Source: rtinsights.com

Every company aspires to be data-driven, but it takes expertise and investment over long periods of time to attain this. In an era of change and disruption, the months — or perhaps years — it takes to build and deploy data analytics solutions may be too late for a struggling company. It also means hiring data scientists and analysts, often with PhDs — another onerous, time-consuming process. Is there a better way?

There is an emerging class of pre-built analytics that may help provide shortcuts around the trials and tribulations involved in attempting to build things out onsite. “Automation is removing the need for developers to be paired with traditional data scientists,” writes Nick Jordan in SD Times. “The vehicle that is accelerating this transition is the API or application programming interface, the mechanism by which different software platforms talk to each other.”

Data analytics and data science are increasingly becoming automated, and “as businesses make the leap from big data to AI, and automation becomes increasingly sophisticated — with every major cloud vendor already investing in some type of Auto ML initiative — fewer organizations will need the traditional data scientist, and data engineers will be able to harness the power of a Ph.D. through APIs,” Jordan adds.

In addition, companies are increasingly relying on analytic already pre-embedded into their enterprise applications. Companies seeking to step up their analytics game need to reduce their software build times, reduce costs from ongoing development and maintenance, and increase user productivity, a recent report from Nucleus Research concludes.

When adding dashboards, reports, and other analytics features to software, embedded analytics may cut software build times by up to 85 percent, according to the study’s author, Daniel Elman of Nucleus Research. “Companies who eschewed internal builds in favor of an embedded solution were able to go live with hosted analytics functionality in an average of three weeks,” “Homegrown builds were projected to require, on average, six to eight months.”

An embedded analytics application is one that is built into and executed from within enterprise applications, Elman explains. This feature is valuable to companies since “data science skills are scarce, and every company cannot afford to find and hire developers with the qualified background to create and maintain a custom analytics solution.”
Over the next five years, more than half of all corporate job functions will include self-service analytics responsibilities such as insight discovery and reporting, Nucleus estimates.

Elman cites the example of an industrial equipment manufacturer which was outgrowing its data management and analytics framework. “The company was falling behind in deliveries and lacked the internal visibility to track progress and diagnose issues,” he said. The company plugged its a pre-packaged analytics solution into its ERP environment, and “as a result of the deployment, the company was able to reduce late deliveries, with total orders delivered on-time increasing by 18 percent. The constant data integrated with the application ecosystem allowed the company to reduce the time to act on new leads by 15 percent. More generally, the organization was able to eliminate paper-based reporting and adopt a more data-driven culture of accountability and trust.”

There are cases where embedding the solution is not the most feasible approach, Elman cautions. “For example, in speaking with end-users, we found that applications requiring highly-specialized functionality or handling massive volumes of data are often better suited for standalone solutions or self-builds. “

In addition, care must be taken in fitting the right analytics approach to the business problem at hand. “Embedded solutions are more lightweight and tend to lack the back-end computation engine and complex data management architecture as compared to more traditional standalone analytics solutions,” Elman says. “As a result, embedded solutions are ideally suited for executing simpler processes like creating reports, producing graphics and data visualizations, and tracking key benchmarks and indicators with dashboards. For more complex processes such as big-data analytics — predictive, prescriptive, diagnostic — and machine learning, standalone solutions are the more feasible choice due to their more robust data handling capabilities and more labor-intensive functional options.”