If you’ve tuned into the news recently, you can’t miss the headlines for the “Great Resignation.” Along with COVID-19, it’s an unprecedented reality. Companies must address large volumes of voluntary turnover and, in many cases, lose their most prized asset, top talent. The result is a seemingly never-ending recovery cycle from losses that stem from low global pandemic revenues and institutional knowledge as top talent walks out the door.
People have long studied theories to retain top talent and recruit for the perfect fit, but how do companies know if their approaches are genuinely successful? Many leaders have historically run with their “gut,” identifying a strategy without measurable results. But in a world of unprecedented firsts, the “gut” no longer applies. Employers must promote mental well-being, lead with empathy, and develop people more aggressively than ever—and they must visually prove their success to attract new talent. According to an article published in Forbes, 69% of employees feel they should be measured on results rather than hours worked. Yet, many organizations still measure results based on who worked the most or vague factors such as “strong problem-solving skills” or “strong communication skills.”
Companies must turn to the inevitable truth to put new programs in place for productivity and employee development. The status quo is no longer acceptable. The modern employee wants a fair, results-based methodology to measure productivity in a supportive, flexible environment. And data literate companies will come out on top. Data literate companies can operate efficiently in a remote environment and prove results through data. They properly define metrics for productivity, carefully communicate them, and then successfully track, consume, and report the results back to the organization. And if they’re good, they can continuously improve outcomes over time.
When we work with clients, Personify often finds recruitment and retention metrics are not highly sophisticated, even in the most elite of organizations. Hiring managers are often left to operate on “feelings” rather than quantifiable results, and that’s when companies begin to scramble. Most of us understand that data is our future, and it’s not going away. However, according to an article in the Harvard Business Review, most companies still struggle to build data literacy. Only 25% of workers feel confident in their data skills. Given the digital demands of our new workforce, this number is astounding and scary.
So how do we build a data literate organization that properly measures results? I think Rasheed Sabar, co-founder, and co-CEO of Correlation One, has some great suggestions in his article titled “How Data Literate Is Your Company?”
Here are five strategies Sabar recommends:
- Make data literacy an organization-wide priority, not just among people within the technology organization.
- Develop a common internal language for speaking about data, how it intersects with your business and industry, and how it changes specific roles at your company.
- Create spaces within your organization for workers to connect business concepts and data concepts.
- Create incentive structures to reward data-driven decision-making.
- Deploy L&D programs that teach data literacy in the context of your business problems—and that engage your employees.
With the changing environment of our near post-pandemic world, HR Professionals need data skills. Data skills empower organizations to attract and retain highly engaged employees who feel trusted to balance work with life. Gone are the days of “feeling” like recruiting and development is working and here to stay, whether we like it or not, are the days of accountability in a data-infused world.
What’s our advice? “Moneyball” your organization by dropping the status quo and digging into the dirt. Identify your data, consume it, and use it to your advantage. Give your employees what they’ve asked for–measure progress and productivity on facts. But most of all, don’t get left behind. The data is there; you just need to use it.
To see some of the top strategies we use at Personify to gain data literacy among our organization, check out this article written by Donna Horowitz, Personify’s Head of R&D and Quality.
See Harvard Business Review’s article “How Data Literate Is Your Company?” below.
We have all read stories of facial recognition software that fails to recognize dark-skinned faces, or robo-loan officers that deny mortgages to certain groups. As a growing body of research has made clear, algorithms created by non-representative groups have resulted in AI that perpetuates the inequities already prevalent in our society. As more companies rely more heavily on data and AI, these problems of algorithmic discrimination may only become worse.
Most companies know this by now. What they’re trying to figure out is: how can they avoid becoming yet another bad example?
The short answer is, thinking critically about the data you’re collecting and how you’re using it needs to be everyone’s job. Expanding the circle of who is in the room helping to question, build, and monitor algorithms is the only way that we will develop responsible AI. Doing that work requires data literacy — the ability to parse and organize complex data, interpret and summarize information, develop predictions, or appreciate the ethical implications of algorithms. Like math, it can be learned in beginner and advanced modes, spans multiple disciplines, and is often more practical than academic.
Building up data literacy in an organization can also help diversify the data teams who are at the forefront of making critical decisions about how data will be collected, processed, and deployed. The importance of diverse data teams is something I learned firsthand over more than a decade as a quant fund manager. It’s a commonly held belief that more diverse portfolios outperform because they reduce risk. But it is analogously true that diverse teams outperform because they reduce the risk of groupthink. By investing in data literacy across the enterprise, businesses can bring more divergent and creative perspectives to bear on both mitigating the risk of algorithmic bias — and identifying other efficiencies and opportunities that data can often reveal.
But a look at the data tells us that most companies are still struggling to build data literacy. Ninety percent of business leaders cite data literacy as key to company success, but only 25% of workers feel confident in their data skills. Not only that, but some estimates suggest that nearly nine in 10 data science professionals are white, and just 18% are women. Research from General Assembly indicates that when it comes to diversity, data science lags behind even other tech-oriented disciplines, like digital marketing and user experience design.
Why, despite the obvious need and increasing urgency, are we not teaching data literacy systematically and at scale? That’s the question that has animated my work for the past several years. At Correlation One, which I co-founded after leaving my fund in 2018, my team works with financial services firms and Fortune 500 companies to build more inclusive pipelines of data science talent. By helping employers from Target to Johnson & Johnson to the Government of Colombia assess the capabilities of their current workforce, and providing free training to aspiring data scientists (like our partnership with SoftBank and the city of Miami), we’ve gotten a front-row seat to better understand the urgent need for a more data-literate workforce, and helped companies put specific practices in place to make that goal a reality.
Here are some of the strategies we use.
Make data literacy an organization-wide priority, not just among people within the technology org.
Data literacy is not a technical skill. It is a professional skill. Encourage all of your employees — marketers, sales professionals, operations personnel, product managers, etc. — to develop their data literacy through quarterly engagement sessions that you host, where you cover topics like data-driven decision making, the art of the possible in AI, how data connects to your business, ethics & AI, or how to communicate using data. This kind of organization-wide emphasis is the basis for a transformation to a data-first culture.
Develop an internal common language for speaking about data, how it intersects with your business and industry, and how it is changing specific roles at your company.
The world of data is big, filled with buzzwords and misunderstanding. Develop a view as an organization which components of data literacy matter most to your organization — if you are a financial services firm, it may be probability and risk measurement; if you are a technology firm, it may be experimentation and visualization. In your L&D sessions, develop learning content that uses this language and demonstrates how it connects to your business in multiple departments, so employees can connect all the dots between data literacy and their workflows.
Create spaces within your organization for workers to connect business concepts and data concepts.
One thing we recommend to all of Correlation One’s clients is to empower employees to generate new business ideas that apply their data literacy. For example, suppose your company is in the music industry. As part of your L&D program, have employees develop project proposals that leverage their newfound understanding of data literacy — combining it with the knowledge they have of the industry, they will generate surprising new ideas for cost savings or revenue generation. Just as importantly, you will be empowering them to drive a new data-first culture from the bottom up.
Create incentive structures to reward data-driven decision making.
Take your current process for approving ideas or setting budgets. Then add mechanisms that reward data-driven thinking. For example, require managers to include clean visualizations in their proposals, or to build dashboards that track their KPIs quantitatively and in real time. If you can shift your managers’ decision-making from intuition to data by granting faster project approvals or larger budgets for proposals made using data-driven thinking, you will quickly get the behavior you seek from your managers through incentive alignment.
Deploy L&D programs that teach data literacy in the context of your business problems — and that actually engage your employees.
Subscriptions to education and training platforms like Coursera often fall flat in organizations that are looking for lasting transformation. That is because learning is much more effective when it is social (done with others), personalized (done with expert feedback), and contextual (connected directly to the business problems you are solving). Developing these personalized, social, contextual learning programs is more resource intensive, but the benefits in terms of employee engagement with the material, employee retention of the material, and empowering your employees is worth it.
Perhaps most importantly, my experience both before and during Correlation One has helped me understand that data is not a vertical — it is not just one job family, like a data scientist or data engineer. Instead, data is a horizontal — it is a skillset that cuts across a growing number of jobs in every field. A marketer is a better marketer with data skills. A product manager is a better product manager with data skills. And so on for operations, engineering, sales, and even HR. Not everyone needs to know how to code. But soon everyone will need data literacy.
Ultimately, data literacy is about much more than machine learning and data science. And it’s about more than AI. Data literacy is simply about humans coping better in a data-infused world — which is why we need it now more than ever.
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