Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI) or traditional analytics, to discover deeper insights, make predictions, or generate recommendations. Advanced analytics has become more common during the era of big data mainly because it helps process and understands big data better. Predictive analytics models and, in particular, machine learning models usually require large amounts of training to identify patterns and correlations before they can make a prediction. The growing amount of data managed by enterprises today opens the door to these advanced analytics techniques.
The Uses of Advanced Analytics
Advanced data analytics is being used across industries to predict future events. Marketing teams use it to predict the likelihood that certain web users will click on a link. And in the healthcare industry, providers use Healthcare Advanced Analytics and prescriptive analytics to identify patients who might benefit from a specific treatment; and cellular network providers use diagnostic analytics to predict potential network failures, enabling them to do preventative maintenance. Advanced analytics practices are becoming more widespread as enterprises continue to create new data at a rapid rate. Now that many organizations have access to large stores of data, or big data, they can apply predictive analytics techniques to understand their operations at a deeper level.
The Increased use of Data
Beyond reorienting the existing business models, analytics leaders are also learning how to create and capitalize on new opportunities. Organizations are moving from hoarding data to sharing it. Some are pooling data as part of industry consortia, increasing their comprehensiveness and therefore their value. Product-based organizations are adding data and analytics to their offerings as value-added services. Some have gone further, charging for the analytics-enabled service rather than directly selling the product. For example, some jet-engine manufacturers now sell flight hours instead of the engines; this is only possible because sensors provide the data that help them understand usage and required maintenance.
Analytics create value when big data and advanced algorithms are applied to business problems to yield a solution that is measurably better than before. By identifying, sizing, prioritizing, and phasing all applicable use cases, businesses can create an analytics strategy that generates value. For example, a CEO of a global consumer packaged goods company told us that the application of advanced analytics and machine learning to business functions such as revenue-growth management and supply-chain optimization.
Few executives, however, have such a detailed view of value across their business units and functions. Business executives need to have more clarity on what business value they are trying to create by using analytics. Most business executives have experimented with a handful of use cases, but lack a comprehensive view. Even fewer have considered how analytics can create new sources of revenue. Lacking an enterprise-wide view of opportunity, business leaders struggle to make a considered business case for analytics. They may also struggle to communicate why analytics matter and that is essential to get the organization committed to change.
The sea of data is vast and growing exponentially. To avoid drowning, executives must connect the data strategy to the analytics strategy. When exploring new data sources, it helps to have specific use cases in mind and to reflect on how data are acquired—whether through commercial vendors or via open sources. Know what data the business owns; this can become an asset to monetize. To continuously improve data quality, put in place governance and processes, and ensure that the rightful owners have direct access. Mandate good data and metadata practices and build automatic data-reconciliation processes that constantly verify that new data meet quality standards. To drive new insight, interconnect different data sets, potentially in a centralized repository (or “data lake”). Resist the temptation of complexity. Rather than building a data lake for all legacy data—a project that can take years—fill the lake gradually. Start with data required for priority use cases, and gradually add to it. Get started with what you have, and don’t let perfection be the enemy of the good.