February 23, 2018

The 5 Biggest Mistakes Analytics Hiring Managers Make

There are common mistakes that hiring managers make when hiring or re-assigning staff for data analytics positions. In some cases, they simply don’t understand what questions to ask or how to evaluate candidates properly. We detail some of the common mistakes in this post and provide solutions so you can avoid those same mistakes in our next post 4 Strategies to Avoid Analytics Hiring Mistakes.

Mistake #1 – Business Analyst ≠ Data Analyst

Business analysts excel at meeting with key business stakeholders, SME’s and related teams to gather requirements, write functional specifications, and create business process diagrams. They may analyze ERD diagrams but typically will not have experience with tools like Cloud-services such as AWS or Azure, Big Data Platforms such as Hadoop and Jethro, Data Access tools such as Athena or Hive, and Analysis and Visualization tools such as Tableau or R.

Mistake #2 – Skills Assessment

While someone can be skilled at performing ‘self-serve’ analytics using some of the commercially available end-user tools such as Business Objects or SAS, the skills needed to analyze, cleanse, transform and prepare data in the big data advanced analytics world are very different. A real data analyst, data engineer, or data scientist has a depth of skill that is more advanced than having used out-of-the-box analytics platforms.

Mistake #3 – Programmer ≠ Data Engineer or Data Analysts

While programmers are vital to an organization and excel at building business applications, they typically lack the analytic skills necessary to be a data analyst. Yes, they have significant working knowledge of SQL, however, they excel in writing SQL code and statements to build an application versus interrogating billions of rows in a single table to uncover data quality issues. Most programmers are not experienced in the art and science of asking questions of data or creating insights from data. Programmers are also not experienced in correlating unstructured data (images, texts, tweets) with structured data (data tables).

Mistake #4 – Not Comprehending the “Big” in Big Data

While 5TB of data may seem like a lot of data, let’s put this into perspective for a minute. The CERN supercollider in Switzerland generates 1 Petabyte of data daily. Social media, streaming, sensors and other types of IoT devices generate large amounts of data as well and are becoming more pervasive. Traditional database tools are not effective when working with datasets of this size. Further, the processing power required to perform any analysis on a data set of this size is well beyond the capabilities of almost any traditional data center.  Working in the Big Data space requires higher cognitive functioning due to the degree of abstract problem-solving skills required to make progress.

Mistake #5 – Hiring Data Geeks

Data geeks, while they love data, their focus is not on addressing real-world business issues and problems or finding ways to optimize resources. You need people that understand business, business problem, and opportunities and see a more holistic view of their role within the company.

Solution #1 – Find a Great Partner

Your ability to hire correctly and have the right people in analytics roles is vital to your analytics culture and success. We work with our clients to help them evaluate their team, develop the skills, and develop the team culture of contribution, commitment, and self-management. Let’s us provide you with a skills and culture assessment to help you identify the best way forward. Schedule a time to speak with us.

 

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