Introduction 

With the increasing use of technology in the business world, traditional business roles in the field of marketing, administration, customer support, etc. are losing their original value. Although these business roles are essential for the existence of the business world, other roles such as those of a Data Analyst, Data Engineer, Data Scientist, etc. are becoming the need of the hour. In this technological era, every flourishing business is making use of the latest technology to beat its competitors and cater to the growing needs of its customers/clients. Companies have started looking for professionals with data analytical skills who can help solve real-world business issues. Data analytics is a very wide field and finding the right talent is not that easy. Thus, hiring talent in the data analytics space is a trick to ask for skills and traits, which people in traditional business roles do not possess. Let us discuss below the need for and importance of data analytics in the new business world.    

Need of data analytics in businesses  

Artificial Intelligence (AI) and the Internet of Things (IoT) are generating overwhelming sums of data, but that does not yield very good results if organisations don’t use it effectively. Therefore the tech industry is increasingly seeking employees in the field of data analytics. With every passing year, the number of jobs posted by tech companies requiring analytical skills is increasing in comparison to the requirement of jobs in engineering, marketing and administration. The data analytics life cycle has undergone a lot of learning and corrections in the past two decades. Earlier, a data scientist was considered a unicorn with a magic wand who could solve all business problems. However, this myth has been busted over time. Besides the traditional roles, even the roles in the data analytics space are diversified from a Data Engineer, Data Architect, ML Engineer, BI Engineer, Business Analyst to a Full Stack Data Scientist. Talking about the bigger roles in the analytics field, the key challenge for an analytics manager is to decipher what the promoter or the client or the consumer needs on the skills and capabilities side, and document it as a ‘Job Description’ to look for candidates.  

With the demand for data analytical skills outstripping the supply, there is surely a shortage of genuine talent in this field. It is a field that requires deep expertise and hands-on focus, even at a CDO or a CAO level. This signifies that the field is rapidly evolving, and general strategy will not work here. Hence, companies need to go with all guns in the hiring market to find the needle in the haystack. Although there are thousands of jobs on portals open for months, it takes time for companies to find the right talent. Clearly, there is a ‘skill and knowledge gap’. 

What is the optimised strategy to hire?    

A company filling the job roles in data analytics has two options: either to fill the role by creating a new talent pool or elevating current employees’ skill levels on specific data and analysis fields. When employees gain experience in the field of data analytics, their role and contribution in the company increase, both technically and functionally, which is relative to their salary growth as well. The war between the new talent pool versus the experienced talent is too fierce on this battlefield. Hence, different strategies need to be applied. There is no general guidebook for winning, but the employer must use talent building (for freshers) and transformation models (for laterals) for growth and sustainability of the business as hiring on the right time is essential to avoid the risk of revenue loss. These models need to undergo a lot of trial and error for key roles in an organisation at a skill level. Nurturing is the best method to create the talent pool, but it needs investment, patience with a long-term vision and leadership commitment.  

Importance of hiring talent in the data analytics space 

It is extremely fruitful to have data analytics professionals in the company. The use of data analytics helps to create comprehensive customer profiles from the customer data available with the company. Businesses can gain customer behaviour insights to create a more personalised experience. The use of data analytics also assists top and middle-level management in decision-making and minimising financial losses. Where predictive analytics suggests the consequences of bringing changes in the business, prescriptive analytics indicates how the business should cope with these challenges. Data analytics even help organisations in improving operational efficiency by streamlining operations. It tells where production delays or bottlenecks originate from and even predicts where future problems may arise from. Risk is inevitable in business and is found everywhere. For instance, investing in new projects, legal liability, uncollected receivable, etc. Data analytics helps organisations in understanding risks associated with activities and preventive steps that can be taken to mitigate the risk.  

Conclusion 

A high-skill data scientist is capable to outperform any traditional business role in this era. The importance of data analytics is discernible in the new business world. The tremendous growth of data analytics is lucrative, but it needs the tricks of the trade to get the value from the talent pool. 

Source: https://www.cxooutlook.com/hiring-talent-in-the-data-analytics-space-is-a-trick-asking-traits/