Data Analysts: Different Skills, Similar Process
- Adam Wealand
- April 24, 2019
The week of a data analyst can vary widely depending on the size of the firm and their unique combination of skills. Despite their differences, every analyst will tell you they spend too much time “prepping” data and not enough time in higher-level analytics and interpreting the results for business insights. The process can be broken down into six steps, and you’ll see that the majority of the time spent today is weighted in the beginning of the process rather than the end, that is evaluating data-driven insights and making decisions.
In short, analytics is fun, everything surrounding it can be a pain.
What is an “Analyst” Anyway?
Although some analytics skills span multiple data science categories, such as data analysis, each category is defined by a unique combination of skills. We will consider two general groupings of analysts. For example, Advanced Analysts (Data Scientists) must be familiar with sophisticated analytical methods and tools; hot topics such as Machine Learning and Hadoop quickly come to mind when thinking of these folks. Whereas Functional Analysts require more general data analysis and business intelligence skills that are aligned with a specific function in a business (finance, marketing, product, operations, etc.).
However, all analysts have two similarities to answer business questions in that (1) data are needed to produce insights and answers and (2) they follow a process of analysis.
That process involves 6 stages:
- Select an analysis method.
- Identify data sources.
- Prepare the data.
- Execute the analysis.
- Evaluate the results.
- Operationalize: Automate data preparation and execution of analysis, if the business question has to be answered more than once.
In many cases the process iterates stages three and four, between data preparation and the analysis itself. Often times the analysis results require more data from the data lake and thus, more preparation. Many times, evaluating and presenting the results to the business owners means going back to find more data (beware of analysis paralysis!). In any event, we all know data preparation is essential to the quality of the results.
The Solution For All Analysts
Within both Datameer X and Datameer Spectrum you have the agility to easily revisit any stage of the analysis process. In fact, it was designed to do this very thing – visualizations, deep analytics, and data preparation that are implemented in a single spreadsheet style environment.
Furthermore, it was created as one universal solution for your entire BI team. Your analysts and engineers are all trained in Datameer and collaborate in one environment, rather than a series of disjointed solutions where everyone speaks a different language. This provides not only the Functional Analysts, but also your Advanced Analysts, the freedom to quickly iterate on an analysis project and focus on designing models and evaluating insights, rather than searching for and cleansing the underlying data.
Easily Create A Virtuous Analytics-ML Cycle With No Code
Check out this white paper on how to create efficient machine learning workflows with no coding using Datameer X and Amazon SageMaker. We also show how the cooperative relationship between these two solutions – with data sent from Datameer X to SageMaker to build and test models, then the results of those tests and predictive output from the models fed back into Datameer for further exploration.