Data Analytics Skills Set Every Data Analysts Must Have

Every organization needs in order to run properly. For this reason, certain number of employees must have Data Analytics Skills to carry out the organization’s data needs such as storing, analyzing, translating and interpreting the data. There are many skills in the data analytics skill set that goes beyond a simplistic explanation. Below are some Data Analytics Skills that experts need to have.

  • Export, transform, and load (ETL): The data expert and data analysts needs to be able to perform export, transform, and load (ETL) processes. Simply put, they need to take data from one system and put it into another.In an EDW, a user pulls data from disparate systems (e.g., EHRS, finance, human resources) that don’t talk to one another. For example, you may have an EMR system, a patient satisfaction system, and a costing system that don’t interface directly. Making a copy of the data found in each of these systems and pulling the data into the warehouse will allow integration of data from the various systems. This movement of data is accomplished through the ETL process.
  • Data modeling: Data modeling is a fancy way to say that you write code that models real-world processes and workflows. For example, consider a common healthcare scenario: a hospital admission. What information do I need to capture to model that workflow? In this example, you’d need some demographic information, such as the patient’s name, date of birth, gender, and complete address. You’d likely want to pull insurance information, such as the plan name, co-pay amount, and effective coverage date. Clinically, you would want to know some history. Is this patient new to the system? Do we already have a medical record number for the patient (indicating we have seen her before)? What is the admitting diagnosis? Who is the attending provider for the admission? Did the patient come through the emergency department or some other venue? A good data model captures all of these data elements and relates them in a meaningful way to reflect the actual workflow.    
  • Data analysis: An analytics team member needs to be able to make sense of the data once it is in the EDW. There is so much information produced in healthcare, and not all of it is relevant for the analysis that needs to be done to drive improvements. A good analyst has the ability to sift through data to extract pertinent insights. This requires some complex thinking around set theory and the ability to do their analysis through SQL, a statistical reporting tool, or a combination thereof.Let’s give an example. In healthcare, there is a lot of attention to the management of diabetic patients. Diabetes is a chronic condition that affects the patient’s quality of life, and if not well managed, can be lethal. From a financial perspective, diabetes is extremely costly if mismanaged.
  • Structured query language: An analytics team member needs to be able to talk directly to and manipulate databases through structured query language (SQL). Recognizing there are various dialects of SQL, I refer generically to the ability to speak to and manipulate databases through code. He should be able to write SQL code without a dependency on an intermediary, guided interface (e.g., a drag and drop tool). Many workers rely on a tool like the Microsoft Access GUI interface or Crystal Reports GUI interface to generate SQL for their reports. In doing so, they attain a rudimentary understanding of querying.SQL offers users fine-grained control of the data being pulled. It also provides a powerful way to explore data that isn’t filtered through a predefined data set or model, as is the case with a business intelligence (BI) tool. Teams that can’t query the data with SQL are beholden to whatever information is being pushed to them from another source. Using a BI tool to generate SQL on your behalf is a good starting point.


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