Outcomes are an important motivator in the business world and beyond, but just how much are companies driven by the end goal that inspired their whole enterprise? What if the process of reaching a desirable outcome was more efficient because metric-based evidence had eliminated time-wasting efforts?
Automatic data analysis will allow workers to significantly shorten the time dedicated to research and grappling to form insight needed for reliable, focused planning. As companies grow their presence on social media, through email, and obtain access to other data sets that could augment and inform their internal information, the chance to quickly understand a market shift grows more realistic. These new forms of customer information are growing in business thanks to content-driven marketing, and should be employed to create more efficient market segmentation development. As tools to interpret and act on data become increasingly more effective and more automated, the time to go from data collection to action shrinks from months and years to days and weeks.
When a team uses an automatic analytics tool, the project is done in a fraction of the time. As workers collect more data on the outcome of projects, they can discover inefficiencies and address them. This revolution doesn’t stop with the projects above, though. Worker productivity is has been a challenge for ages, borne both from the need to produce more and for the sake of making workers’ lives more livable. When data can be collected around the kind of job done, how long it takes to complete it well, and what skills are needed to do the job, companies are able to perform more targeted hiring to find the right people, and potentially to hire based on the metrics of a team’s strengths and weaknesses.
Finding the Right Person the First Time
Hiring the right person is a monumental challenge for many businesses. Every bad hire costs money. More importantly is the cost of lost time, especially in mission-critical roles. The movement towards measured outcomes will create efficiency in the workplace, but it will also help workers establish a reputation of credibility based on reportable facts about work completed. While the prospect of outcome-driven work might make some who take pleasure in the process balk at the thought of working only for the end product, both worker and employer ultimately benefit from developing reachable, measureable goals and outcomes. The company has the benefit of analyzing the data to formulate new working styles and suggestions for improvement, and the worker has the benefit of being able to report his or her experience in very concrete terms to the next place that he or she might work.
If companies could hire based on a candidate’s performance metrics, there would be more time to focus on how that person would fit into and contribute to the company—both as a player in an immediate team and how that worker might be the missing cog a company needs to perform optimally. A move towards analyzing and rewarding workers based on outcomes would allow human resources departments to spend less time guessing if a person’s skills would make the most sense for a job and spend more time considering if the person is a cultural fit, if they’ll contribute to the company’s mission, if they’re likely to stick around and try harder, and if they’ll ultimately add or subtract from the company’s value.
Data is going to drive the analysis of potential workers—and will also become ever more integral for hunting down the right employees to analyze data and develop a mission to act on from it. The beauty of the data worker is that he or she already works in the world of incredibly measureable outcomes. As industries develop better analytics systems and come to new breakthroughs in how to see projects through to the most successful end, we’ll also have better tools to grasp a metrics-based understanding of just how and why a project was successful. The metrics around the people developing metrics will allow for micro-segmentation of workers who have highly specialized skills. Employers will find the programmers they need, and root out who has the most transferable skills for their project.
It all comes down to the right person for the job, and allowing teams to better gauge the intangibles before hiring a person. When you know solid facts around a person’s competencies and have the predictive technologies to understand just how successfully those skills will play out, human beings are left to do what human beings do best: intuit team and culture fit, assess behavioral quirks, and woo talent away from the other organizations that might snatch it up first.
Let Innovation Run Free
Data has laid the path to potential workplace utopia. Organizations will have their metrics-driven work completed by their tailor-chosen workers. What more could be needed, right? What could go wrong in a world run by numbers?
While a metrics-driven world is counter intuitive to a vast number of authorities around the world in education, business, and economic development, something must be done about the barriers to innovation. A number of technologies spurred incredible economic and humanitarian growth and innovation in the world. From harnessing electricity to splitting an atom, we’ve done and seen a lot since the early 20th Century. Workers are more productive today per capita than at any other point in history. What are they doing, though? What’s the real output? What if that output could be spent doing fewer rote tasks and more innovative thought and conceptualization and utilization of ideas that could change the way we think and operate?
One of the most exciting prospects the data economy brings for the working world is the opportunity to spend less time collecting and analyzing the information needed to act and more time acting on it and understanding it. Creativity is the synthesis of thought and action, and the chance to automatically reach insight into the information lodged in data could be huge for anyone from an undergraduate liberal arts student to an elderly man with a strong chance at beating an unexpected cancer. The mean of our population spends its time as a working stiff. What can efficiency born of better data-driven outcomes and practices do for the working world? It can free our minds and our focus from trivial tasks that simply take up too much time.
Time is of the essence when attempting to create something from little or nothing. Thankfully, there doesn’t have to be a reason to feel crunched for time when we have answers for ways to save and spend it wisely. There will probably always be some functions that involve time-intensive research and creation of a product, and perhaps there will be even more jobs like this in the new data economy.