What are AI Analytics?

Simply put, AI Analytics is the discipline of automating data analytics using artificial intelligence and machine learning.

This approach to analytics takes tasks that are manual and repetitive for human beings, and structures them in a way that machines can understand and solve for computationally, ideally with no human input during the process.

AI Analytics helps significantly reduce the time and effort spent to arrive at a solution for an analytical problem, with just as good (if not better) accuracy.

How does Machine Learning fit into AI Analytics?

Machine learning is a subset of Artificial Intelligence, in which a machine is presented with a large amount, and recognises the patterns pertaining to the question that is being asked.  

Whilst machine learning algorithms employed in AI analytics are powerful, they do not represent artificial general intelligence (AGI) – they have some common prerequisites:

  • Different algorithms are employed to answer different questions – for example, identifying customer sentiment from comments likely requires Natural Language Processing, which is a different discipline to identifying cancer markers in MRI scans using computer vision, and again yet to answering business questions like “Why are sales down today?”
  • These algorithms depend on the data being appropriate structured – even with the proliferation of data warehouses and data lakes, poorly structured data is of little use.
  • There are a number of different classes of machine learning – supervised, unsupervised and reinforcement – each depends on the scenario and how much training data and system response is available.

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Difference between AI analytics and Traditional Analytics

In business environments, traditional analytics approaches place a heavy reliance on human operators – here’s a few examples:

  • Reporting – static views of data that refresh with time, these are built by humans after a consultation process with the people who wish to have the insight.  Once these dashboards are created, they provide a central point of reference for users to understand performance.
  • Humans, however, are inquisitive creatures, and almost always have follow-up questions – it’s also rare for a set of dashboard to cover every single potential question that could be asked.  As such, answering further questions in this format requires new dashboards to be setup by humans.
  • Ad-hoc Investigation – typified by questions like “Why did the performance of Metric X change?” – this type of question is disarmingly simple to ask, but often fiendishly difficult to answer.
  • In traditional approaches, a human analyst would need to collate all necessary data, explore it to look for the likely reasons driving the change, identify the most relevant of those, and present them back to the questioner – a process that takes hours, if not days, and does not scale if multiple questions are asked.

AI Analytics is different – because it has automation in its corner, it’s able to perform significantly more analyses in a given amount of time than a human analyst, and do so without bias – in a business setting these could be:

  • Identifying unusual behaviours in your business’ performance quickly, across thousands of metrics, and alerting you in real time
  • Answering the question of why performance changed in a rapid fashion – so that you can act sooner
  • Predicting ahead performance based on historical data and known future factors

Benefits of an AI analytics approach complementing Traditional Analytics

  • Look, you won’t get away from dashboards.  They’re far too useful
  • But you should keep track on the unknowns – whether they are things you’re aware of already or not
  • You get to answer questions a lot faster, and act on the insights much more rapidly (to do something within the business)
  • Less bias – humans tend to get bored/tired after a certain amount of analysis, whereas machine learning doesn’t.

Use Cases for AI analytics

  • Streaming
  • Gaming
  • Ad Operations
  • Marketing
  • Customer Services
  • eCommerce
  • Retail

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