What if Every Employee Had Their Own Data Scientist?
Imagine a world in which your company had the resources to provide every single manager and employee, with their own personalized data scientist. The amount of data being generated by companies globally grows at an explosive exponential rate year over year—and companies regardless of size, that are now cloud-enabled continue to launch that number skywards. Often, it’s already too much for your average Excel junkie to decipher. The largest companies leverage teams of analysts and data scientists and a portfolio of tools to help make sense of the stories hidden within that data. So, how would it be possible to equip each employee with their own analyst or data scientist?
What would hiring a data scientist per employee cost?
According to Glassdoor, the average salary of a data scientist in the United States checks in at $113,436. If you have a company of 75 employees, and you were dead-set on providing an analyst or data scientist for each of those employees—the cost would be north of $8.5 million dollars. Now, of course one data scientist can provide analysis, modeling, and reporting for more than just one employee at a time. Typically as a company grows and the data science team grows, they get siloed into business units and then teams, but often become over-burdened much quicker than you would think. After all, everyone has pressing analysis that needs to be done, couple that with a professional job demand that is much higher than the supply, and we’ve got a situation where organizations are missing out on vital insights.
Well, that’s not possible, what is?
So, is there a feasible way to democratize data analysis and expertise, while also scaling the functions required to provide significant insights that continue to move your business forward? This is where Artificial Intelligence (AI) and Machine Learning (ML) have great applicable value creation. Now, a lot of people that hear AI and/or ML think one of a few things, “AI/ML is all Blackbox,” “AI/ML can do anything and everything,” and then of course the obligatory, “You have heard of Skynet, right?”
Let’s start with Skynet first: we’re not there, yet.
For those that think AI/ML applications have value creating benefits absolutely anywhere and everywhere: again, we’re not there yet, but there are specific applications where these tools provide a TON of value.
The black box issue is an unfortunate result of the industry. When AI/ML became a bit mainstream, every company in the world had to have it, had to be a part of developing it, and had to market these solutions as if they were a magic cure all. What happened was a lot of initial early adopters, particularly at large organizations that created a new budget line item around AI/ML initiatives, were left scratching their heads saying to themselves, “well, this isn’t so magical after all.” The early applications, and many today, still require a lot of human hand holding, which is great for the Skynet folks. However, companies have recently bridged the technological gap and AI/ML tools can really scale operational tasks—things like scheduling, live chat, phone scripting, fraud monitoring, and yeah, even data analysis.
Using AI/ML tools for automation—the industry’s sweet spot:
Automation is a critical tool for scaling explosive growth that many companies yearn for. If you can save a few minutes here, a few minutes there, by alleviating those tasks, it adds up quickly. Now, jumping back to the beginning of our story–imagine taking your best data scientist, embedding their knowledge, their modeling capabilities, and their analysis skills into one of these tools. This tool can connect to the platforms your teams uses on a daily basis and conduct that analysis in real time, automatically notifying executives, managers, and employees of interesting insights within their data, pushing recommendations and guidance on how to get ahead of the red flags.
It doesn’t have to stop there either. Stories hidden within that vast amount of data may suggest your employees are working too much and at risk of burning out. Those same stories hold the key to identifying and solving critical cross-functional workflow bottlenecks, understanding which external clients and partners are over-taxing your internal resources, and the narrative of how work is getting done within your organization.
That is the mission we embarked on when we launched Peoplelogic.ai. Our goal was, and continues to be, to provide a comprehensive mission control for teams. By connecting to tools like Slack, Zoom, Office 365, and tons of others, we can help scale data expertise, democratize data, and empower each and every one of your colleagues to make the best possible decision they can. We provide the right insight at the right time, coupled with prescriptive guidance to ensure that your team continues to hit their goals, and that your company’s growth is just as scalable as you dreamed.
If you think this sounds too good to be true, why not give it a shot? With Peoplelogic, you can get started with a free 30-day trial. We look forward to being your partners in growth.
Peoplelogic for Your Team
Peoplelogic uses advanced analytics and technology to unlock often hidden quantitative insights into your organization’s engagement levels and health. By providing actionable recommendations to drive operational efficiency, Peoplelogic helps leaders understand the interconnectivity of their people and processes by leveraging output from the work tools used every single day. Combining these insights with the qualitative data and contextual understanding of your team can be the winning recipe for your business.
Book A Demo With Us Today
Slack Analytics — So Much More Than Messages Sent
Slack Analytics — So Much More Than Messages Sent At Peoplelogic, we’re big believers that the data being generated in the
Oct 13, 2020
Individualized Management: What it is and Why it’s Important
If you’re a people manager and expect a one-size-fits-all model to elevate, support, and drive your team beyond their goals—stop
Jan 19, 2021
Calculating Time to Close — Salesforce, HubSpot, Zoho, and Peoplelogic
Calculating time to close is a vital statistic when optimizing a sales team. There are so many factors that can
Mar 2, 2021