There’s no doubt about it, data science holds the keys to creating an unfair competitive advantage, operational excellence, and a whole host of other advantages in the modern business landscape. Data science, was labeled as “the sexiest job of the 21st century,” by the Harvard Business Review. With demand that is through the roof, the role is a tough role to hire for, particularly if you are a small to mid-sized business. So, how do smaller organizations compete when the cards are stacked against them? For these organizations, using automation and AI-augmentation allows them to empower everyone to become a citizen data scientist within their own organization.
Gartner defines a citizen data scientist as, “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.” So how do you create a colleague who can conduct both simple and highly sophisticated statistical and analytical analyses, without the previously required experience of a formal data scientist? By leveraging tools that incorporate artificial intelligence, machine learning, and augmented analytics solutions.
There’s been a natural evolution in empowering employees to conduct data-driven analysis over the years. The most recent trend has certainly been data visualization platforms and dashboarding tools that make data more accessible, easier-to-understand, and make it somewhat faster to make data-driven decisions. The problem? Data visualizations (and more traditional static data analysis) leave the door open to misinterpretation and bias, particularly when an employee doesn’t have formal expertise in analysis and data science. These tools are also costly, require infrastructure upgrades to warehouse and structure data, and at the end of the day rely on the end users’ ability to derive what insights are important, and what should be done based on the latest information.
Every millisecond of every day, organizations are creating, collecting, and structuring immense quantities of data to become better. People that are in roles that traditionally didn’t rely on having to plow through massive datasets in order to effectively perform, are now interacting with incredibly vast amounts of data daily. Aside from a lack of formal data science training, and the use of tools that leave room for bias and misinterpretation, there’s also another problem—they just don’t have the time to go into all of these disparate platforms, structure the data, and analyze it in a way that makes it actionable.
Employees today deserve a better solution—a tool that can seamlessly connect to a number of the platforms they, and their teams, use on a daily basis to get work done, deliver an awesome product or service, keep customers happy, and generate revenue. Just as important though, is that to truly become a citizen data scientist, employees need tools that deliver value across the data maturity curve—descriptive insights (“what happened?”), diagnostic insights (“why did it happen?”), predictive insights (“what will happen?”), and most importantly prescriptive insights (“here’s what we do about it”).
Peoplelogic is a tool that executives and managers are adding to their toolbelt to do exactly that. For line of sight managers who are grappling with their team’s goals, their own individual contributions, and continuously dedicating time to being both a player and a coach—they too deserve the optionality of becoming data-driven—so the platform automates prescriptive guidance, recommendations, and insights to deliver a superior employee experience and team performance. Likewise, by providing a mission control center that keeps an eye on the overall health of an organization—things like retention, potential attrition, employee well-being, engagement, productivity, and performance—executives can mitigate imminent risks and automatically surface opportunities for optimizations in real time.
Peoplelogic is redefining how businesses get work done. By empowering everyone in an organization to become a data scientist, regardless of their level of comfortability or expertise with data, companies can rest easier knowing that everyone in the organization is making the best call they possibly can—rooted in real-time data and fact—and ensure that growth stays on track. Get started with Peoplelogic and start to understand how your company gets work done.
In this guide, you will find:
- OKR principles
- Formulas & scores
- OKR methodology
- Step-by-step guide
- Free OKR templates
- Common mistakes
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