We are living in a world where the reality of a tidal wave of big data is upon us. And the force as well as the layers propelling this sea of data are enormous. Every transaction, operational task, discussion and indeed decision in the business world is increasingly finding its roots in some snippet of data sprouting in the course of business activities. Amidst this ever-growing dependence on data analysis, machine learning and cognitive business intelligence, what must a candidate vying for a fulfilling job-role in the field possess in terms of capabilities?
At the outset, one critical requirement is to have a curious outlook when approaching operational projects and interacting with business data. Beyond the technical competence, an individual should feel intensely motivated to understand organization-wide process challenges and then overcome them through building, maintaining and improving data-enabled platforms and tools. A hyper-connected business environment has necessitated the shift of data analysis from collection and access to generating multiple inferences that are not only highly relevant but also those that occur in real-time. In fact, businesses are investing resources to develop decision frameworks that supplement real-time inferences with pre-emptive analysis.
The buzz around artificial intelligence and big data is gaining genuine acceptance from companies mainly because businesses have understood that it is no longer a fashionable trend to latch onto, rather a vital asset to drive strategies and truly influence bottom lines. Big data is big only if the insights it produces indicate disruptive business impact.
With unprecedented VC focus towards the AI-start-up landscape signifying a marked spike in the number of M&A and private equity deals in the sector, the overall investment in artificial intelligence across industries is expected to be greater than 300%. Naturally this investment-focus signals an incredible shift in future recruitment trends as well, with candidates possessing the apt mix of technical and academic competencies poised to strike an edge above their peers. A recent study undertaken by Glassdoor highlights the popularity of such talent with ‘Data Scientist’ and ‘Data Engineer’ job roles, featuring in the top 3 ‘best jobs in America’. However, recruitment experts express a niggling worry in relation to the ever-widening gap in the supply and demand for data science and machine learning researchers. Companies, especially tech-start-ups, are banking on the technical know-how of these set of individuals than the heftiness of their work experience. This odd job-market situation is inducing companies to devise innovative solutions to get to the right labor-pool. Greylock Partners, an early stage venture firm, with tech giants like Facebook and Linkedin in its portfolio is reported to have formed its own specialized recruitment team to fulfill the talent needs of its clients.
Here are a few in-demand job roles that are predicted to climb even higher on the popularity measure:
Data Scientist: As per this HBR article , titled ‘Data Scientist: the sexiest job of the 21st century’, a “data scientist” is stated to be a ‘high-ranking professional with the training and curiosity to make discoveries in the world of big data’. It’s a job role that combines the expertise of analytics, statistics and programming. However, programming knowledge acts as the dominant stepping-stone to embark on a career in this role. With the dramatic advent of artificial intelligence on the business scene and the sheer volume, variety and velocity of data, companies are eyeing professionals who can provide answers of relevance and speed through automation and data-embedded platforms. A candidate with the requisite academic credentials-a Master’s or a PHD in machine learning, statistics or data science along with a deep practical understanding of –Hadoop, Python, Java, R as well as SQL can look to make great strides in the field of data science.
Such credentials, however, do not compensate for the critical requirement of domain-specific knowledge. Data scientists are expected to utilize company platforms and BI tools to not only gather the most accurate and relevant pieces of information, develop algorithms that support automation for, say, pattern-recognition but also use their programming skills to enable communication of pertinent and actionable insights for the benefit of all stakeholders. Naturally, some stakeholders may not be comfortable with data automation, yet would look to rely on the unique analytical value entrenched in the database infrastructure built by the company’s data scientists.
Certifications like Certified Analytics Professional (CAP), EMC: Data Science Associate (EMCDSA), SAS Certified Predictive Modeler and Cloudera Certified Professional: Data Scientist (CCP-DS) can prove really handy in the long-run in this field.
Data Engineer: The primary responsibility of a data engineer is to conceptualize and implement the database infrastructure of the organization. In addition, they are expected to actively participate in the management of data with the objective of supporting data extraction, testing and analysis by business analysts. With collaborative analytics and cloud technology presenting effective and alternative solutions to data processing, data engineers would be expected to have a stronger grasp on skill sets relating to designing, building and managing databases/applications in the cloud. An in-depth understanding of Hadoop, Apache Spark, SparkSQL would be helpful to gain expertise in this role. The emergence of cloud service providers like Amazon (Redshift), Microsoft (Azure) and Rackspace has enabled accelerated data sharing and processing, in turn pushing the development of cloud-based applications for fraud-detection, face recognition and medical diagnosis among many others. Skilled professionals who have their thumb on the pulse of these emerging technological trends would be able to create an impression in this role.
Business/Data Analysts: These are professionals entrusted with the task of capturing, accessing and investigating data by exploring disparate GUI-based dashboards as well as reporting and other enterprise-specific BI tools connected to both relational and non-relational databases. They are expected to be adept at extracting data from where it resides to perform gap analysis, requirements estimation, testing as well software-specific documentation. They are supposed to exhibit a great sense of teamwork as this role entails interacting with software developers, data architects, data scientists, UI/UX developers as well as product analysts. Proficiency in statistical modeling techniques like SAS, R and Excel modeling would prove useful here.
With increasing democratization of data delivering unique value in terms of insight for developers and the end-users and everyone else in between, the afore mentioned job roles would continue to get the eyeballs they are predicted to. Information produced and proliferated through sensors embedded in host of devices presents a great opportunity to skilled data-specialists to hop onto the ‘internet of things’ bandwagon and let their special knowledge make a special data impression-in a big way!3