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Productivity Improvement | 09.25.2020

Applying Next-Gen Work Analysis and Insights Across Your Enterprise

The evolution of enterprise analytics tools is certainly not complete. The current generation of work analytics tools are either highly specialized by function or built to measure only a few dimensions among the many that make up enterprise-wide productivity. To get a better picture of what a next-generation work analytics tool should embody and the insights it should provide, we caught up with Nitin Maini, Sapience’s Senior Vice President of Engineering.

“Organizations will make better decisions once they understand not only where their customer pain points are, but where their own organizational pain points are,” he said. “When this data is collected from different tools across the enterprise, but standardized and made centrally available, then your strategies can all become data-driven.”

Specifically, an enterprise-wide work analytics tool needs to be able to:

  1. Establish deep integrations with everyday productivity apps and convert nuanced actions into a common language of data that can be applied across tools and processes.
  2. Anonymize the data to fit the needs and laws under which any organization operates.
  3. Establish the connection between effort and output – and how this connection integrates with the aspirational goal of measuring and predicting outcomes.

Let’s take a closer look at what these insights could mean for your enterprise.

A Common Language

Historically, employee productivity has been measured in terms of how many hours employees have been present on site. More recently, screen capture tools provide information on technology usage but still do not tie any of it back to delivery of outcomes. Nitin says the next generation of analytics tools will be expected to connect effort and tool usage directly to the organization’s KPIs.

“These analytics will be expected to coordinate effort to output, and to the tools used, and to how both effort and output support your KPIs,” he said. “Are employees working to support organizational KPIs? Or are they working on something else? This kind of visibility is extremely critical to making your enterprise-wide processes more efficient.”

This is also integral to recognizing operational pain points: Are your employees making the right efforts but still not achieving the best outcomes because they don’t have the best tools, or because they are pursuing the wrong goals, or because their indicators are not properly tracked? The latest analytics tools will record what processes or tools are working for each team and help with the effort of applying those insights to the processes of other teams.


Another key to enterprise-wide data is cutting away the chaff to hone in on only the data that best reflects the state of the enterprise. This means aggregating data to the team level or above.

“At Sapience, we support whatever data collection is important to the organization, according to their standards and their goals for consuming their own data,” Nitin said. “At the enterprise level, the goal is to use data to understand capabilities and remove bottlenecks from the system in order to enable digital transformation — not to target the individual.”

The Aspirational Goal

In 2020, as much of the knowledge workforce has become a remote workforce, the focus has shifted to expected outcomes as the best measure of employee productivity. Establishing the connection between effort and output helps optimize the processes that result in an efficient organization. However, organizations need to be able to measure output (not just effort) at every level, top to bottom. Then the question becomes, how does an enterprise best condense its own output into a common language across departments, regions, business units, etc.?

“One way to think about output is through the prism of predictability, velocity and quality,” Nitin said. “These three dimensions provide a great framework for unifying measurements across business units and functions. Then, these three dimensions can be developed into a form of standardization across the enterprise.”


Nitin defines predictability as understanding consistent delivery and making projections based on historical trends. There are two parts to this:

  1. Is the organization delivering what it agreed to, in terms of quantity, timeliness and quality?
  2. Then, can the organization become more predictably efficient in its delivery in the future?

“My team might say we can do 100 units today, and 150 tomorrow and 170 later this month – but historically I need to know we have only been capable of delivering 80 units,” Nitin explained. “So I will predict that we are capable of delivering 80 or 90 units for the next month, but if we improve something for the team, they can go further. We just need to establish what specifically needs to improve.”

In next-gen analytics tools, machine learning will analyze trends from historical delivery and will make predictions based on all aspects of the data put forth for analysis. However, it’s critical that it also recognizes anomalies and surprises in the data, incorporating them in order to establish a baseline of predictability.


Velocity is simply the speed at which outcomes are delivered and why. It takes into consideration the complexity of each project, the tools in place to support each project, and whether best practices are being followed.

“For instance, Team A has more automation so they’re delivering more things faster. Team B is delivering slowly, but we discover they have less automation – and now we can make some improvements to reduce their time to value,” Nitin said.

“Similarly, when comparing two development teams, if we find one is delivering more complex features than the other in the same time period, then there might be bottlenecks in requirements analysis, in collaboration, or in some other area. You can now bridge the gaps and apply the best practices from Team 1 to Team 2, and vice versa.”


Quality is defined as output that can be standardized, such as number of sales presentations delivered, against the number of issues observed in the output, such as software bugs.

“At Sapience, we drink our own champagne – we use our own tools in our own work so we can track our outcomes and see problems and trends over time,” Nitin noted. “For instance, what are the quality and severity of any bugs, and how much time is needed to fix the issues? How do we ensure that the bugs don’t repeat? Then we track the time and effort needed to fix the issue and get back into delivery.”

Apply the three functions of predictability, velocity and quality to any business unit, and you’re on the way to standardizing your enterprise applications.

Standardization for Your Enterprise

Standardization is the process of using data to recognize and apply best practices from any team across the organization.

Identifying Best Practices

This means noting what is working for a particular team compared to other teams and then breaking that process down to its most basic component. You could end up identifying a process improvement, a different use of technology, better communication tools, etc. This concept can then be taught and applied across teams; how each team applies the concept will be different.

Standardizing Data

In addition to standardization of process tracking, tool usage tracking is equally important to standardize. The concept is similar to process tracking where the interaction data for applications is broken down to its fundamental components, allow it to be aggregated into data from other tools and systems.

Nitin elaborated “For instance, for a chat app, the data could include the senders, receivers, attachment types, etc. Then we apply machine learning algorithms to process this data into a standardized form.”

Going Enterprise-wide

Once you identify best practices and standardize any data collection, the next step is to make the enterprise-wide adoption seamless by integrating throughout both legacy and new infrastructure systems.

Going enterprise-wide also entails allowing a finer level of control at the team and tool level so companies can stay compliant while aggregating workforce analytics data across the enterprise.

Nitin said. “We envision Sapience Vue as standardizing effort to output correlation, as well as predictability and velocity elements. This is what a true enterprise level workforce analytics tool should be able to do.”

The Next-Gen Workforce Analytics Tool Available Today

Your next-generation enterprise analytics tool isn‘t just part of some theoretical exercise outlining a distant future. Sapience Vue standardizes and connects your data so it provides insights into predictability, velocity and quality. Because there are zero degrees of separation of insights about your organization, it’s designed to illuminate any bottlenecks in your system and to grow your businesses based on reliable, predictable, qualitative strategies derived from your organizational data.

Sapience Vue illuminates the correlations between effort and output. As a result, Vue can become an enabler of digital transformation within the enterprise, providing data-driven strategies and actionable insights at all levels of the enterprise.

To learn more about how Sapience Vue can standardize and illuminate the data from across every aspect of your enterprise, request a demo.