In early 2013, American Management Association and i4cp undertook a study of analytics needs in the workforce. The survey garnered responses from 789 businesspeople representing a variety of industries (50-plus industries), global locations (40-plus countries), and organizational structures and sizes (1-10,000-plus employees). The study findings pointed to an urgent call for analytics talent across organizations. Only one-quarter of all companies in this study felt they were equipped to meet today’s analytics needs, but top-performing companies are not content to play catch-up. Instead, according to the study findings, companies are building analytics capabilities mostly through training, but also through recruitment.
Two of the reasons companies said that they need greater analytical skills are a massive influx of data and less expensive but more powerful technology that will enable a future increase in the use of Big Data. Organizations with fully developed analytics skills—the ability to organize, analyze, and communicate data that can be applied to their human capital and not just to the other elements of their businesses—will continue to be the top performers in the years to come.
Among the study findings were the following:
1. Analytics are important today and will be even more important in the future. Overall, 58% of company leaders say analytics are important to their organizations now, and 82% say they will be important in five years. Less than 1% of companies say analytics will not be important to their business in five years, an unambiguous indication that analytics will be a ubiquitous part of the business world by the end of the decade.
2. Competitive and performance pressures are driving the need for better analytics. The greatest need for analytical skills in an organization were found to be accountability for results (67.o0%), competitive environment (61.6%), complexity of business environment (52.6%), increase in customer data (51.3%), and risk management (50.7%). So the study suggests that competition, adaptability, and risk prediction—offensive and defensive strategies are both improved by analytics.
3. Technology, data, leadership, and skills enable analytics. Technology, data, leadership, and skills are viewed predominantly as factors that can enable the development of an analytical organization, whereas culture and resources are necessities may inhibit the process. In other words, having an accepting culture and adequate resources won’t necessarily help you become more analytical, but having the right technology, leadership, and so forth, can. Resources and culture are necessities, but not a springboard.
4. Analytical acumen is highest among leaders and managers, members of R&D, the executive team, and Gen Xers. Companies are better positioned for overall market success when they use data, analytics, statistics, and probability to define and solve problems. That mindset has to come from the top to set direction for the company and to support analytical frameworks for understanding and making strategic decisions. Because of this, strong analytical acumen and support among senior leaders is definitely a plus for building an organization’s capabilities to use data effectively.
5. Most companies are looking to build analytical skills through training. Companies agree that training for analytics is better than hiring for it. Overall, more than twice as many organizations report that they would be training rather than hiring for analytics skills, 47% compared to 17%, respectively. A 2011 McKinsey report also offered some eyebrow-raising predictions about the coming data skills shortage. It suggested a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and use the results of the analysis of Big Data effectively. “The United States—and other economies facing similar shortages—cannot fill this gap simply by changing graduate requirements and waiting for people to graduate with more skills or by importing talent (although these could be important actions to take). It will be necessary to retrain a significant amount of the talent in place; fortunately, this level of training does not require years of dedicated study.”
6. Training for analytical abilities is most often focused on learning why, not how. Training for analytical abilities is most often accomplished through mentoring, team-based training, and self-study. Traditional, top-down training is also utilized, but to a lesser degree. The emphasis on organic or unstructured learning strengthened the argument that the most important skill for successful analytical ability includes mindset and methods, rather than any specific software or mathematical skill. For successful data analysts, the analytical tools used are secondary to learning to approach problems in an analytical way. This type of learning is more common among mentoring and peer-learning, no matter the level of the worker.
7. The top five analytical skills today and in the future involve interpreting and using data. Critical and analytical thinking, problem solving, data analysis (drawing conclusions), communicating and presenting findings, and decision making were the specific skills identified. The bottom skills included data prep (business math, data manipulation, Excel, other tools), contextual thinking, and visualization and visual analytics. In other words, the key skills were those associated with making decisions, not making spreadsheets.
8. The most valuable benefit of Big Data is better decision making. Better strategic decision making was found to be the leading reason for tapping into Big Data. Many saw the promise of analytics for enhancing decision making and have identified the need to increase analytical skills and capabilities to support this.
9. Corporations see the business and talent-related benefits of Big Data. Overall, more than 50% of companies see Big Data as helping improve strategic workforce planning, create more efficient and targeted marketing, and increase sales and profitability, customer satisfaction, and productivity. In other words, departments across the board—and the executive team—see the need for Big Data.
10. Turning data into insight is by far the biggest challenge of Big Data. The greatest concerns associated with Big Data were the ability to make use of the data/turning data into information (57.8%), access/database management (15.5%), security (10.3%), privacy issues (9.2%), legal issues (4.4%), and storage (2.8%). In other words, the roadblock to successful implementation of the use of Big Data may be the dearth of analytical skills.
The study’s results came up with four recommendations:
1. Identify analytical needs in organizations. Determine where the deficiencies are by surveying or assessing employees to determine their analytic strengths by segment.
2. Build analytical strengths by training needed employees in analytical abilities, increasing analytical skills with strategic hiring, and importing abilities from other areas of the business.
3. Prepare for the advent of Big Data by identifying deficiencies in technology for processing large data streams and by making sure that your enterprisewide systems can store the amount of data needed, which can be easily retrieved, and can collect and protect all data, keeping even irrelevant data available for future use.
4. Embrace the analytical decision-making mindset by evaluating leaders on their ability to make decisions with data and use data as an explicit part of leadership evaluations and hiring practices. Moreover, use statistical measures to rate leader’s abilities and creating an index of leadership ability based on the team performance of their leader.
The gap between where organizations say they are in terms of analytical abilities and where they would like to be is great (i4cp, 2012). The next step in taming Big Data is closing the distance by drilling down to where those specific deficiencies are in different workforce segments. It is not sufficient to have analytical ability sequestered within traditional, number-crunching departments. Analytical ability must be strengthened throughout the organization, especially among human resources personnel and within the executive team.
Also, whereas increasing an organization’s analytical capabilities can be done through hiring, the dearth of qualified data scientists, especially in the human capital field, indicates that training existing staff is the more effective method. However, strengthening analytical ability requires more than just math classes. To be truly effective, a broad understanding of finance, operations, and marketing must be combined with statistical analysis, presentation skills, and a focus on problem solving.
This study comes from American Management Association, a world leader in professional development and performance-based learning solution, and the Institute for Corporate Productivity (i4cp), the world’s largest vendor-free network of corporations focused on building and sustaining highly productive, high-performance organizations.