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Advancing in a Data Analysis Career

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A data analyst can find many ways to practice and improve skills in requirements discovery, analysis, documentation, and approaches to the role.

Defining the role of a data analyst is the first step in understanding its varied activities.  A data analyst looks at data and produces information – hopefully actionable – running queries and reports, often using powerful and sophisticated tools.  The analyst can report to IT, can be part of a data management function, or report directly to a business sponsor.

Data Analyst Basic Actions

A business sponsor has given the data analyst requirements for a query or a report, and the report was produced, the query was executed and the results were delivered.  At this point, many data analysts will walk away and let the business sponsor decide what to do with the results.  Other data analysts take some additional and meaningful action to provide benefit to the business user first, by fully understanding the results before presenting them.

This analysis should be one of the tasks of the data analyst since they know how the data was transformed, filtered, aggregated and summarized in the ETL process to validate the results – and then identifying particular issues or caveats in the report or query that are relevant to the business sponsor.  For example, if some of the data is questionable, the analyst should identify the results as problematic, and indicate that the data has been accessed from a certified source.  A good data analyst should express how the results were validated.

However, something has been ignored.  The data analyst was given the requirements for the query or report but did they ask the business sponsor what he or she was looking for or what they were expecting to see?  Knowing this might change the approach or prevented the analyst from producing information that has no value to the business sponsor.

Did the analyst collaborate with the sponsor concerning the potential source data?  Knowing the sponsor’s assumptions about the source data could minimize the possibility of producing embarrassing results.  In the collaboration process, the analyst may suggest better and more extended data sources that could lead to information that is more relevant.

Identifying New Analytical Opportunities

Perhaps the data analyst is in a position to identify new opportunities for information which would normally mean executing additional reports or additional queries.  There are two options here for taking action:

  1. Run these queries without first notifying the business and then presenting the results if they appear to have interest or value.  This has the benefit of the business sponsor being pleasantly surprised by the analyst’s initiative.
  2. Meet with the business sponsor and explore query options.  This approach places in the analyst in a partnership position with the business sponsor and gives the analyst additional insights into the business sponsor’s issues, opportunities, and thought process.  However, this option should only be attempted if the analyst has been successful at previous duties and has a good working relationship with the business sponsor.

It would be presumptuous to take either of these steps without first fully understanding the data that is being accessed, and that understanding comes from a close relationship with the data’s metadata.  Hopefully, the organization has identified the source data, documented the transformation performed in the ETL process and identified that the data comes from a trusted and certified source.  Effective metadata includes clear definitions developed by the business.  Metadata should show that the data is timely and the quality of the data (complete, incomplete, un-validated, etc.).  A good data analyst should know what other data, both inside and outside the organization could be accessed to provide additional information, and the costs of accessing external data.

Data Analysts, Evaluation, Predictive Analytics

While most data analysts routinely examine and report what has happened in the past, perhaps evaluating trends using historical data, there are opportunities to try to predict the future and suggest courses of actions based on predictive analytics.  For example:

  • What if the company raises subscription fees?  How many customers would be expected to cancel?  What are the characteristics of those customers and what will be the impact on costs and revenue?
  • What if the primary competition raises their rates by 10%?  What might it mean for the company’s ability to steal these customers away from the competitor?
  • What could be the outcome of a promotion for slowly moving products? 
  • What if customer service representatives were outsourced?  How much will the company save in wages?  What will the change mean to customer satisfaction metrics?  How many customers could the company lose?

An advanced data analyst should look for opportunities to evaluate these “what if” possibilities.  While there will be many dead ends with analysis that generate no valuable results, there will be others that will initiate actions that will have a significant impact on an organization’s bottom line.

A strong data analyst will consider the technique of data mining which does not involve the normal hypotheses common to queries.  Data mining looks for patterns in the data that have never been considered but could present on opportunity for effective action.  An example is a data mining application that identified credit cards being used at a gas pump for a very small charge and that card having been stolen.  There had been no hypothesis proposed for this outcome – it was exposed through data mining.

By being proactive, proposing and creating new ways of looking at the data, by expanding the scope of data sources, through continuing data management education and analytics, by extending into predictive analytics and data mining, a strong data analyst can become far more valuable to the business.

Conclusion

Elevation to the role of advanced data analyst does not come easily or quickly.  It is a gradual process involving delivery of relevant and important information to the business users and continuing education.  It involves being thoroughly familiar with the business terminology and the operations of the business unit.  Advancing in a data analyst career can take a variety of paths and offer value to the organization and to the analyst.

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Sid Adelman

Sid Adelman founded Sid Adelman & Associates, an organization specializing in planning and implementing Data Warehouses. He has consulted and written exclusively on data warehouse topics and the management of decision support environments.

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