A s a software company that works with databases and Custom Data Analysis tools on a regular basis, it would be pretty simple for Beeline to say, “We are a data-driven organization.”

While this is certainly true, that would be a flimsy claim at being data driven. (We’re data driven because we work with data? Really?) So, what does being “data driven” mean at Beeline? It means that we strive to use a quantifiable basis to make decisions and measure success. OK, but what does that mean? In this post, I’d like to share a few specific examples to illustrate how Beeline truly adopts a data-driven culture.

Annual Personal Goal Setting

Many companies have an annual review process that requires employees to set professional goals for the year. At the end of the year, employees meet with their managers to review the success and outcome of these goals. Beeline uses a framework similar to this, but with a focus on setting specific, measurable, attainable, relevant, and timely (SMART) goals. Because these goals are measurable, each goal is set so that there can be no question as to whether the employee met it. Some goals are simpler to measure than others, particularly those associated with economic value.  A sales person, for example, may set a goal to “increase sales by X percent year over year,” or “increase leads Y percent year over year.” An operations manager may strive to “reduce costs by X percent.” In those examples, using specific percentages to measure goals makes sense, so how can you measure learning a new skill, or improving public speaking skills, for example? Both are admirable goals, but how can a manager or employee gauge whether these goals have been met?  At Beeline, acquiring a new skill may read, “Learn XYZ by completing two training courses.” Improving public speaking may translate into “Improve public speaking skills by delivering three lunch-hour talks to internal audiences.”

Beeline’s “annual” review process is ongoing throughout the year, allowing both employees and managers to measure and update progress at various points during the year, not just in December. Using this data-driven approach to goal setting requires employees to spend time defining their goals at the beginning of the year, but makes the process very simple to track as the year progresses.

Tracking Time on Software Development Tasks

A significant component of Beeline’s data-driven culture is tracking the amount of time spent on software development tasks.  This process begins with project managers and developers estimating the time required to complete a task. The process continues with analysts, developers, and testers recording time spent on their respective aspects of the work.

With this information, project managers take a data-driven approach to iteration planning (i.e., how many development items can be completed in a given timeframe). Project managers use this information during a development iteration to measure progress of tasks and identify tasks that are at risk of not being completed on time.

On a broader scope, Beeline uses this information to gain visibility into where most effort is spent and to identify opportunities for process improvement.

Quantifying Impact of Code Changes

Many software development tasks focus entirely on improving the performance of a piece of the application. Improving performance can mean different things to different people. In software development, measuring performance ultimately means measuring the length of time a process takes. Many factors make up the time a task takes to execute, and developers use a variety of techniques to make improvements.

At Beeline, our development process includes capturing baseline performance statistics on a feature that is changing (the “before” picture) and re-measuring after changes have been deployed (the “after” picture).  At a minimum, this includes measuring how much time a process takes.  With this data captured, we can make statements such as “reduced page load time by 10 percent” or “improved load time of a data warehouse table by 25 percent.”

The reasons that a process may take a long time are not always obvious. Once a developer determines the cause of the poor performance, additional metrics can be gathered to quantify impact of software changes.  For example, these metrics may include amount of server memory used, number of database records that are stored temporarily but never used, or number of steps used to complete a process.  In Beeline’s data-driven culture, developers are encouraged to take measurements such as these to quantify and illustrate improvements made.

Certifications

Beeline developers are encouraged to take software certification exams.  Achieving a certification is one measurable validation of skills.  At Beeline, it is also a good way to create competition between individuals and teams.  Without discussing the merits of certification, it is very simple to quantify whether an individual or team has achieved one.

Logging Win4Youth Kilometers

As you may know from earlier posts, Beeline participates heavily in Adecco’s Win4Youth program.  Employees record the number of kilometers completed each time they participate in a Win4Youth event, quickly creating a large volume of data.  Employees receive updates periodically on which individuals, business units, or countries have covered the most distance.

Survey Responses

As a strategic business unit of Adecco Group, Beeline employees occasionally take surveys or internal training updates administered by Adecco Group.  Adecco tracks who has (and has not) completed the survey/training. Adecco sends updates to leadership team members (including Beeline’s president), indicating the percentage of employees in each organization that have completed the survey/training. Being a data-driven culture, and having a competitive spirit, Beeline employees are highly motivated by these numbers. You can be sure that Beeline will have a very high completion rate!

These are just a few examples of how Beeline measures success. When Beeline says it has a data-driven culture, it is more than just lip service. It’s quantifiable.

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