 to 5.
The next thing that is typically looked for is the ratings histogram – even if users don’t recognise the word. The ratings histogram is the horizon bar chart that shows the frequency of each rating choice. The shape of the histogram is crucial. If there are mostly 4’s and 5’s, then it’s probably good. If there are too many 1’s and 2’s, it is more likely bad. If there are lots of 3’s, it probably is only ‘OK’. At the same time, if there are only 5’s, users will assume it is probably false (rigged ratings) or, more likely, a not statistically significant sample.
The third thing typically looked for is the number of reviews or the sample size. Too few and the ratings can’t be trusted. It’s a law of large numbers that, without necessarily having taken a stats class, you inherently know holds true. Then the user looks for unstructured data, which comes in at least three forms. These include any pictures, and the review that individual consumers type in. This probably includes both positive and negative reviews. They inform the user in ways the numeric ratings cannot. Lastly, the user is likely to view a video of the product in use. In terms of how recent they are, he/she will refer to the reviews just written because it is then possible to read about the latest edition of the product, not the one from six months ago.
The point of this Amazon illustration is that big data and analytics in context of an action – in this case buying an item – is far more powerful than having to carry out that same work in a disconnected application. As a goal to set for business applications, when employees use analytics from big data without knowing it, the application will achieve maximum impact. The name for this is genius apps.
Genius apps focus on helping people make smarter decisions by embedding analytics into the fabric of the application experience. Genius apps understand exactly what problem is to be solved, and they solve it well – in the context of a user’s workflow. When done well, the analytics disappears in the fabric of the application, and users take on the best practices without even realising it. A genius app does not require a genius to use it.
For business applications, a ready choice is to reduce customer churn. A customer service representative at a telecommunications company would have far more profitability impact by retaining customers and keeping them happy by having all the big data analysis in context of their customer relationship management (CRM) application. If the user can see in one place their total financial relationship with the company, contract status, satisfactions scores, open support cases, data usage, call usage, sentiment (from Twitter feeds or Facebook), and tell me what options to keep the customer happy, there is a good chance that customer and his/her business can be retained.
That is a genius app for business. Help employees make smarter decisions in context of where they work every day (applications) and those tens of thousands of smart micro-decisions will add up to success. It is time to start thinking about big data analytics very differently if we want to have maximum business impact.
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