Are you ready to be a part of the Analytics world?
“Elementary, my dear Watson”.
You instantly recognize this famous phrase from the world’s most famous detective, Sherlock Holmes. His deductions were based on keenly honed cognitive skills of reasoning and interpretation. His professional success was a result of the skilled evaluation of available data, selection of a suitable strategy and drawing of logical conclusions.
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 are beneficial for 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.
According to a 2015 MIT Sloan Management Review, 40 per cent of the companies surveyed were struggling to find and retain numerical data interpreters. How can recruiters ensure that candidates have these cognitive skill sets even before they invest time in interviewing? Can they be supported during the crucial tasks of sourcing by technology and tools that can evaluate such discrete skills and competencies? Can a candidate assessment tool be the answer to these questions?
Given the sheer number of candidate assessment tools, the answer seems to be a resounding yes. The innate desire to make the correct hiring choices is what has resulted in a variety of assessment tools that are proving invaluable to firms in their recruitment strategies. 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.
An assessment tool to evaluate critical job-related qualifications can be a boon. But in order to justify its cost, a company needs to ensure that the tool integrates with its business, is scientifically built, legally defensible and has the psychometric expertise to evaluate the data generated. All in all, a tool to help identify cognitive skills for the Big Data industry should mirror the very characteristics for which the field is known.