March 9, 2018

4 Strategies to Avoid Analytics Hiring Mistakes

Organizations are trying to reach a higher level of analytics maturity while trying to reduce costs and stay loyal to employees. There are ways to use existing employees with relevant experience and transform them into data professionals but substantial training and identifying right characteristics is key to success. Here are a few strategies that will help you stay away from the common hiring mistakes.

Solution #1 – Complete an Analytics Skills Assessment

The first thing to do before posting new job openings or using existing employees is to understand your current Analytics needs and skills gaps. A proper skills assessment of your analytics team should identify skills gaps, weaknesses, and lead to a thorough understanding of the capabilities of your team. A skills assessment allows you to determine if your staff is adequately trained and knowledgeable in the field of Big Data Analytics.

Solution #2 – Understand the real job requirements

We often find that although clients have people with titles such as Data Architect, Data Engineer, Data Analyst and Data Scientist, their skill sets do not align with the work requirements that are necessary to successfully implement and maintain an advanced analytics initiative.  You must know what is needed to succeed in each role and have the insights to evaluate your employee’s knowledge.

Here are some typical job roles, required skills, and responsibilities:

Job Title Tasks Skills
Data Architect
  • Data extraction
  • Production operations of the infrastructure to ensure performance and reliability
  • Scalability while managing costs
  • Knowledge about cloud solutions (Amazon Web Services or Microsoft Azure)
  • ETL tools
  • Database design
  • NoSQL for big data sets
Data Engineer
  • Initial data exploration
  • Moving, aggregating, and preparing data from the main storage repository
  • Ensure data availability for  SQL, ETL, Python and Rtools
  • Data organization
  • Testing data integrity
  • Ensuring quality and validity of data
  • Data acquisition to support the defined analytics use cases
  • Automation of data pipelines
Data Analyst
  • Deep dive data exploration
  • Data cleansing
  • Analyzing data to find correlations, trends, and patterns
  • Building reports and dashboards
  • Problem solving
  • Finding the right answers to the business questions
  • Tableau and R.
  • Excellent Communication skills
  • Delivery of highly technical analytics output
Data Scientist
  • Building and validating statistical models and machine learning algorithms
  • Creating predictive algorithms
  • Performing data quality analysis
  • Model tuning
  • Regression and classification modeling
  • Bayesian method
  • Markov process
  • Outlier detection

Solution #3 – Find the curious and adaptable

Hire good people, with good ethics, aligned skills and a thirst for curiosity and learning. With proper coaching, mentoring, training and collaboration, creating a solid internal data analytics team will take time and work, but if properly managed, will yield quality results.

Solution #4 – Find a great partner

A good partner will help you keep your focus on the business problems you want to solve, what insights you want to gain and ensure C-Level support. It’s best to take a very incremental approach when you start down this path and partnering with an outside firm, such as WebbMason Analytics, can help you transition into advanced analytics. A partner can make your transition smoother, provide initial wins while validating the strategy, and provide technical or data science support. Schedule a time to speak with us and get moving toward more wins with analytics.