Four Dimensions of Data Career

As the data world is getting bigger, wider, deeper it's becoming more complex. Building a career in data can't be generic anymore. One needs to pick the right combination of specializations to stand out. This article aims to list four dimensions to pick the right combination for ones data career.

Industry

Understanding special data needs , gaining experience in one or more industries is important because each industry has its own nuances, security, compliance and regulatory requirements. Here are some of the major industry areas that have large data needs.

• ?Retail
• Finance
• Healthcare
• Manufacturing
• Telecommunications

Business Function

Each department or a business function within an enterprise has specialized data requirements, analytics needs, security and data sensitivity considerations and very specific business lingo. Understanding these and having experience in these specific areas helps not only in building a specialized career bust also can help in narrowing the focus when looking for new opportunities.

Here are some of the most popular business functions that have very distinct data and analytics requirements.

• ?HR Analytics
• ?Financial analytics
• ?Operational Analytics
• ?Marketing analytics

?Process Area

As data moves from left to right, in other words from source to final consumption, each stage of the process needs specialized skills, tools and expertise. Its almost impossible for one to be an expert in all of these, so picking one or two of these areas to specialize and go deep can enrich ones data career.

• ?ETL/ELT/Data onboarding
• ?Data warehousing/Lake/Lakehouse
• ?Data visualization
• ?Data science
• ?Data analysis
• ?DataOps
• ?Data Governance
• ?Data Quality?

Technology

Picking the right set of technology also brings improved focus to dive deep. With so many new technologies emerging, without pre-meditated selection of tools, Career path can lead to confusion to oneself and also to the target companies looking for the right data professional.

Here are some of the combinations to consider. What combination of tools to pick also depends on the process area that you choose from the above. For example someone who wants to focus on data visualization, should focus on picking a Visualization tool without worrying about the cloud platform or data platform.

• Cloud?Platforms : Amazon Web Services (AWS) Microsoft Azure , Google Cloud
• ?Data platforms: Snowflake , Databricks , 谷歌 BigQuery
• ?Visualization Tools : Tableau , Microsoft Power BI , Looker , Sisense
• ?ETL/Observability Tools: Fivetran , Talend , 咨科和信 ,@Matillion, Apache Airflow , dbt Labs
• ?Data Governance/Quality tools: OvalEdge , Ataccama , Alation . Atlon
• ?Data Science Al/ML frameworks: @AWS SageMaker, @Snowflake snowpark
• ?Programming languages: Python Coding , R Programming , Go Language.

Conclusion

One may choose be specialist in one of these dimensions while being a generalist in other dimensions such as a snowflake expert who can work in any industry.

Another may opt to be a specialist in a business function such as marketing analytics for any industry using any technology

Yet another may choose to deep focus and be a hyper specialist such as HR analytics in Financial industry using Snowflake and Tableau.

Picking a combination and deciding how deep you want to go as specialist will help you standout from all other run-of-the-mill data professionals. As I mentioned earlier this will also give you focus on which companies to target, what type of professional network you want to build.

One last thing not to forget is that knowing we have the power to change any of these combinations as your career evolves, your interests change and your experiences get deeper.

Wishing you all the best and go have a DATAstic career !!.

#aws #azure #gcp #snowflake #databricks #bigquery #tableaue #powerbi #sisense #looker #dbtlabs #fivetran

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