In a famous poem called “The Rime of the Ancient Mariner,” a sailor laments there’s (salt) water everywhere, “nor any drop to drink.” This ironic feeling of being adrift on an entire ocean of undrinkable water is a familiar one to enterprises with storehouses of data they’re finding difficult to leverage. There’s so much data available now companies can easily get lost in the weeds when it comes to the analysis and practical usage of those vast information stockpiles.
As it stands, between 60 and 73 percent of enterprise data may go unused for analytics. To reverse this, the right tools, training, culture and strategy must be in place. Here’s more on how companies can avoid becoming overwhelmed by data to create a workable strategy as well as best practices use information to improve their business outcomes.
Reduce Reporting Backlogs & Insight Wait Times
Even the most interesting piece of information becomes essentially useless if it arrives after a decision is already made. Companies dealing with constant reporting backlogs and long wait times for decision-makers get less out of their data stores — especially considering the sheer volume of available data today.
Working to reduce — or, ideally, eliminate — backlogs in reporting goes a long way toward allowing workers to efficiently harness insights to “strike while the iron is hot.” One major contributor to these backlogs is the existence of data silos, storage units separated from one another and from unified, enterprise-wide data administration.
Silos usually go hand-in-hand with the need for specialized teams to pull data from separate sources, compile them into reports and hand them off to business users. As you can imagine, this fuels reporting backlogs because this tends to be a time- and effort-intensive process — one that can easily take days, weeks or even months. The larger the access gap between decision-makers and stored data, the longer the wait time to get insights.
Empowering Workers on the Front Line with Data
There’s also the issue of whether or not would-be front-line users can perform ad hoc queries on data without help. If no, this means they’re probably receiving largely static reports based on siloed data — which can provide an incomplete picture and offers little to no flexibility. If yes, decision-makers can keep asking questions, drilling down into data and pulling insights on an as-needed basis rather than waiting to make decisions based on a clunky information pipeline.
Mine Hidden Insights with AI & Machine Learning
Another facet of “information overload” is the existence of so many insights teams can get swamped trying to uncover them all — especially where tedious manual data mining by analysts is concerned.
Artificial intelligence (AI) and machine learning analytics address this challenge, using advanced algorithms to quickly uncover anomalies, trends and causal relationships buried deep within data. We’re talking millions or billions of data points here — so the ability to quickly parse through vast swaths of stored data is a game changer in terms of being able to act on findings.
The machine learning component of AI-driven analytics helps these algorithms refine as they go using human feedback, which helps them keep honing in on what’s relevant to specific users and teams. The result is human decision-makers getting pushed potentially useful insights in straightforward language so they can take advantage of them. In the old days, these insights could have otherwise languished as “needles” in the “data haystack” for a long time, if not forever.
There’s a lot of data out there, and companies today are facing the very real challenge of getting the right insights into the right decision makers’ hands at the right times. Having the tools to facilitate speedy, relevant search- and AI-driven analytics is the biggest differentiator here.