In the few minutes it takes you to read this page approximately 166 petabytes of new digital data will be created in the world. 166 petabytes is approximately equivalent to all of the digital data that was created worldwide in 1995. So where does all of this digital data come from? Well, much of it comes from sources that have been in place for decades. The everyday things that all organisations do like banking, sales, transport and HR all produce digital data. But there are also some newer sources. Online user-generated content like social networking posts, emails and blog posts all generate digital data – both in the content itself, and meta-data describing the fact that the content has been produced. On top of this the world is becoming more and more full of digital sensors, all of which are generating digital data at a tremendous rate. This is a phenomenal amount of digital data, and offers a phenomenal opportunity for businesses of all sizes in all industries. Data, however, is only worth thinking about, if we can use it to make better decisions that lead to better performance in our business.
Data analytics is the science of extracting insight from data to allow organisations to make better, data-driven decisions. This is in contrast to the way many organisations currently make decisions based on opinions, and ungrounded assumptions.
When we think about data analytics we should always think about these three elements: data, insights and decisions. No matter the application we are working on, or the industry that we are working in, data analytics always comes down to these three elements.
This diagram nicely illustrates the many different ways that we can use data analytics to help answer questions in support of data-driven decision making. We can use relatively simple descriptive analytics techniques to answer questions about the past. For example, high-street retail chains use drill-down querying to fully understand what types of products are best performing in what regions with what kinds of customers. The insight gained from this allows them to make informed decisions about where to locate stores and what product mix each store should carry.
Advanced analytics techniques are a little more complex but allow us to make predictions into the future. For example, telecommunications companies use propensity modelling to predict which of their customers are likely to leave for another provider in the near future. Armed with this insight these companies can offer targeted incentives to those at-risk customers to convince them to stay and so reduce overall customer churn and increase profits.
Analytics has the potential to transform how your business makes decisions. If would like to discuss the potential of analytics in your organisation please don’t hesitate to contact us at firstname.lastname@example.org