Data analysis is the process of cleaning, analyzing, and visualizing data with the aim of uncovering valuable information and driving smarter business results. Check out this article for a deeper dive into this 5-step process.
Rapidly changing markets, varying customer landscapes, and even global pandemics have necessitated a business to stay on top of its game. Companies that want to stay in business and thrive can acquire a drastic advantage by making smart choices and implementing strategic data analysis to inform decisions. Today’s digital ecosystem is rich in data which is bound for exponential growth. Organizations aim to harness the power of data to inform business decisions, predict customer behavior and drive transformative technologies. However, despite the colossal volume of data created every day, a mere 0.5% is actually analyzed and used for data discovery, improvement, and intelligence.
So, what can you do to ensure your data doesn’t get drowned in a deluge of information overload, and how can you properly analyze your data?
Before we get into the key steps to effective data analysis, let us answer one fundamental question:
What is Data Analysis?
Data analysis refers to the process of cleaning, analyzing, and visualizing data with the aim of uncovering valuable information and driving smarter business results. According to Brian Balfour, founder, and CEO of Reforge and Former VP of Growth at Hubspot:
“As you gain fresh insight from your data, it opens the door to new questions. As you have new questions, you need to update your instrumentation and analysis. Saying the process is “done” is saying you understand everything there is to know about your users, product, and channels.”
Data analysis can increase customer satisfaction and retention by providing a holistic vision of all aspects related to customers. A data-driven organization can reap the benefits of valuable insights that boost growth.
The following is a rundown of the 5 Key Steps you should take to analyze data and get the most out of it.
Defining your Objective
Defining your objective is the first step in the data analysis process. It is referred to as the ‘problem statement.’ Determining why you need data analysis typically stems from a business problem or question, such as:
- What type of social media marketing is more engaging to our potential customers?
- How can we reduce production costs without sacrificing product quality?
Marli Mesibov, VP of content strategy at Mad*Pow, states that “A screen full of analytics data looks like a secret code, and in a way, it is. That data has a lot of information in it, and it’s impossible to make sense of it without the key. Put another way, data can give answers, but only if you ask the right questions.”
Once you frame the right questions, data can supply new perspectives, steady predictions, and reveal influential knowledge. Defining your objectives and knowing how to solve and eliminate glitches can be achieved by a Root Cause Analysis (RCA), which ultimately determines the primary cause of a problem. Timely recognition of complications and priorities along with a clear definition of goals early on enables an organization to implement a strategic and solution-oriented workplace culture. Defining the problem is the first step to data analysis and will help you determine which data sources will best help you solve it.
We are surrounded by abundant data, and it can be overwhelming if you don’t know where to start your data analysis process. Data collection is a process that involves gathering and measuring data from a variety of relevant sources to find answers to specific research problems, evaluate outcomes, and forecast trends. According to Statista, the total volume of data collected was 64.2 zettabytes in 2020; it is predicted to reach 181 zettabytes by 2025.
After you have identified your organization’s requirements, it is time for the next step of data collection – ensuring that your data is complete during the collection phase and that it has been collected ethically and legally – which is critical for accurate analysis.
Data can be collected from a variety of sources such as case studies, surveys, interviews, direct observation, and focus groups. Creating a strong strategy for collecting and aggregating the appropriate data is a key part of determining which data will be useful for your company’s growth. Consumer data can be classified into the following categories:
- First-party data: It is collected directly from users by your organization and involves methods such as interviews, projective technique, Delphi technique, focus groups, and questionnaires.
- Second-party data: It includes data shared by another organization about its customers. Sources of second-party data comprise financial statements, government records, sales reports, retailer feedback, etc.
- Third-party data: This is a form of data that’s been aggregated and rented or sold by organizations that don’t have a connection to your company or users.
According to Dean Abbott, Co-founder and Chief Data Scientist at SmarterHQ:
“No data is clean, but most is useful.”
Abbott emphasizes a key aspect of the data analysis process: cleaning the data. Data cleaning is extremely important during the data analysis process because not all data is good data. Data cleaning is also referred to as data cleansing and data scrubbing. It is one of the most important steps for an organization that aims to create a culture around quality data decision-making. Once data is collected from all the necessary sources, data experts will be tasked with cleaning and sorting through it.
Data scientists will identify and eliminate duplicate data, anomalous data, and other inconsistencies that could negatively affect the analysis to generate accurate results. Only when the data is cleaned up can it be analyzed and turned into something useful. Some of the key steps in data cleaning include the following:
- Removal of Duplicate or Irrelevant Observations.
- Fixing Structural Errors.
- Filtering Data Outliners.
- Handling of Missing Data.
- Data Validation.
When an organization sets up a data cleaning process that is right for its business, it yields tremendous benefits. Just take our word for it!
After the data is cleaned, it will be ready for analysis. This refers to the process of analyzing raw data to draw meaningful conclusions from that information.
A variety of software tools such as spreadsheets, data mining programs, and open-source languages are utilized for data analysis.
The following approaches are implemented to manipulate data for information:
- Descriptive Analytics: It is the process of using current and historical data to identify trends and relationships. Descriptive analytics concerns itself with what has happened.
- Diagnostic Analytics: It uses data to identify trends, extract insights and answer questions. Diagnostic Analytics concerns itself with why a particular issue came up and utilizes data to highlight the problem-solving process.
- Predictive Analytics: In business, predictive analysis is commonly used to forecast future growth. Companies employ this method to find patterns in data to identify risks and opportunities.
- Prescriptive Analytics: It suggests a course of action. By considering all relevant factors, this type of analysis yields recommendations for the next steps. Prescriptive analytics provide valuable insights, which make it a vital tool for data-driven decision-making.
Data Interpretation and Visualization
Data interpretation is the process of reviewing data and drawing meaningful conclusions using a variety of analytical approaches. Data interpretation helps researchers categorize, manipulate and summarize data in order to make informed business decisions. As succinctly put by Carly Fiorina, the former chief executive officer at Hewlett Packard:
“The goal is to turn data into information and information into insight.”
Analysts employ various data interpretation methods to make complex numerical data understandable and accessible to employees, stakeholders, and clients. It ensures an organization can make sound decisions by identifying trends and anticipating demands. Effective data interpretation can help a business with cost reduction and better management of its resources.
Data Interpretation is executed using the following methodologies:
- Qualitative Data Interpretation: This data interpretation method is used to analyze qualitative data, which is also known as categorical data. It uses texts rather than numbers or patterns to describe data.
- Quantitative Data Interpretation: The quantitative data interpretation method is used to analyze quantitative data, which is also known as numerical data. It includes gross profits and net profits, statistical data on marketing returns, etc.
Data visualization utilizes elements like charts, graphs, maps, dashboards, and infographics. Such tools provide an accessible format to see and understand trends, outliers, and patterns in data. They are essential for analyzing massive amounts of information and making data-driven decisions. Writer and data journalist David McCandless illustrates the point by stating:
“By visualizing information, we turn it into a landscape that you can explore with your eyes. A sort of information map. And when you’re lost in information, an information map is kind of useful.”
For any business to be successful in today’s booming digital ecosystem, it is essential to understand and execute the process of effective data analysis in order to produce actionable insights and adapt to new market trends. Follow these five steps of data analysis to tackle obstacles and create a functional data-driven approach to your business.
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