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Some tips before we move on

When I interviewed Jonathan and Elizabeth we discussed tips and techniques for increasing the likelihood of success on data projects. I asked them, “If you could travel back in time …

First level of maturity – the data

We can now expand the maturity model by adding in a new first stage, simply called data. It is the stage that comes before reporting and highlights the need for …

Monetisation

Seduced by stories of the success of Facebook, Amazon, Google etc some organisations gravitate towards monetisation of data thinking that they have an untapped pool of money in their business, …

Further level of maturity – innovating

I would like to introduce a further level of data analytics maturity based upon the Aggreko study, Innovating, using data to innovate within your industry. Data Analytic Maturity Model: Innovation …

Aggreko case study

Aggreko are suppliers of power and temperature solutions globally. I spoke to Elizabeth Hollinger, Head of Analytics and BI at Aggreko, and asked her if he could give us an …

Data analytics maturity

Let’s start to introduce the concept of data analytics maturity by looking at how it relates to the NHS case study. The diagram below helps you to identify your level …

NHS Case Study

I asked Jonathan Cameron, Head of Service (Strategic Development) at National Services Scotland NSS (the shared services arm of the NHS in Scotland) if he could give us an overview …

The 3 areas of value

Here’s a quick reminder of the 3 areas of data value we introduced last week. Data Values (Click to expand) Make sure you have your Data Value Canvas handy so …

The barriers to adding value

Given that the main goal of this course is to “give you the confidence to start adding value through better use of data”, I am keen to understand what is …

Exploring investment priority

As we saw in the video: Mostly “improving decision making” is indicative of the early stages of exploring data value and a good place to start as it helps you …

Focus on outcomes

One of the most common mistakes we come across is the use of data just for the sake of it. For example, because you have been told to “go do …

Be open to learning

Hopefully this starts to highlight that there is a big difference between being clever about data and being able to use it to drive value. Your definitions of value I …