Pearson TalentLens, a global leader in the talent assessment market, provides unique strategic value with its proprietary processes and assessment tools.  Its new product “Numerical Data Interpretation Test (NDIT) (a numerical reasoning test) is designed to measure an individual’s ability to interpret and manipulate data, which is required in most roles.

Today, companies have recognized the benefits of numerical data interpretation and its positive impact on their competitiveness and hence are on a constant lookout for these skills. They attract the workforce in hordes for positions that include finance professionals, business analysts, marketing managers, etc., who have data analytics as a crucial part of their job responsibilities. Therefore, for anyone interested in numerical data interpretation (employers, students, and data professionals), it is imperative to be skill ready. From an employer’s standpoint, it is about selecting the right candidate for the right position and from an applicant’s perspective; it is about developing a well-rounded skill set.

With top-notch salaries and enriching experiences, a career that involves data interpretation & analytics is viewed as an extremely attractive profession and in fact jobs in this stream are growing at a fast pace.  Firms have to be extra vigilant and understand the skill sets required clearly to avoid pitfalls in their recruitment processes and avoid staffing mission-centric positions with wrong recruits. Let’s review these skills sets briefly to understand what is required for a successful job fit:

  • Numerical Data Interpretation skill: This is the ability to interpret numerical data and derive meaningful conclusions that is beneficial for the companies. Spotting patterns, linking cause and effect will help in arriving at actionable plans.
  • Creativity: In an emerging field like data science, the ability to come up with new and innovative ways of using numerical data interpretation will be deemed a differentiator.
  • Mathematics and Science: Quantitative data in the form of numbers will need assessments so as to draw conclusions from them.
  • Computer Science: Familiarity with open source technologies is a must for a budding data scientist’s rounded skill set.
  • Business and Communication Skills: An organization’s grasp of business goals and objectives and the understanding of key performance indicators are absolutely essential in order to be truly productive. The skill to communicate the results arising from strategic action plans and provide insights to the management team will be of utmost importance.