How Time-Tracking and Data Analytics Can Help Drive Better Business Decisions

Law firms, accountants, consultants and other professional services businesses have long been accustomed to tracking employee time for billing and payroll purposes.

But there’s gold in that time tracking data — more than just what comes from billings.

Analysis of that data allows firms to gain insights into productivity, plan for staffing demands, improve operations, learn which clients or projects offer the highest potential profitability, and even qualify for R&D tax credits.

Companies need not be large multinational operations or professional service firms to take advantage of such time tracking and data analytics capabilities. Small and mid-sized businesses can choose from several online time-tracking applications, including some that say they can tap artificial intelligence (AI) technologies to offer automatic time-tracking and data analysis to help businesses make better decisions.

Time Is Money

Accurately predicting how much time and money a project will require is a challenge that trips up many companies, sometimes with costly results, according to a study by McKinsey & Co. Most firms’ projects “massively blow their budgets,” said McKinsey.

Of more than 5,000 large IT projects the consulting firm studied, the average project ran 45 percent over budget, blew past its deadline, and delivered 56 percent less value than predicted. Software development projects showed the highest risk of busting budgets.

Scary as that sounds, some projects can morph into a “black swan” nightmare that puts the company in danger. According to McKinsey, about 17 percent of big projects it studied went off the rails so badly that they threatened the companies’ very existence.

Some experts also warn that time-tracking brings its own challenges if used unwisely. Decisions will only be as good as the data they are based on. Sometimes it’s unclear how an AI-based system allocates employees’ time to various projects and clients. Clients may be leery of accepting bills spit out by a so-called “black box” computer algorithm.

Here’s a look at the potential uses and challenges of tracking how individual workers spend their days and how companies use that data.

How time-tracking works

While consulting and law firms and other businesses that are driven by billable hours have long required employees to track their time, it is often a despised and neglected task. A number of software developers, professional services and other businesses are tackling that problem using automation, GPS technology, and AI to improve the reliability and usefulness of companies’ labor data, according to an article by Socialnomics.

Time tracker apps such as Timesheetmobile.com and busybody.com use the GPS sensors in employees’ smartphones to track how work gets done at construction projects, for instance.

According to Socialnomics, the software firm Rescuetime pioneered desktop applications that track employees’ time spent on various tasks at their computers. The software provides visual reports that allow workers and their bosses to see how much time they spent working on projects versus watching cat videos.

The next step, according to Socialnomics, is using artificial intelligence and automation to gather and analyze time-tracking data.

San Francisco-based Allocate, for instance, says it captures raw logs of employees’ calendar events, email subjects, desktop activity, etc., and uses AI technology to help figure out which projects the work applies to. Employees still have to make their own decisions and review reports to see that the work is assigned to the correct projects.

Timely, an Oslo, Norway-based time tracker, says its software can fully automate time-tracking by consolidating employees’ activity from multiple sources — Gmail, Google calendar, website URLs, GPS tracking, etc. — into a clear report on activity.

That firm says it is rolling out an AI application, Memory AI, that allows companies to track their daily revenues, expenses and profits at the project level. The software can help managers determine how their past decisions affected the bottom line, and make predictions about a project’s completion time, costs and financial performance, according to Timely.

The Memory AI application uses natural language processing of data from multiple sources in the company, including time-tracking applications, bookkeeping systems, CRM and resource scheduling software. Managers and employees initially have to input task-related data to “teach” the software about what’s going on in the company, according to Socialnomics. But ultimately, the company ends up with a custom-made system that can produce “significant and magical results,” Memory AI told Socialnomics.

Other workplaces besides those that bill clients by the hour also minutely track work times, according to Peter Molnar, a senior research scientist at Georgia State University and senior data scientist at Fabric.com.

Using barcode scanner data, some fulfillment centers “track virtually every move” by their workers to look for ways to make the process more efficient, said Molnar. Many call centers also automatically generate “tickets” for each customer call, then track the time it takes employees to resolve or complete those tickets.

Such tracking is also often used in so-called “agile” software development, he noted. The work is broken up into tickets with rough time estimates to reach each milestone, allowing the company to calculate the “velocity” of a team and to track the progress and scope of the project, said Molnar.

The idea of using AI to automatically track employee time is still a hard sell to clients wary of being billed for hours of work determined by a computer. Such algorithms still have to do some guessing about how to allocate employee hours to various client jobs and projects.

Some may question whether AI-based applications are ready to take hold in the marketplace, and how they can best be used. Are they truly useful and accurate? Is AI-based time-tracking good in certain settings — like manufacturing, where your location and interaction with a device indicates your activity — but not in professional services?

What’s Past is Prologue

However it’s generated, time-tracking data can help with project execution issues such as unrealistic schedules and reactionary planning. Poor focus and unclear goals were the biggest cause of troubled projects, according to McKinsey.

Time-tracking data can yield the information companies need to tackle this challenge, but managers may need to re-think how they put together budgets for future projects, according to San Francisco-based firm Allocate.

The root cause of such project mishaps is something called the “planning fallacy,” according to economist Daniel Kahneman, who won the Nobel Prize for his work on the topic. Humans tend to run into trouble, he argues, because of overconfidence. They fail to take into account the variability of how past projects turned out when planning the next one.

To counter this tendency, he said, companies need an accurate database of past project outcomes — what he calls a “reference class” — so that they can see, of instance, that one project took 200 hours of work while a similar one took 300 hours because of variables that may not be under their control. The company can then make a more objective estimate of how its future project compares, and what the range of labor demands and costs might be. This, he said, is “reference class forecasting.”

Kahneman’s technique becomes especially powerful, according to Allocate, when a company uses its time-tracking data to look at how multiple phases of past projects turned out. The odds of each stage of a project going off-kilter due to changing client demands or sick staff may be relatively low. But link all the stages of a project with multiple players together, and “the joint probability that at least one of these elements will go awry in rather high,” said Allocate on its blog. The firm said its time-tracking and project planning software “allows you to become a reference class forecaster, likely without even knowing it.”

How To Go Beyond Billing

Even in day-to-day operations, time-tracking data offers a lot of potential uses beyond simply determining client billing.

Time-tracking data can help with determining which individuals and departments are more or less efficient, or point to other performance issues. Deeper analysis can give managers a better understanding of the company’s capacity to take on additional work. Data analysis also can pinpoint why some projects tend to bog down more than others, perhaps because they include repetitive tasks that can be automated or outsourced.

By using time-tracking data to gain deeper insight into its staff’s productivity and work capacity, a company also can get a better handle on which projects or clients are most or least profitable. Combined with information on upcoming client jobs, a firm can determine when it needs to staff up or down.

Accurate time-tracking data can also provide the documentation that software and other technology firms need to claim valuable R&D tax credits.

Thanks to the 2015 Protecting Americans from Tax Hikes Act (the PATH Act),small and mid-sized businesses have an easier time claiming a dollar-for-dollar offset to income taxes for qualifying research expenditures. The company’s spending must be related to developing and improving new products or processes, and should involve hard science or engineering, such as computer science or biology.

But such tax benefits can help fund labor expenses, whether for traditional employees or outsourced hires – which is where tracking employees’ relevant work time comes into play.

Summary

Time tracking isn’t just for law firms, accountants, consultants and other professional services providers. And its benefits can extend far beyond billing and payroll.

Analysis of time tracking data allows firms to gain insights into the productivity of their workers, plan for staffing demands, provide verification for R&D tax credits, improve operations, and even learn which clients or projects offer the highest potential profitability.