5 Key Components to Success in Data & Analytics

December 28, 2020

Data & Analytics has steadily grown into a top field of interest over the past decade and is forecasted to continue this growth well into the next decade. It has benefited from buzz and hype attached to advanced predictive modeling methods such as artificial intelligence and machine learning. However Data & Analytics is so much more than those two highly visible disciplines.

Data & Analytics includes Data Analysts, Business Analysts, Business Intelligence Analysts, Process Analysts, Data Scientists, Modelers, and so many more disciplines. There are 5 key components that every Data & Analytics professional must learn to master.

These include:
Data & Information

Data is the foundation by which all information is created. It fuels the decision making potential of all performance indicators which has a critical impact on the operation and direction of any business.

Analytic professionals must have the ability to understand data at a granular level and also be able to navigate all storage systems in which data resides. Having complete control and/or understanding of all data mechanisms is a requirement to ensure data is properly transformed into information and maintains the utmost quality.

Process & Perspective

Data & Analytic professionals must continually place data and information into relevant frameworks. Context and perspective is a requirement for this to be accomplished. This means analysts must have a granular understanding of how a business operates.

Analytic professionals must understand who the businesses customers are, what their products and/or services are, how revenue is generated, how the P&L works, and what are the businesses strategies and goals. This context and perspective is needed to understand all of the systems and processes that allow the business to operate and generate data.

The result is an analyst that can accurately organize data into systems which reflect the business therefore enabling measurements for decision making, modeling, and predictive analytics.

Organization

Data & Analytic professionals must be in command of the skills and disciplines required for their analytical focus. Some analytic focuses, like Data Science, require the command of skills such as advanced statistical methods, coding languages like python or R, and the ability to wrangle large sums of structured and/or unstructured data for the purpose or predictive analytics.

Other analytical focuses may require analyst have the ability to operate within relational databases. Here the skills required might include SQL, strong understanding of probability & statistics, database architecture, and operating within data lakes or data warehouses.

Each focus requires its own specialization but the purpose remains the same. The end goal is the organization of information to facilitate an outcome, a decision, a determination.

Determination & Decision Making

Data & Analytic professionals must have the ability to use their knowledge and acumen to make determinations on the information organized. Through each of these components analysts have been building relevant frameworks to consume, process, and understand data. However there must come a decision point to which the information serves it’s intended purpose.

Knowing when and how to accomplish this is a skill learned over time and by performing analysis. Some analysts are gifted and hone these skills quicker than others however the more time and experience acquired the better and quicker an analyst becomes.

Storytelling and Influence

Communication is the base for storytelling and influence. Analytic professionals put a lot of time and effort into understanding, transforming, and interpreting data. As they work through this process findings must be shared within the organization. The key however is to share information that is relevant with the appropriate audiences. For example, as an analyst works through a project, information that would be useful and actionable to the data engineering team would not be useful to a VP level decision maker on the Product team.

Analytic professionals must also give consideration to the methods by which information is being shared.

Is it best to create a deck with full context and details or better to use a one pager/email?

How information is visualized is also critical. Visualizations must contain the necessary details but still be clear, concise, and not confusing.

And lastly influence; analytic professionals are providing information with the intent of influencing decision makers. This is an important responsibility and demands an objective approach. Analysts should present information in a way that is devoid of preloaded intentions therefore eliminating any bias.

In the end we have 5 components that can be summed up into an easy to remember acronym, IPODS: Information, Process, Organization, Determination, and Storytelling. This in affect defines The Analytic Process. This is the foundation for all analytics and should sit at the core of all analytic professionals.

About the Author: My name is Ion King and I am the Chief Executive Officer at SimDnA. My focus is on helping others passionate about growing careers in Data Science & Analytics achieve their goals. Connect with me on LinkedIn or find more of my articles on medium

This article is a reflection of my opinion with additional information gathered from the sources linked throughout.

Header image by Methawee Krasaede from Pixabay

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