We’re obviously going to be a little biased, but when looking at data analytics versus machine learning we believe our work can unlock secrets in your data that will really help your improve your customer-facing services. It’s also a good time to be involved in data analytics. Computing power has increased rapidly and tools to analyse data are more available via the cloud. Plus, of course, there is a growing interest within organisations to be more data-led. Finally, cost – which was a major barrier – is less of a worry as technology becomes more widespread.
There is sometimes however a little bit of confusion over some terminology. For example, what is the difference between data analytics and machine learning? So, as part of our ongoing series to help improve knowledge of this discipline, we’ve put some notes below to help you understand the world of data a little better. Let’s take a look at data analytics and machine learning in a simple summary.
A typical organisation produces huge swathes of data, much of it incidentally as part of daily operations. The information can be relevant to core activities or tangential to them, or seemingly almost irrelevant. Some of it may be duplicate data from other areas. And all this data exists in various locations, from in-house servers to desktops, laptops, phones, the cloud, notepads, desk drawers and filing cabinets. It’s everywhere – and usually unstructured too.
To try and get some value from all this data various tools can be used to aggregate the information in ways to bring some analysis of it. The end result could be a spreadsheet or on-screen dashboard reporting on performance or outcomes. The point here is data analytics looks at historical data to provide information. The answers being shown are to questions asking about the past, e.g., how many patients were treated last week, or how many times a patient was visited to change dressings. That historical data may contain some patterns or relationships with other information. And this could prompt questions about tasks or operations.
As the data is about the past, it means basing any decisions on this information are essentially guesses. Which brings us nicely to predictive analysis, where projections are created. This can help to test assumptions to refine ideas, but it has a drawback in being restricted on assuming past patterns remain the same. This automatically limits the range of what answers the data could provide to a business question. To a degree predictive analysis is still very useful of course, but patterns in data can be impacted. To try and accommodate variations requires human input and detailed, time-consuming work.
Which is where we arrive at…
This takes predictive analysis and learns through a process of making assumptions and testing them without human involvement. This ability to test for varying outcomes quickly speeds up accurate decision making, making it cost-effective to use
Machine learning is able to test and re-test data to predict every possible customer-product match, at a speed and capability no human could attain. It can handle both large data volumes and sources, dealing with complex analysis effortlessly for extremely detailed and accurate projections. And this in turn allows an organisation to evolve operational practices and customer focused services with more confidence the changes will be successful.