Data Analytics

Introduction

In today's digital world, we constantly generate information, from swiping credit cards to browsing the web.

• This abundance necessitates the ability to analyze and interpret it into actionable insights.

Data: The Foundation

“Data! Data! Data!... I can't make bricks without clay!"—— Sherlock Holmes via Sir Arthur Conan Doyle

Now, data science, the discipline of making data useful, is an umbrella term that encompasses 3 disciplines.

• Statistics
• Helps make informed decisions with some uncertainty
• Machine learning
• Automates decision-making for large datasets with inherent uncertainties
• Analytics
• Explores data to uncover potential insights that can guide decision-making

Essentially, data is a collection of facts and information.

• Only with data, conclusions can be drawn for the best way forward (data-driven decicion).
• In everyday life, an allergic reaction prompts us to avoid the allergen in the future.
• In businesses, companies use data analytics to improve processes, identity opportunities and trends, launch new products, serve customers and make thoughtful decisions to stay ahead of the competition.

A data ecosystem encompasses the various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data.

Data life cycle provides a generic or common framework for how data is managed, and the specifics can differ based on the organization's needs.

• Plan: Decide what kind of data is needed, how it will be managed, and who will be responsible for it.
• Capture: Collect or bring in data from a variety of different sources.
• Manage: Care for and maintain the data. This includes determining how and where it is stored and the tools used to do so.
• Analyze: Use the data to solve problems, make decisions, and support business goals.
• Archive: Keep relevant data stored for long-term and future reference.
• Destroy: Remove data from storage and delete any shared copies of the data.

Analytical Thinking

Thinking is second nature to us. It just happens automatically, but there are actually many different ways to think.

• Analytical thinking involves identifying and defining a problem and then solving it by using data in an organized, step-by-step manner.
• In data analysis, you may also think critically to find out the right questions to ask or creatively to get new and unexpected answers.

The five key aspects to analytical thinking include

• Visualization - The graphical representation of information, such as graphs, maps, to explain information more effectively.
• Strategy
• Problem-orientation
• Correlation - NOT equal to causation
• Big picture and detail-oriented thinking

Data analysts use various techniques to solve problems, including

• The Five Whys to reveal the root cause
• Gap analysis to understand areas for improvement
• What did we not consider before?

Analytical Skills

Analytical skills are qualities and characteristics associated with solving problems using facts. These skills are

• Curiosity - to discover how much information he can coax out of the data in expected or unexpected ways
• Understanding context - to narrow down the variables that are most likely to influence the outcome
• Having a technical mindset - refers to the ability to break things down into smaller steps or pieces and work with them in an orderly and logical way.
• Data design - How you organize information
• Data strategy - The management of the people, processes and tools used in data analysis

Google Data Analytics

While there is no a single defined structure that is uniformly followed by every data analyst, Google data analytics process provides a solid framework:

• Ask questions and define the problem (business challenge, objective, or question) and understand stakeholder expectations
• Identify how the current state is different from the ideal state.
• Determine who the stakeholders are, what they want, when they want it, why they want it and how best to communicate with them.
• Prepare data by collecting and storing the information
• What type of data we need to answer those key questions and how are we going to collect that data?
• Data and results should be objective and unbiased.
• Process data by cleaning and checking the information
• Fix typos, inconsistencies, or missing and inaccurate data in spreadsheets and structured query language (SQL).
• Transforming data into a more useful format and combine two or more dataset to make information more complete.
• Removing outliers (data points that could skew the information)
• Analyze data to find patterns, relationships and trends
• Continue to use tools (like spreadsheets and SQL) to transform and organize the information to draw useful conclusions, make predictions and drive informed decision-making.
• For more advanced tasks like machine learning or statistical modelling, programming languages like R or Phyton come into play.
• Share insights with relevant stakeholders
• Data visualization tools like Tableau, Looker, Power BI and RStudio, make complex data understandable in graphs, maps, tables and charts.
• Act on the analysis results to solve the problem at hand

Real-World Applications

Businesses everywhere are increasingly leveraging data-driven insights to optimize operations and make informed decisions.

• This can involve using data analytics to gain a deeper understanding of customer buying habits, which can then be used to create more effective social media messaging.

Given a local city hospital has been receiving complaints about long wait times, data analysts can leverage data on the hospital's daily foot traffic to make more informed decisions about how many doctors they need to staff at any given time.

• This approach can help reduce wait times, improve the patient experience, and make better use of healthcare workers' time.

Summary

Data-driven decision-making involves using facts to guide strategy for successful outcomes.

However, no matter how valuable data-driven decision-making is, data alone will never be as powerful as data combined with human experience, observation and sometimes even intution.

• To illustrate, subject matter experts (the human touch) have the ability to look at the results of data analysis and identify any inconsistencies, make sense of gray areas, and eventually validate choices being made.