Business intelligence: where is it heading towards?
Until just a few years ago, 'business intelligence' was something reserved for large, capital-rich companies. Today, it’s a technology that has become ingrained everywhere. Other trends include fast visual reporting and the advent of external partners.
Businesses are gathering data like never before. Previously, the information collated was kept neatly stored in separate systems. Finance, sales and HR each had their own data and the idea of exchanging that information was out of the question. Today, we are seeing systems beginning to talk to each other more and more. Which is creating a huge mountain of data – ready to be mined and extracted for valuable insights.
Until recently, a data warehouse was used to do this. Building up a relational database was a lengthy business. Some of these processes could take over a year to complete. These days, we are seeing a move towards greater flexibility. You are no longer tied to the rigid step-by-step plan of a data warehouse. As a result, you are able to load data more quickly and implement changes more flexibly.
"The need for real-time reporting has grown."
Just to be totally clear, the concept of a data warehouse remains intact. The aim is still for us to link data sources to each other. Only today, we can use API (Application Programming Interface) plug-ins to do this much more flexibly than before. An API is a link to another program that facilitates the exchange of data. Designing a data warehouse has also become much quicker these days through the use of automated processes capable of identifying, structuring and saving the data.
With the demand for real-time information, this is a great advantage. The faster new sources of information can be linked and unlocked, the more current your total overview of data becomes and the better you are able to take decision based on facts. Reports are based not only on known data, but they also take predictions increasingly into account. For this, take a look at the blog and wiki page dealing with predictive analytics.
And finally, reports also look a lot more attractive these days. Companies focus on creative and original ways of presenting information. Below are just a few examples of what Nodebox makes possible with visualisations:
© Jan Aulbach © Jonas Lekevicius, Juste Ziliute, Augustinas Paukste
Another trend is that companies are entrusting their analytical requirements more and more to external partners by using SaaS solutions and external analysts. More software providers are also appearing, such as Infer and Fliptop, offering standard analyses as part of their products. These two software providers offer the technique of predictive lead scoring with prospects, enabling you to enjoy a greater chance of success. Your salespeople will then know which potential customers to focus on, while at the same time there is no need for you to do your own predictive lead scoring.
Also new is that in their search for relevant data, companies are using location-based services. These applications on mobile devices enable you can locate the user. So, for example, some retail stores actually track the route taken by customers as they shop. The Dutch app, Flitsmeister, which warns drivers about speed traps, also displays advertising messages to users about stores located on the route they are travelling.
"Businesses are making more and more use of location-based services."
Define your target
Finally, there’s this: some critics are sceptical about ‘data mining’, which involves looking for links in the gathering of data. Critics are afraid that instead of looking for a needle in a haystack, you simply create more hay. Well, they are wrong. But first of all you need to define your objectives clearly. Which means you need to outline where you want to go with the data. The possibilities are endless. For example, you can go looking for customers to generate more turnover. Or you can seek out information that will enable you to improve your products or services. Which in term will help you generate more turnover. From ‘big data’ to ‘smart data’, in fact.