What is Advanced Analytics and why are businesses investing in it?

Advanced analytics is rapidly increasing in popularity as business leaders look to leverage the advantages that deep insight and more robust decision-making can bring

Any organisation can gather data about customers, their market, the performance of their team, products or services, even their competitors – there is an unlimited number of datasets with the potential to provide insight when effectively analysed.

But having the data alone is of little to no use without the analysis that follows, and with recent significant technology-enabled developments in areas such as visualisation, machine learning, sentiment analysis and pattern matching (among others), analytics as a discipline is radically different, and much more powerful than a generation ago.

Today, advanced analytics enables organisations to add value and certainty to their decision making. It meets the widespread need that – whatever the situation – minimising risk and narrowing the probability that future events or scenarios will or won’t occur is hugely valuable.

It brings together processes and tools that can take the data that organisations – and the developer community in particular – enthusiastically collect, even in daunting volume, to draw out patterns, trends, anomalies and insight to inform decision making with more precision and value than ever before.

Advanced Analytics Techniques & Processes

Advanced analytics applies a range of techniques and processes to turn data into insight. Big data analytics, for example, takes large amounts of structure or unstructured data and examines it to highlight the most useful areas for further focus or analysis.

Data mining is the process of extracting useful data from raw data, primarily through the identification of patterns and relationships. Once the useful data is extracted, it can be analysed further to understand the importance, impact and relevance of the detail uncovered during the data mining process.

That’s where predictive analysis comes in, whereby businesses can apply machine learning algorithms to current and past data to build a prediction model that can help improve business outcomes. Typically, the more data that’s available for analysis, the more accurate the prediction. Even though predictive analysis won’t reveal exactly what will happen in the future, it will help reveal the range and certainty of possibilities given the decisions made.

Taking this a logical step further, applying prescriptive analysis is a process that can inform the most effective way to implement a decision. In many situations, implementing a choice can involve a range of options. Prescriptive analysis draws on current and past data, and helps assess different possible outcomes and the best course for implementing a business decision.

Real-World Implementation

From commercial organisations looking for competitive advantage to government organisations working to save lives, advanced analytics forms a solid foundation for better decision making, investment decisions and good governance, among a huge range of other possibilities.

One everyday example is the way advanced analytics enables a whole range of capabilities offered by online retailers, entertainment services and media outlets, among many others. Websites and apps that recommend relevant items to buy, shows to watch or content to read are often employing advanced analytics to track and measure user behaviour, predict other areas of interest and make relevant recommendations.

Many people find these suggestions extremely useful, and for brands and service providers, they can drive additional revenue and engagement. In an era when user experience is a top priority, advanced analytics is delivering a win-win for organisations and their customers.

From Data to Insight to Decision

Because advanced analytics is not limited to a narrow area of implementation, it’s being applied across a multitude of circumstances and to drive a wide range of benefits. Top of the list for many organisations is a desire to make smarter, more certain business decisions, helping people understand areas for improvement while eliminating negligible factors that won’t influence success.

And then there’s the widespread emphasis on managing and minimising risk. Businesses use advanced analytics to understand the trends and variables that determine the level of risk relating the datasets in their possession. This can be used to design processes, policies or business models that can deliver acceptable outcomes, improve governance or help ensure organisations meet their compliance obligations.

Enter DevOps

DevOps processes can greatly benefit from advanced analytics. Developers can better understand the vast amounts of data being produced throughout a software delivery pipeline, and make smarter decisions that can improve the quality of the code. When issues arise, the metrics in the analytics process can help to identify the root cause and suggest where corrections need to be made.

The terms observability and monitoring are often used interchangeably, but the real difference is: something must be observable to be monitored. By making data observable and able to be monitored, organisations can then utilise advanced analytics to reap all of the benefits mentioned above.

Advanced analytics as a whole is rapidly increasing in popularity across the economy as more business leaders look to benefit from the advantages that deep insight and more robust decision-making can bring. Organisations now understand that the data they collect can be a competitive and efficient asset, but the challenge they’ve always had – that of applying effective, meaningful analysis – is now becoming a strategic imperative that they can achieve.