The higher transaction velocities and data volumes associated with modern applications are driving change across all industries, causing more data to be generated today than ever before. The goal of modern organizations is to be able to harness all of this data—regardless of source or size—to generate actionable insight from it. This is known as analytics.
Advanced analytical capabilities can be used to drive a wide range of applications, from operational applications such as fraud detection to strategic analysis such as predicting patient outcomes. Regardless of the applications, advanced analytics provide intelligence in the form of predictions, descriptions, scores, and profiles that help organizations better understand behaviors and trends.
Moreover, the desire to move up the time-to-value for analytics projects is causing a move to more real-time event processing. By analyzing reams of data and uncovering patterns, intelligent algorithms can make solid predictions about what will occur in the future. This requires being adept enough to uncover the patterns before changes occur.
Issues in Deploying Advanced Analytics
Although adopting advanced analytics is on the radar for most organizations these days, it is important to understand some of the problems that can occur as you implement analytics projects. Perhaps the most important obstacle to overcome is ensuring buy-in from your organization’s leaders.
Change is rapid in modern organizations, to the point of being impossible for humans to keep pace with. Cognitive computing applications that rely on analytics can ingest and understand vast amounts of data thereby keeping up with the myriad of changes occurring daily—if not faster. Armed with advice that is based on a thorough analysis of up-to-date data, executives can make informed decisions rather than guesses.
After adopting analytics, managers need to be willing to embrace the data-based decisions formulated with analytics—instead of gut feelings and the illusion of sufficient information, which is often the case today. It can be difficult to convince leaders to forgo their previous decision-making patterns based more on intuition than on the facts. As such. Some managers may push back on the advice proffered by analytics-based decision making. Without buy-in at the executive level, analytics projects can be costly without delivering ROI because the output, which would deliver the ROI, is ignored.
Another potential difficulty involves managing and utilizing large volumes of data. Not only is new data being created all the time by your applications, but additional data is being purchased to augment existing business data. This breadth and depth of data availability is one of the driving forces behind analytics. The more data processed and analyzed, the better advanced analysis can be at finding useful patterns and predicting future behavior.
Even so, as data complexity and volumes grow, so does the cost of building analytic models. Before real modeling can happen, organizations with large data volumes face the challenge of getting data into a form from which they can extract useful information. One of the most time-consuming steps of analytic development is preparing the data. As the amount of data expands, the analytical modeling process can elongate, making time-to-market an issue.
Real-time analytics can also present difficulties. Being “real-time” implies a level of responsiveness that is immediate or nearly immediate. Such immediacy may be driven by factors like market forces, customer requirements, technology changes, and governmental regulations. As a result, many organizations are working to assess and respond to events in real time based on up-to-date and accurate information, rules, and analyses.
Although it is undeniable that up-to-date data and real-time analytics is desirable, it is not without its challenges to implement. One challenge is reducing the latency between data creation and when it is recognized by analytics processes. Latency between many copies of the data poses another pitfall that must be overcome for real-time analytics to occur.
And we have yet to discuss the potential technology issues that can be encountered when adopting analytics. With new technologies comes new operational challenges.
Performance and data movement can cause problems, as with any complex IT application. Good practices and good software can help to alleviate such issues by ensuring efficient and effective monitoring and data movement is in place.
Taking advantage of more in-memory processes, such as with Apache Spark, can also be an effective approach for managing analytical tasks. Another technology that is becoming more popular for analytics is streaming data software, which involves the ingestion of data—structured or unstructured—from arbitrary sources and processing it without necessarily persisting it. But there truly are no technology panaceas for effectively implementing analytics.
The Bottom Line
The benefits of deploying advanced analytics are many including increased productivity, the ability to gather and analyze large volumes of data, and the ability to make faster, more-effective business decisions.