How to identify and implement integrated analytics opportunities
Integrated analytics covers both software development and data science workflows. Software development tends to focus on functional requirements. Data science and analytics tend to focus on how best to develop models. Business users should take a step back and consider both disciplines to assess integrated analytics opportunities.
To get started, it’s helpful to identify a few examples of built-in scans that might correspond to existing problems. Developing experience with these projects can help teams discover new embedded analytics opportunities.
Start by analyzing the user experience. Teams must also address customization and integration challenges. It is also helpful to develop an iterative process that allows integrated analytics projects to improve over time.
Here are four places to look for opportunities:
- New product offerings. Sal Stangarone, partner at Michaels, Ross & Cole Ltd., a software development consultancy, said many software vendors are turning to embedded analytics as part of a new product offering. The technology helps them improve their product without the need to integrate analytics functionality into their software. This allows them to offer analytics capabilities as another selling point, or even as a new paid feature to improve customer experience.
- Lower reporting tools. Stangarone sees many people turning to embedded analytics to improve the lackluster reporting features found in software packages. ERP reporting is a typical example. If a business does not like the reporting capabilities of their ERP, they can integrate custom reports or a reporting tool into their ERP to provide high quality data to the business.
- Process gaps. It’s also helpful to understand how analytics can improve day-to-day business decisions. For example, before a manager approves a raise for an employee, they probably want to check that it matches the salary of other team members. Interviews with managers can identify shortcomings in this process, such as the need to leave the system to answer questions before making a decision. This is a great opportunity to improve the user experience by integrating the information that supports the required action so that it is streamlined into the same workflow.
- Decentralized systems. Robby Powell, Advisory Product Manager at SAS, evaluates decentralized systems for integrated analytics opportunities. Look for cases where metrics are collected from multiple sites. Decentralized embedded analytics can provide value by helping users identify issues that need to be resolved as soon as possible, and at the source of the problem where it is easier to manage. He recommends focusing on situations that require high model accuracy. Built-in analytics can help assess model performance at each edge location and in aggregate, and then update models as needed.
Personalize the experience
Once teams have identified embedded analytics opportunities, it’s time to start integrating them into applications. It helps to think about user experience as part of this process. Sri Raghavan, director of data science and advanced analytics at Teradata, said this first requires understanding each user’s unique data and analytics needs and type of environment. in which it consumes this information. For example, some analysts prefer relevant KPIs integrated directly into their applications.
Next, teams need to address customization issues when integrating analytics. Personalization helps analysts focus on their needs. Raghavan said this typically requires finding ways to take only the relevant subsets of data and information that are typically crammed into a large dashboard and displaying them to specific individuals or groups responsible for task.
Integrate and iterate
Integration is one of the biggest challenges of embedded analytics, Stangarone said. Much of it comes down to architecture: some vendors build their software on a proprietary architecture, while others use open architecture and frameworks.
“It’s crucial to understand how your software will integrate with the vendor’s platform before you even get started,” he said.
He worked with a software vendor who spent three years and over $100,000 trying to integrate analytics software into their product, but still had a long way to go. Rather than continuing to spin the wheels, the vendor switched to a different platform based on open architecture and was up and running in less than three months.
It is helpful to develop a process for reviewing built-in analytics applications to provide the desired value and adjusting as necessary. Integrated analytics is likely to be driven by IT teams, but it’s not just an IT initiative.
“It’s an iterative process that requires the collaboration and alignment of individuals across the organization,” said Scott Gnau, chief data platform officer at InterSystems.
Different teams should work together to continuously assess the contributions made by the introduction of integrated analytics. Use cases and required metrics should be reviewed often to understand if efforts are delivering value and what changes might be needed to measure progress. This helps resolve any issues as they arise and ensures that all users get real value from the platform.