> Discover: Business Analytics >
by Walter Rodriguez, PhD, PE
In this blog series, you will learn what data analysts do and how you can get started without background knowledge, skills, or experience in the field. Hint: It’s all about curiosity, concentration, context, correlation, design, observation, strategy, dedication, and persistence. Although helpful, it’s not necessarily about having previous knowledge, skills, or experience. In fact, you might not realize it yet, but you already possess most if not all of the above skills. Like many other business, information technology, project management, and data science fields, becoming a data analyst is about having (or developing) the mindset to learn and apply what you have learned consistently.
First, let’s start with what data is and what a data analyst does. Essentially, people in business, cybersecurity, healthcare, e-commerce, science, technology, government, and industry use data for design & development, decision-making, discovery, modeling, production, sales, marketing, operations. For instance, data can be used to (1) decide which new products or services should be developed and offered to customers, (2) improve employee and customer retention, (3) evaluate business opportunities, and (4) redesign entire business processes among many others.
But, what is data? Simply put, data is a collection of facts—from numbers & words to images & videos. You create data all the time when you enter your name, address, phone number, or age in a software application or a website form. When you post on Facebook or stream a movie on Netflix or order a product on Amazon, or when you make a call on your cell phone or pay with your credit card, you are using and creating data. And, yes, when you set your GPS in your car. Data is all over the place. And data is power, as demonstrated by the success of people and top companies that know how to collect, store, transform, organize, analyze, and mine data. More formally, according to Wikipedia, data is defined as “units of information, often numeric, that are collected through observation. In a more technical sense, data are a set of values of qualitative or quantitative variables about one or more persons or objects. In contrast, a datum (singular of data) is a single value of a single variable.”
Activity: Use Google to find: how many searches are conducted on Google Search Engine every minute?
Data can be transformed into useful information, knowledge, insights, and even wisdom.
Managers, scientists, and engineers use data to develop and test new products or services. These individuals may start working and creating data in the form of observations, notes, or surveys. Once they collect data, they can transform (i.e., clean, etc.) and organize it to help them (1) make decisions, (2) obtain useful insights into a business situation, (3) arrive at a certain conclusion, (4) make purchasing decisions, or (4) predict weather patterns or even future product needs and wants, among many others.
Data analysts are people like you and me responsible for collecting, transforming, and organizing data & information to facilitate decision-making and problem-solving. This makes data analyst in 2021 one of the most practical careers in healthcare, engineering, finance, government, science, law, politics, tourism, management, marketing, and business & industry in general.
Activity: Check the US Labor Statistics to find out the need for data analysts.
You will certainly realize that there are more positions available than data analysts available. At this writing, there are far fewer qualified data analysts than the number of positions available in the workforce marketplace.
As you move forward in your desired career path, you will find many more positions demand a data analysis skillset. That is the knowledge and skills to collect, transform and organize data to gain insights, arrive at conclusions, or make predictions. It’s now a must-have skill, along with your communication, decision-making, and problem-solving skills. More importantly, it’s a powerful skill to add to your resume to become more competitive in the job market or perhaps gaining a raise.
If you are already an IT professional, the skills & knowledge in the data analysis field will help you move even farther and quicker in your career path. Some of my former IS/IT students and certification-training participants have become data analysts in forensics, healthcare, project management, AI strategy, data management, cybersecurity, finance, and cloud computing. And one has super-specialized in just cleaning data (one of the most important data analyst roles since it requires assuring data is complete, correct, and relevant to the problem or situation at hand. Another former student devotes most of his time illustrating data by planning, creating, and presenting data visualizations.
In addition to working on data cleaning, data management & design, data visualization, and predictive modeling, data analysts apply techniques & methodologies, such as Cross-Industry Standard Process for Data Mining (CRISP-DM), regression analysis, decision trees, cluster analysis, and statistical analysis in general. As you develop your career as a data analyst, you may also learn about machine learning (ML) and AI neural networks to inform decision-making and discover patterns in data. Of course, let’s not forget Big Data—using large datasets for sentiment analysis, for example.
Activity: Get familiar with the lingo. Search your library (or web) for the definitions of terms related to data, such as data, database, dataset, gap analysis, query, query language, data analytics, data analysis, data design, data-driven decision-making, Big Data, data context, data ecosystem, data science, data visualization, data strategy, data cleaning, data management, data lifecycle management, etc.
If this sounds a lot, please don’t get discouraged because you will learn these definitions, techniques, tools, and methodologies just in time to apply to a real situation or problem. And as you seek industry certifications, such as Cognos (for data cleaning, preparation, and visualization), SAS (for transforming data into intelligence), and SPSS (for statistical analysis, ML algorithms, text analysis, integration with big data, and deployment). Further, becoming a Certified Business Intelligence Professional, Certified Analytics Professional, Certified Health Data Analyst or a Risk Analytics Specialist will certainly enhance your career. Fortunately, you don’t need to start knowing everything about data! You can start slowly and gain speed as you become more familiar with the concepts and tools available.
Why should we spend time & energy learning about data analysis? Answer: Job opportunities! For instance, the Society for Human Resouce Management (SHRM) estimates that almost 60% of the companies surveyed have plans to hire data analysts. According to the Bureau of Labor Statistics, the demand for data analysis is more than twice as high as any other function. As a data analyst, you can help your organization recognize patterns in the data and uncover relationships among data like a detective. This is what makes data analysis such an exciting career.
Activity: Write ten questions that you may use data to answer or address an issue. For instance, when is the best time to go to the beach and avoid traffic jams? When is the best time to show up to play tennis in public courts? Think: What types of data will you need to answer those questions?
You must use critical & analytical thinking skills to ask and draft relevant questions to define the problem or issue at hand. This is the first step in the data analysis process. While drafting those questions, you need to consider what kinds of data would amount to the desired result. That is, can the collected data be used to analyze the problem? Then, you will need to get ready to plan the data analysis process. For instance, how long will it take to interview or survey the stakeholders? How are you planning to secure the data? What resources (i.e., money, materials, computers, software, people, time) needed to conduct the survey? What are the ethical issues involved? How to avoid potential issues involved in asking the questions or collecting data from a survey? Will the process be fair and equitable? Was employee consent provided? How will data be stored and managed? To sum up, generating, collecting, storing (storage), and managing data.
This data collection process involves how you intend to collect, store, manage and protect the data. Of course, the data collected must be processed (i.e., data cleaning and data integrity to ensure it’s complete, correct, relevant, error-free, and avoid outliers). The processed data is explored, analyzed, and visualized to discover patterns and document your findings or results. Finally, the findings and results need to be interpreted, shared, and communicated clearly to the stakeholders or client. In this way, the actionable recommendations can be implemented promptly. The above is part of the data analysis life cycle—that is, the process involved in going from data collection to decision-making and beyond. Of course, data can be created, consumed, tested, processed, and reused within the life cycle.
In this process, you will use data analysis tools, techniques, and methodologies (along with some coding, math, and statistics) to productively derive meaning and facilitate decisions from the data collected.
You will be using descriptive, predictive, and prescriptive analytics in the data analysis processes. Descriptive analytics helps interpret data to identify patterns or trends and gain “insights from existing historical data” (Adobe Analytics 2021). Predictive analytics assist in forecasting outcomes and incorporates a “variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.” (Wikipedia 2021). Testing and prescriptive analytics help to determine the best outcomes and results. And it “uses data and machine learning to provide recommendations for the next steps a company can take for growth or optimization.” (Adobe Analytics 2021).
Activity: Select one of the previous issues and go through the above process to arrive at conclusions and recommendations for action. Alternatively, search on the web for business analytics examples using keywords or short phrases such as “data analytics in action” or “data analytics trends.”