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.

DID

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.

DID2

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 info@theanalyticsstore.com

Latest News

6 Nations Bonus Points: What does the data say?

February 23rd, 2017|0 Comments

In 2017 bonus points will be awarded for the first time in the 6 Nations rugby tournament. The motivation behind this is to encourage attacking rugby by rewarding teams (winning or losing) that score multiple [...]

Analytics is a Team Sport

January 11th, 2017|0 Comments

I was recently at the Predict Conference in Dublin and one phrase that I heard that really resonated with me is "analytics is a team sport". One of the key focuses of The Analytics Store [...]

12 Days of Data Analytics: Day 12

December 25th, 2016|0 Comments

On the 12th day of Data Analytics ... all that is left to say is Happy Christmas! We're looking forward to lots of analytics adventures in the new year.