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Career Advice for Analysts: Become an Agile Problem-Solver

To become a recognized expert, you cannot just rely on your technical knowledge. There are two parts to becoming an agile problem-solver: the "problem-solver" part and the "agile" part.

Acquiring technical expertise with business intelligence and analytics tools and techniques is only the starting point for becoming a professional data analyst or data scientist. To become the recognized expert in your organization, you cannot just rely on your technical knowledge.

Instead, you need to develop a reputation for being a problem-solver -- the person who can understand the business challenge, internalize what the business partner is looking to achieve, and put together a plan to deliver the necessary information that can help drive profitable actions.

There are two parts to being an agile problem-solver: the "problem-solver" part and the "agile" part.

Basics of Problem-Solving

We begin our training in problem-solving early in our education; the fundamentals are integrated into algebra and geometry. We can take lessons from these disciplines to summarize a straightforward approach to solving a problem:

Step 1: Define what problem you want to solve

Step 2: Plan out the tasks needed to solve the problem

Step 3: Identify what data you need to solve the problem

Step 4: Specify what you already know

Step 5: Determine what information you are missing

Step 6: Figure out how to get the missing information and collect it

Step 7: Execute your plan

Step 8: Review the results, and if the solution is not exactly correct, go back to step 1 and refine what question or problem you are looking to solve.

Often, the business problem itself is not well defined, and your business partner may be asking one question but looking for a solution to a completely different question. Reviewing the results at step 8 allows you to cycle back to step 1 and refine the problem, which may also dictate changes in the approach used to solve the problem. This lends credence to the need for "agility."

Basics of Agility

The agile development method is predicated on incremental and iterative delivery, and it is motivated by a number of operating principles. Some of the more important ones for the analytics professional include:

Face-to-Face communication: Having frequent direct interactions establishes a close working relationship with your business partner and helps to improve trust as the project progresses.

Rapid and continuous delivery: Frequent iterations of delivery and inspection of the results allow you to collaborate directly with your business partner to refine the approach and look for the best outcomes.

Flexibility in requirements: Attempts at creating a solution may yield a better understanding of the business problem. This may lead to adjustments in the desired outcome, which then impacts the original requirements. Being flexible enables rapid adaptation to changing demands.

Keep it simple: The most elegant solution is often the one that is simple to devise, simple to explain, and simple to optimize.

Combine these aspects of the agile development approach with a clear process for problem solving. That allows you to properly leverage your technical expertise in the use of analytics methods and tools while you gain a reputation for quickly satisfying your business partners' needs.

About the Author

David Loshin is a recognized thought leader in the areas of data quality and governance, master data management, and business intelligence. David is a prolific author regarding BI best practices via the expert channel at BeyeNETWORK and numerous books on BI and data quality. His valuable MDM insights can be found in his book, Master Data Management, which has been endorsed by data management industry leaders.


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