As virtually every sector becomes more data-driven in how they approach all aspects of their business, many employees will be faced with the new and unfamiliar task of figuring out how to do data analysis. People who are unsure of how to analyze data might find this new responsibility intimidating, but this article will outline six foolproof steps to get even the most timid budding analysts off to a great start!
What is data analysis and why is it important?
With the rise of digitalization, companies routinely collect huge amounts of data – commonly referred to as big data. Yet, most of these companies only use a fraction of this data, leaving behind a treasure trove of undiscovered insights that could potentially change the course of their future for the better. Data analysis aims to harness the power of this untapped data by digging in and interpreting it to uncover actionable insights that can help companies make informed business decisions based on facts instead of intuition.
Effective data analysis can profoundly affect profits by tracking marketing and sales ROI, providing a deeper understanding of customer behavior and market trends – and more! With the right data, know-how, and a will to discover, there are few things that data analysis can’t uncover about how your company does business – and what it should do in the future.
4 Reasons Savvy Companies Take Steps to Analyze Data
While there are virtually unlimited ways and reasons to use data analysis, here are four of the most common reasons companies are increasingly looking into how to analyze data.
1. To make better-informed decisions
When companies understand the impact of their current business practices from marketing to sales to customer service and beyond, they can tell with absolute certainty what’s working and where they need to make changes. When companies make data-driven decisions, they’re less likely to waste time and money on things that are ineffective – or worse, yet, have a negative impact on the business. Understanding how to do an analysis of data can help inform future investments, identify growth opportunities, predict profit and loss, and identify negative trends before they become irreversible – all leading to better and more informed business decisions.
2. To better understand their customers
The world’s most successful companies have long understood how to analyze data related to customer behavior, and this will only increase in importance as businesses become more competitive. Understanding how to analyze data gathered from customer interactions can help companies understand their customers’ needs and preferences, so that they can improve communication and personalization, offer the right products and services, and deliver better customer service. Robust customer-data analysis can sharpen customer segmentation by pinpointing customers’ demographics, interests, and purchasing patterns – and uncover what customers like the most and least about the company’s products.
In addition to helping companies better serve their existing customers, taking steps to analyze data can identify the key attributes of people who are most likely to become customers. Understanding these demographics can drive informed marketing decisions while saving significant time and money by targeting only the most likely prospects.
3. Reduce costs
When looking into how to do data analysis, many companies discover that predictive analysis can help reduce costs by forecasting demand. Predicting inventory demand can help companies to avoid overstocking and understocking products while predicting peak and low periods of foot traffic and customer service requests can help them to maintain ideal staffing levels. Furthermore, identifying the most effective marketing and sales tactics can help companies achieve optimal ROI when winning new business and upselling existing customers.
4. To better understand their competition
Many companies first look into how to analyze data as a way to stay ahead of their competition. Companies are in the best position to outperform their competitors when they study and understand their competitors’ sales tactics, pricing structures, marketing strategies, customer demographics, and strengths and weaknesses.
6 Steps to Analyze Data
If you’ve found this article, you’re probably wondering how to do an analysis of data. The following six steps to analyzing data are an ideal place to start.
1. Decide what you want to find out
It’s easy to get lost down the rabbit hole of analytics tools, so the first step to analyze data effectively is to define some objectives or what you’re hoping to find out. For example, if you’re in marketing, you might want to identify which of your company’s social media channels get the most engagement and which types of content perform best.
Customer service managers might want to understand traffic patterns to help inform staffing levels. Product teams might look into support requests to help prioritize bug fixes and new product features.
This critical first step will help to ensure that you’re collecting the right data, using the right data analysis techniques, and identifying actionable insights.
2. Gather the appropriate data
Once you know what you’re hoping to find out, one of the next steps to analyzing data is to figure out what data is relevant to your analysis and where to find it. You’ll probably need to collect a mix of qualitative and quantitative data from various sources. Qualitative data is objective, non-numerical, and unstructured (e.g., customer reviews, competitor analysis, market research, employee surveys), which means that it can’t be measured or counted in its raw form, and usually requires some structuring or categorizing before it can be analyzed. Conversely, quantitative data (e.g., financials and other numerical data) can be easily measured, counted, compared, and analyzed in its original format.
3. Scrub the data
Data analysis assumes that you’re using reliable data. Before proceeding to any of the next steps to analyze data, it’s important to make sure that you’re looking at high-quality data – data that’s consistent and accurate. You’ll want to check for typos, missing data, and inconsistencies – this is particularly important when dealing with numbers (e.g., financials, dates) from different sources, which may be formatted differently in different systems.
You’ll also want to remove duplicate data and data that isn’t relevant to your analysis, and look for ways to summarize the data you’ll be analyzing. Most small data sets can be analyzed in a spreadsheet, but if you’re dealing with massive amounts of data (i.e., big data), you might want to look into a data cleaning tool that can assist in tidying up the aforementioned issues. If you’re planning to perform this data analysis on a regular basis, it could be worthwhile to create data collection processes with standardized naming and formatting conventions to cut down on future busy work.
4. Choose an analysis tool
Now that the tedious stuff is out of the way, you’ll need to choose a tool for how to do an analysis of data to get the insights you’re looking for. The best tool depends on the type of data you’re analyzing (i.e., qualitative or quantitative) and how much data you’re dealing with. Here are four of the most common types of data-analysis tools:
- Many departments already use software that includes built-in purpose-specific analytics tools that can analyze things like employee performance, marketing metrics, web stats, sales figures, and financial information. These can be an ideal starting point in your data analysis journey.
- Spreadsheets are the perfect entry-level solution for analyzing small- to medium-sized datasets by employees with limited technical expertise.
- Business intelligence tools are an excellent choice for identifying trends, patterns, and other insights within large data sets.
If you have a large volume of historical data, predictive analytics tools can help predict future outcomes based on past performance.
5. Analyze the data
This is where the rubber hits the road! When trying to understand how to analyze data, the first step is to look for patterns, trends, and cause-and-effect relationships. From here, you can begin to understand why certain things happened and even predict what’s likely to happen in the future – and how.
If you’re dealing with quantitative data, data analysis should be a relatively simple task. However, if you’re dealing with unstructured qualitative data, you’ll probably need to use one of these methods:
- Text analysis uses machine learning to detect, interpret, and translate large data sets into actionable data by rearranging text-based data into a more manageable format. This is also known as “data mining” and it’s commonly used to analyze qualitative data from things like product reviews, social media channels, and surveys. This can be a complicated process that requires an appropriate data analytics tool.
- Sentiment analysis uses a combination of machine learning to detect and categorize positive and negative sentiments in unstructured data (e.g., support tickets, social media channels) to measure brand perceptions.
- Topic analysis organizes and structures text-based data by assigning pre-defined tags to unstructured data. This is commonly used to help companies understand what customers like most and least about their products based on support inquiries, product reviews, and other feedback channels.
6. Understand what the data is telling you – and what it isn’t
Correlation doesn’t always mean causation – be careful not to jump to the wrong conclusions. Let’s say your company experiences a spike in online sales in parallel with a surge of new Instagram followers. It’s easy (and obvious) to assume that marketing is hitting it out of the park. Before you invest the majority of this quarter’s marketing budget in new Instagram content, make sure that Instagram is in fact driving the majority of converted traffic to your e-commerce store. It’s quite possible that this is a false causality and the two trends are unrelated. It’s also possible to inadvertently select and interpret data in a way that supports a desired outcome, this is called confirmation bias – and it can lead to the wrong conclusions and poorly informed business decisions. Remain skeptical, always base your conclusions on concrete data, and use data analysis to try to disprove your assumptions rather than prove them.
What to Do After Analyzing Your Data
Once you understand how to do an analysis of data, there’s little doubt that data analysis can help drive business decisions that increase profitability. To reap the full benefits of their understanding of how to analyze data, many companies visualize their data on real-time dashboards.
With Plecto, you can schedule and generate reports that automatically distill complex data into visual formats that make it easier to understand so that you can keep tabs on the crucial data that’s bringing you closer to your business goals. Automated reporting ensures that you always have the most information – and Plecto even lets you export your data to PowerPoint or Excel in just a few clicks.
Check out our blog post about the benefits of using dashboards, and then sign up for a free 14-day trial of Plecto!