Today, most business decisions are backed up by data, but its practical applications go beyond pure analytics and long-term business strategizing. In fact, workplace digitalization and access to data affect businesses of different sizes on practically all levels, from hiring new employees and building new partnerships to searching for the most effective ways of positioning a brand or promoting a product. Today, everything is about data, truly. Below are just some of the most notable workplace shifts already made obvious by global access to data, big and small.
Identifying Relevant Skills
According to the World Economic Forum, around 23% of all jobs will change drastically by 2028, which, in turn, will mean a totally new set of in-demand skills. Even now, AI advances and the growing automation of many workplace processes have made many routine skills obsolete, and the process is still ongoing.
Leveraging data to make informed predictions about professional skills required in the future can help companies up-skill and re-skill their existing staff, which is a wiser and more cost-effective alternative than constantly hiring new talent. Since technology's pace will keep increasing, the speed at which acquired skills get obsolete will also accelerate, which means employee upskilling and reskilling are likely to become a professional standard. So far, the only challenge is anticipating which skills to prioritize, and that's exactly where data analysis comes in.
Currently, LinkedIn and other analytical platforms predict a paradigm shift in the sought-after workplace skills, with AI and data analytics in the lead. However, deep technical knowledge of machine learning algorithms won't become a must-have skill for every occupation out there. However, general knowledge of generative AI is already essential in many professions, and the dependence on AI for data analysis will only keep growing.
Besides, the need for continuous learning and upskilling prioritizes a set of soft skills, including adaptability and analytical thinking. Creativity should also gain more spotlight in the years to come because, as far as we know it, creative thinking is something machines cannot tackle for us. As businesses keep investing in automation to handle routine, repetitive tasks, employees will have more time to focus on the creative aspects of their jobs. This, however, requires creative thinking to begin with—or, at the very least, an open mind.
Reshaping Work Ethics Through Data
Today, data-driven HR is a way to ensure better cultural fit, employee performance, and retention. Very similar approaches apply to recruiting as today's automation and a focus on data give recruiters a chance to find culturally suitable candidates for available positions while eliminating the hiring bias.
More specifically, today's applicant tracking systems (ATS) and candidate relationship management (CRM) software rely on AI algorithms and data analysis to pick the best-fitted candidates for every job opening. They analyze vast amounts of data and even tune into the candidate's speech patterns to form a picture and score job applicants by multiple parameters, including personal characteristics.
Of course, the system is not without its flaws right now, but semi-automated candidate screening is already common. Besides, contact lookup databases like SignalHire allow recruiters to save time and effort while reaching out to passive candidates, who make up 70% of the global workforce. And even though they are not actively looking for a job, they are prepared to consider an enticing offer—provided it is tailored to their professional goals and interests.
Once again, that's where data analysis and human creativity must work together if recruiters are determined to attract the best human talent with the right skills, experiences, and mindsets. While contact lookup databases are essential in collecting data and presenting it in one neat package, it's up to the recruiter to make a job pitch that could make employed professionals reconsider their current place of work. SignalHire, for example, presents data on current employment and past years of experience, along with verified emails, phone numbers, links to social media accounts, and even personal blogs. All of that is enough to make an informed decision about the candidate's cultural fit and professional aspirations—by integrating the data into an AI-powered CRM or using the good old-fashioned human reasoning.
Finding Suitable Employers and Partners
Even though data-driven recruiting is more common, this street goes both ways, as today's job seekers can find out a lot about the company and its existing employers through the same contact database recruiters use to find the best-suited candidates for open vacancies. Based on different estimates, over 70% of candidates research a company before applying, and roughly half of them get very thorough with the research by analyzing different sources.
For job applicants, easier access to data means higher odds of landing a job that appeals to their personality and career expectations. For employers, qualified hires who are a good cultural fit have a chance at steady corporate growth, higher employee retention, and overall job satisfaction, which directly translates into team productivity.
However, this also emphasizes the need to invest in positive candidate experiences and build an authentic brand image online. Right now, GenZ is on its way to becoming the dominant workforce, and this generation is well-known for its focus on corporate values and job meaning. So, paradoxical as it may seem at first, our reliance on data brings less tangible job parameters to light.
Employers who want to attract the best human talent and invest in continuous business must apply a more humane approach to their branding, emphasizing meaning, diversity, and work flexibility whenever possible.
Making Strategic Business Decisions
Even though business reliance on data has already transformed corporate HR management, the primary application of data analytics is still long-term planning. Depending on company specifics, this could imply a diverse range of activities, including staff management. Other notable applications reshaping the future of work include:
- Marketing, including the analysis of customer behavior and creating personalized outreach campaigns, both in B2C and B2B sphere;
- Analyzing operational efficiency and searching for ways to optimize it further;
- Investing in business sustainability through adopting green initiatives, reducing waste, and minimizing carbon imprint;
- Deciding on long-term investments through factual analysis and data-driven market predictions;
- Improving products and services through analyzing customer feedback, running competitor checks, and relying on advanced business intelligence.
Impressive as these applications are, even they barely scratch the surface because industry-specific applications of big data analysis and machine learning are practically limitless and will largely depend on company goals and its staff's creativity in leveraging the innovative tools. After all, that's what AI-powered data analytics is about—a tool to help us process large amounts of information and structure it, even if there are few patterns to begin with.
At the same time, like any other tool, machine analytics and corporate dependence on data have a downside, too. Today, when the market is still in full swing of digital transformation, mass adoption of AI causes insecurity among many workers, especially low-skilled ones.
Another important challenge we will need to address is cybersecurity, which remains a major corporate threat even today. As more businesses rely on machines for data collection, analysis, and storage, security leaks would pose an even greater danger because losing all of that data would render information-dependent businesses non-operational—for days, if not forever. On the bright side, since data algorithms are growing ever more advanced, they might soon suggest an effective approach to boosting cybersecurity, too.
