Given the enormous amount of data enterprises collect and store today, it’s wildly unrealistic to expect employees to be able to extract all — or even most — relevant insights available from within this mass of data. Although self-service analytics do allow employees to pull ad hoc data insights, this still leaves a wealth of potentially useful insights submerged inside millions or billions of other data points.
The answer to bringing more data insights to the surface to be seen and acted upon by stakeholders? Advanced analytics tools. Here’s a more in-depth exploration on what defines advanced analytics and how companies are using these technologies to power up their data strategies.
Advanced Analytics: Beyond Traditional Business Intelligence Reporting
Legacy business intelligence has traditionally relied on the production of BI reports based on whatever metrics had been pre-defined as useful to decision-makers. Although these reports could be useful, limitations — like long wait times for delivery and relatively static formatting — did leave something to be desired in terms of how much value teams could get.
Self-service search analytics have since addressed many of these issues by providing users with a direct interface for asking questions and creating charts. However, by nature, search analytics must begin with a user’s search query. This is an important solution for answering questions, but not for uncovering other insights users have not yet had the thought or time to ask.
Advanced analytics goes one step further, shedding light on formerly hidden insights without requiring users to first manually ask for them.
Artificial Intelligence and Machine Learning in Advanced Analytics
As Gartner explains, a major characteristic of advanced analytics is that this tech is “autonomous or semi-autonomous.”
For example, AI-driven data mining algorithms are capable of diving down deep into data and identifying potentially meaningful patterns: trends, outliers, relationships, etc. This tech can mine data at a much faster pace than human analysts can manually, meaning it is able to uncover more potentially beneficial insights, faster, with less effort required to search for needles in the haystack. Of course, data analysts can then focus their skills on other beneficial projects beyond having to continually dig for insights within the company’s increasing depositories of data.
Another autonomous component of advanced analytics today is machine learning, which uses algorithms to learn from data and improve the insights detected and pushed to human users over time. Rather than requiring data scientists to continually train these algorithms by refining the rules, machine learning means analytics systems can get better on their own over time based on experience. Business users can also offer feedback as a straightforward way to help these algorithms better understand what is relevant and what is not so it can become better at delivering highly relevant insights that fall in line with business objectives by job role.
The ability to not only describe what is happening with business performance but also predict what might happen in the future — i.e. predictive analytics — is another facet of advanced analytics technology today. This empowers teams to understand what might be coming down the pipeline sooner so they can make proactive decisions rather than having to rely on data reflecting past performance alone.
Perhaps the most useful definition of advanced analytics is just to think of it as an overarching term for a collection of cutting-edge techniques like artificial intelligence, machine learning, predictive analysis and more. The key is that these tools go beyond what was previously possible in the world of BI and analytics — acting autonomously rather than manually to produce those insights most closely aligned with the enterprise’s performance goals.