Data is everything. In our increasingly digitalized world, the greatest currency is data. There was gold, then oil, and now, we have data. Daily existence calls for decision-making at various points, and to make the best decisions we need to leverage data.
How do we leverage this data you might ask? That’s where data analytics comes in. In the next few paragraphs, I’ll answer that question as I guide you through what data analytics is about.
II. What Is Data Analytics?
Defining anything is tough. Before I say what Data Analytics is, let me first say what it is not: Data Science. Though related, these are two different processes. My conflating the two is why it took me so long to delve into the world of analytics.
Data Science is a broad field that contains various aspects of data analysis, but its scope also extends to machine learning and predictive modeling, as well as utilizing statistical techniques – which ensured I kept a healthy distance!
Data Analytics, on the other hand, is more focused on examining data to support tactical decisions and improve day-to-day operations. Both fields are essential for leveraging data to drive business success, but they are not the same.
Examining data and drawing conclusions from it is what analytics is about. These conclusions tend to be centered around optimizing business performance, efficiency, and profit. Effectively, whenever you interpret data to make strategically-guided decisions, you’ve put on the hat of a data analyst.
To paint a simple picture for understanding, think of it as the secret formula Netflix uses to keep us from getting a full night of sleep.
Various approaches to Data Analytics exist, generally, there are 4 types of Data Analytics which include looking at what happened (Descriptive Analytics), why something happened (Diagnostic Analytics), what’s going to happen (Predictive Analytics), and what should be done next (Prescriptive Analytics).
Additionally, as a Data Analyst, you would use several analytical methods and techniques to process data and extract information.
A popular method is Regression Analysis which involves inspecting the connection between variables that depend on each other to determine how a change in one might affect another.
For all the talk of data in tech today, you’d be forgiven for thinking this is all new, but Data Analytics didn’t start existing yesterday, even though a lot of the field is centered around modern developments.
There’s the rich historical context that has evolved dating back as far as the 18th and 19th centuries with early Statistical Analysis.
Looking at the historical evolution from the Early Computing Era of the mid-20th century, through the introduction of spreadsheet software like Microsoft Excel in the 1980s to the big bang that was the Internet and Big Data around the turn of the millennium, all the way to today’s Predictive Analytics through the gifts of Data Science, Machine Learning, and Artificial Intelligence (AI), we see the transition brought on by technological advancements and how the field has been greatly augmented.
III. The Basics of Data
Data is a collection of facts. Data quality is a critical aspect of working with data, and it's all about how accurate, consistent, complete, reliable, and relevant our data is for the task at hand. Imagine data as the building blocks of our decision-making process.
When our data is of high quality – meaning it's error-free, stays consistent over time, doesn't have gaps, and comes from trustworthy sources – it lays a strong foundation for informed decisions and efficient processes.
Think of it as having a reliable GPS for your business journey. Clean data not only saves you time and effort in data cleaning but also builds trust among your team and stakeholders, making everyone more confident in the insights and decisions that data can provide.
Plus, it's a money-saving tool, preventing costly mistakes in investments, marketing, and operations.
Moreover, having clean data is like sharpening your analytics tools, it ensures that your models and predictions are accurate and reliable. Plus, in some industries, it's a must to meet compliance requirements and maintain ethical data practices.
IV. The Data Analytics Process
The data analytics lifecycle represents the series of stages and activities involved in a typical data analysis project. It outlines the steps from defining the problem to delivering actionable insights. Google’s Data Analytics Course lays down 6 steps: Ask, Prepare, Process, Analyse, Share, and Act.
V. Tools and Technologies
One way of distinguishing fields is from their tools. Doctors have stethoscopes, scalpels, and thermometers, while analysts have spreadsheets, SQL, and visualization tools. These are some common data analytics tools.
The spreadsheet is software for computing and organizing data. Excel is the most common spreadsheet application. Structured Query Language - SQL (pronounced ‘sequel’) is a programming language built for managing databases.
R is a programming language designed for statistical computing that also has data visualization capabilities. Python is a popular alternative to R because, in addition to statistical computing, it has many other uses.
Furthermore, there are visualization tools that make understanding intricate datasets, facilitating data interaction, and uncovering insights so much more convenient than in the past. Data visualization involves transforming raw data into visual representations, so specialist tools like Tableau and Power BI are essential.
VI. Case Study
I want to highlight the importance of data analytics in the content market, using Netflix. Consider this pseudo examination: When you type “Game of Thrones” into Netflix’s search bar, it fills in the words before you’re done typing, even though they don’t have the show, but Netflix goes ahead to return their shows.
For me, the first result is “The Witcher.” Why do you think that is? Why suggest another fantasy show, based on a different fictional book series, set in a medieval-inspired world of magic and swords? Why not suggest “Orange is the New Black?”
That’s data analytics at play. Beyond content suggestion, it’s a safe assumption that the producers and executives at Los Gatos anticipated an overlap between GOT’s viewers and those who would toss a coin to The Witcher before they signed off millions of dollars to produce the Netflix original.
There was the data, it was analyzed, and a decision was made based on that.
VII. Data Privacy and Ethics
With my legal background, the considerations for ethical data handling and analysis are particularly interesting to me, but I’m only trying to introduce data analytics today, and the importance of data privacy needs to be addressed in-depth on another day, especially since they can hear everything we say!
VIII. Getting Started With Data Analytics
For anyone interested in learning more about Data Analytics, or is just starting, you can look at these cool resources:
Google Data Analytics Professional Certificate: This is a great beginner course for building the foundation of what you’ll need on your Data Analytics journey.
Women in Data Africa (WiDA) Community: WiDA is helping women build data-driven skills for a data-driven career.
Data Analytics: What It Is, How It's Used, and 4 Basic Techniques: An Investopedia article that talks a bit more about the technical nitty-gritty of Data Analytics.
Every business has its goals, and the path to attaining those goals usually lies in data; it’s why our data is so important today because, through good analytics, you can turn statistics into results.
I’m Jason, and I’m intrigued by the world of data. I hope I’ve helped you make some sense of what this field is about.
If you’ve found this article fascinating, or if you’re just starting, good luck as you explore further and practice your skills. To learn more, you could read articles specifically dedicated to any of the four types of Data Analytics –I’m a little partial to Predictive Analytics!
Lastly, don’t forget to think like a data analyst every time you have a decision to make; I wish you many amazing insights ahead!