Ars Electronica

Creating Business Impact With Data

One of our central beliefs at Praxidia is that you can’t transform or manage a business without data. Being able to capture and analyze data effectively creates insights into what your business is doing right – and wrong. If you have the best analytic software in the world, it means nothing if the data you feed into the system is of poor quality or no longer relevant.

This can be a complex problem. The Internet of Things (IoT) is creating an environment where more objects, systems, and processes are recording more data than ever before. It’s easy to be overwhelmed by a deluge of data that cannot be transformed into information and insight. How do you plot a route from raw data to business insight and intelligence?

A recent McKinsey report titled ‘Achieving business impact with data’ explores this issue in depth and I intend to focus my next couple of articles here on their approach to data. In short I believe there are three main areas that need to be understood:

  • Internet of Things; what data is being captured? What is possible to capture? Are you capturing information that can be used to shape insight?
  • Capturing value; how does your business define value? What insights do you want to find from the data you have? Can you see, or define, a path from the raw data to business intelligence?
  • Insights Value Chain; defining the complete set of requirements that lead from the capture of the data to the creation of intelligence. What physical attributes, properties, systems, or skills are needed for you to make this work in your business?

The insights value chain is potentially the most important area for any executive to understand because it combines the need to understand your technology systems and their capabilities along with the foundations inside your business. You need to consider the data, analytic systems, IT systems, along with your people and processes and how they will all interact as data is collected and analyzed.

Within each of these areas in the insights value chain is a multi-layered series of properties. For example, when considering just the data itself you need to consider existing data sources, new data sources, orchestrating the data together, managing unstructured data, privacy and security and any other legal issues regarding the data itself.

This list of properties and issues will be repeated for all of the sections that exist within the value chain. For instance, a list of considerations is required for the process of analyzing the data, how the IT systems should function, and how the people and processes will function. Ideally you will create a grid of component considerations – forming the entire insights value chain.

The bad news is that you may have mapped your value chain and determined that there are 25-30 different discrete components to be managed, but your entire data strategy is only going to be as good as the weakest component in your value chain. In short, you can invest in the best IT systems and analytic processes, but if you don’t similarly invest in the people doing the analysis then the software can’t help you much.

In my next article I will map out a typical insights value chain and explore how to decide which components you need to map out a vision of how your own organization can use data more effectively.

If you have any questions about data analysis or how you might start improving the customer experience through a more effective approach to data analytics please leave a comment here or get in touch directly via my profile.

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