In this weeks Five Question Blitz we focus on hiring Data & Analytic Professionals. Hiring resources in pursuit of Data-Driven excellence requires a clear analytic plan and a constant evaluation of related goals and outcomes.
The Five Question Blitz was created to answer five questions relevant to Data & Analytics. Topics will be broad and answers will be simplified. Our goal is to promote common definitions and increase the general knowledge of individuals with interest in Data & Analytics.
Data & Analytic Professional is a broad term that captures all disciplines within Data Science & Analytics. The core elements of the analytic process are required for each type of role. These roles can include data engineers, data architects, data analysts, business analysts, business intelligence analysts, operational analysts, process analysts, modelers, machine learning engineers, data scientists, etc.
Organizations looking to hire Data & Analytic Professionals must consider five critical factors. These include:
1) Leadership Expectations – There must be consensus amongst an organization’s executive team to the benefits Data & Analytics can deliver. Consensus is important due to the considerable time and cost required before returns are realized.
2) Analytic Target – Organizations must understand their operational model at all levels including decision makers, customers, products, and engagement. Insight to this degree forms a comprehensive road map of decision points in relation to each other and to the business overall. This in turn provides the necessary information to build an analytic support model.
3) Information Support Model – Data & Analytic Professionals benefit heavily from a fully developed information system. Organizations need to evaluate the status of their databases, data governance, common definitions, data quality, ELT processes, analytic tools, business intelligence tools, etc. These processes contribute heavily to the quality and efficiency of the analytic support model.
4) Resource Alignment – Resources are closely aligned to the analytic support model and the health of information systems. The analytic support model dictates what systems are required and the health of those systems contributes to the models execution. Analytic resources should be prescribed per the health and progress of this relationship.
5) Budget Allocation – Whether building an analytics group from scratch or enhancing an existing one the cost can be high. There is a wide range in salary for analytic roles and the tools of the trade are costly. Organizations need to trust their analytic support model and identify the associated costs.
In the previous questions resource alignment bullet we discussed the process of identifying where analytic resource opportunities exist. The type of Data & Analytic professional is dependent on an analysis of that opportunity. As an example, if an organization is challenged by data inconsistencies and disparate data sources then a Data Engineer should be a strong consideration. Another example could be if an organization has completed a number of analysis to determine overall KPI’s and department specific performance indicators than the time might be right for a Business Intelligence Analyst.
Data & Analytic job descriptions contain too many buzzwords and too many general analytic terms. Job descriptions for junior level positions look similar to senior level positions and Data Analyst roles contain many of the same skills as Data Science positions. Organizations must be clear in defining the type of position, where it fits in the analytic support model, and the necessary skills & qualifications.
The tools for hiring Data & Analytic Professionals are not dissimilar from those used for any other non-analytic position. Common tools include job boards, recruiters, assessments, and relationships with educational programs (i.e. internships, work study, labs, etc.). Beyond these tools there are the tried and true resumes, phone screens, and interviews. Unfortunately, none of these methods is perfect in determining a candidates actual capabilities. The closest is likely internships.
The methods yielding highest confidence is selecting candidates with whom there’s been prior shared work experience or the recommendations from trusted colleagues. However, I will take this opportunity to promote SimDnA’s TradeCraft product as a new and innovative method of confirming analytic skills and experience.
About the Author: My name is Ion King and I am the Chief Executive Officer at SimDnA. My focus is on helping others passionate about growing careers in Data Science & Analytics achieve their goals. Connect with me on LinkedIn or find more of my articles on medium