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Data Analytics and Insights


What is it:

Data analytics and insights is the process of extracting business value from the massive amount of data a business capture daily. Analytics can be backward-, or forward-looking and enables businesses to describe what happened, predict what will happen and prescribe the next best action.

The middle of the 20th century brought about the third industrial revolution with computers becoming economically viable, and with it, capturing each business transaction electronically instead of manually on paper. The information became one of the enterprises most valuable assets and mining this data for actionable insights became a competitive advantage.

What are the different components involved to enable value creation from data:

  1. Data needs to be extracted from source systems into an environment where it becomes human-readable.

  2. Data needs to be cleaned and renamed if needed for efficient processing.

  3. Data from different sources and business units need to be consolidated to get a view across the organization.

  4. Data needs to be placed in an environment where it can be consumed by visualization tools, machine learning algorithms, and downstream systems.

  5. Data needs to be placed in a secure environment, and data governance should be applied to ensure data quality.

Data skills development tips:

  1. Use the platform economy to your advantage: Youtube, Udemy and Coursera are examples where information is shared between producers and consumers of information. Much of it is free, some is paid for, but access to sources of information and learning has never been easier.

  2. Never stop learning: The half-life of an engineering degree has dropped to 4 years or less these days, which means half of what you learn in the first year of a 4-year degree is obsolete by the time you graduate. Always keep your knowledge fresh even at small increments.

  3. Practice good data governance principles in all aspects: Take accountability of the fact that you are working with valuable and sensitive information. Keep it secure, accurate and in compliance with relevant regulations.

  4. Certifications: Keep yourself up to date with the latest           certifications in your discipline.

  5. Don’t forget to hone the storytelling skills: Getting your point across with data often involves telling the story with visualizations and patterns.

  6. Collaborate: Sharing tips and collaborating with colleagues improves the skill level of all.

  7. Ethics in data: Always keep it ethical. Ask yourself the question: How would you want someone else to act if they had access to the same information about you?

In Short:
Data analytics, machine learning and AI is projected to add between $10-$15 trillion to the world economy. Many steps are needed before an organization can start tapping into this value including the pipes to move the data to an environment where it can be processed, visualizing the main trends and patterns, and building predictive and prescriptive models. Honing these skills are key to success.

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