I thought what better way to start our blog than with theorigin of Peoplelogic.ai. Too often, origin stories get lost in the shuffle aswe have our heads down writing code, talking to customers, and growing thebusiness. I wanted to share this story early, as the heart of Peopleogic.ai hasbeen a challenge that I struggled with throughout multiple stages at my lastcompany.
Over the years of growing DZone we very often erred on theside of giving high performing team members the opportunity to be managers. Afterour exit, I spent a lot of time talking to other companies going throughsimilar stages of growth and found they had similar philosophies around promotingfrom within. However, these companies frequently failed to give their peoplethe tools or training to be successful. What these companies also had in commonwere their many different systems, all containing data about their teams – salesdata, support data, HR data, daily activity metrics, the list goes on. With allof the data these companies had access to, they were still consistentlysurprised when people left the company or teams underperformed. So, I began tothink about whether there was a way to use the data these companies weregathering to predict churn.
In my wandering in the desert trying to discover a solution to this problem, I discovered the world of people analytics. I found that the ways larger companies were making use of technology, in particular, artificial intelligence and machine learning, to predict everything from candidate success rate to succession modeling, were fascinating and intriguing, and sometimes downright scary. What I also found was that small-to-mid-sized companies are still finding their footing when it comes to people analytics. For these companies with small HR departments who are simply trying to keep their heads above water with managing day-to-day processes, embarking on a people analytics journey just sounds exhausting. What if I could help these companies leverage their systems to get broader insight into the great work their teams were doing and not simply help them predict the churn on their teams?
The business world is changing in a key way in that 75%of the global workforce will be comprised of millennials by 2030. This groupis already managers or will quickly become managers and they expect to manage –and be managed – differently than previous generations. They want to beunderstood on an individual level and be managed to their strengths andpersonality. This generation also has much broader access to insights and datathan ever before and companies are generating more data, and faster, with theiruse of cloud tools. So, when it came to Peoplelogic.ai, I saw the opportunitywas much bigger than simply predicting churn. The opportunity became helping fast-growingcompanies continue their growth even as they scale.
At least once a week I find myself looking at my 11-year-old son walking around with his shoelaces undone (it’s possible he does this just to frustrate me!). I have to constantly remind him that he’ll trip or catch the laces in something if he doesn’t tie his shoes. Arguably, the biggest struggle a company goes through while growing is building a great set of managers that consistently get the best out of their teams. It’s the same scenario with my son as for these small-to-mid-sized companies. They’re trying to run fast, but if their managers and teams aren’t tight, they will inevitably trip up and fall on their journey.
The reality is, managers want to be able to use the datathey have at their fingertips to make better decisions about how to run theirteam, but in companies of all sizes, that data is rarely consumable oractionable. Large companies devote a great deal of money and resources togleaning better people insights from their data. However, small-to-mid-sizedcompanies are not in a position to hire a data scientist and people analyticsteam, although they are generating just as much data proportionally from theirsystems as larger organizations.
By leveraging the latest advancements in cloud, artificial intelligence, and machine learning, we can use the data that’s generated through the day to day activity of our teams to produce insights and recommendations personalized to the individual employee in a way that aligns with their strengths and personality. The data and insights that surface allow managers to make the right decision at the right time more frequently, taking the guess work out of management.
So, I’ve set out to make all this data and the insights accessible. Peoplelogic.ai exists to bring the power of people analytics to small and medium sized businesses and to surface the insights already being captured in their systems to help managers be more effective. Our mission is to help their teams be higher performing and help the company continue to grow with less risk as it scales. In small companies, part of the reason managers fail their people is because they’re trying to be a manager and individual contributor. So, we built Peoplelogic.ai to help them be better at both.
Peoplelogic.ai gives managers actionable recommendations and insights that help spot and correct problems on your team and between the other teams you work with, before you miss your goals. Get started for free.
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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