In this digital world we live in, data is everywhere, and information is valuable. Companies that leverage the full potential of their data can increase their operating margins by 60%.
With so much data but limited time and resources, knowing how to collect, organize, and classify data can be a game-changer for any business. That’s where gaining a better understanding of the different techniques can help you get started on your data analysis efforts.
In this guide, we’ll define data analysis and how it can help create business value. We’ll also discuss the different types of data and explore the six best data analysis techniques. Feel free to click on the following links to go to the topic that interests you the most:
What is data analysis?
Data analysis is the process of collecting, interpreting, and analyzing data to gather valuable insights. In other words, it turns raw data into useful statistics, information, and explanations to help users make better-informed decisions.
In a world with so much data, data visualization technologies are important to analyze massive amounts of information and make data-driven decisions. It’s the visual depiction of information through charts, graphs, maps, and other graphics. It’s imagery dedicated to messaging or presenting the information.
And data is everywhere. In 2020, online activities created 2.5 quintillion bytes of data each day from sales funnels, social media, etc. So, it could be a company’s most valuable asset when analyzed correctly.
For instance, your business can use data analysis to collect, interpret, and analyze everything related to your customers. You can get information on your customers’ demographics, interests, and habits through this process. In addition, the data can help you understand their purchasing behaviors, the channels they use to communicate with you, and which marketing funnels drive most of your sales.
Data analytics specialists are adept at processing raw data to produce useful information. So, whether you’re looking for an expert on Microsoft Access, business intelligence dashboards, or Google Analytics, you can connect with professionals through Upwork to get the information you need to make smart business decisions.
Can data analysis create business value?
Data analysis can create business value because it tells you where to focus your capital and efforts. So, brands need to pay attention to and analyze data from customers and other sources, such as competitors. Some ways data analysis can help create business value include:
- Improve decision-making and forecasting
- Increase business performance and boost competitiveness
- Maximize sales and effectiveness of marketing campaigns
- Streamline operational procedures and processes
- Create better customer experiences
- Lower operating costs and reduced waste
Data analysis can help businesses make better decisions on many aspects, such as what marketing strategies to implement, how to identify new markets, and which channels to use.
For example, data analysis can help companies improve customer experience by collecting customer feedback. The product design team can learn how customers use the product, what they don’t like about it, and what features they’d like to see. Customer support can also use this feedback to understand what frustrates customers and which channels are best to communicate with them.
Types of data
There are several types of data that businesses can use to inform their decisions, including:
- Big data
- Structured/unstructured data
- Metadata
- Real-time data
- Machine data
- Quantitative vs. qualitative data
Big data
Big data refers to large and diverse data sets that are created at incredible speeds in real-time from different sources. It’s defined by high volume, high velocity, and a wide variety. Therefore, data sets that are difficult for traditional relational databases to capture, manage, and process are considered big data.
Relational databases are designed to recognize relations among stored items of information. However, even if it’s difficult for relational databases to process big data, big data is still important because it drives things like machine learning, which forms the building blocks of artificial intelligence (AI). Users can discover patterns to better understand why things happen by mining and analyzing big data.
AI also uses big data to predict how things may happen in the future and provide solutions based on these insights. Google is an example of a company that uses big data. Their algorithm uses data collected from users and search engine queries to develop a better understanding of what a user is trying to search for so it can present the right results.
Structured/unstructured data
All data has a structure in one way or another. So, identifying if data is structured or unstructured comes down to whether it has a predefined data model and whether it’s organized in a predefined way.
The first difference between the two is that structured data refers to clearly defined and searchable data types, while unstructured data is usually stored in its native format. This is why structured data is easier to search and analyze, while unstructured data requires more work to process and understand.
Structured data is also typically quantitative, while unstructured data is generally qualitative. Some examples of structured data include names, addresses, credit card numbers, geolocation, etc. Examples of unstructured data include text, social media activity, surveillance imagery, etc.
Metadata
Meta is a prefix that means an underlying description. Thus, metadata is mainly described as data that provides information about other data. So, metadata can be defined as a summary of basic data information, making it easier to find, use, and reuse particular data instances.
Metadata can be created automatically, but it may contain basic information. When it’s created manually, metadata tends to be more accurate. Metadata is used for computer files, images, relational databases, videos, webpages, etc.
For instance, the metadata on your business website might be pieces of information you use to tell the search engines and any site visitors more about your page and the information it contains. This information may include title, author, date published, content description, etc. Having the ability to search for a piece of particular information from that metadata also makes it easier for users to find what they’re looking for.
Real-time data
Real-time data refers to data that’s available as soon as it’s created and acquired. Instead of being stored, this type of data is forwarded to users as soon as it’s collected. Real-time data is used in real-time analytics or operational intelligence.
Operational intelligence, also called business intelligence, refers to the process of turning raw data into practical and meaningful insights to give users a real-time understanding of what’s happening across their systems and technology infrastructure to help them make informed decisions. It’s used in many industries in circumstances where a quick response is critical.
The information you get from your GPS is an example of real-time data. Banks and other financial institutions also apply real-time analytics to detect credit card fraud as the transaction is taking place.
Machine data
Machine data, also called machine-generated data, refers to digital information created by online activities and operations of networked devices. It contains a definitive, real-time record of all the activity and behavior of users, transactions, applications, servers, networks, and mobile devices.
Machine data is created automatically on a fixed schedule or as a response to an event. And it’s rarely modified by humans, but it can be collected and analyzed. This automation is why machine data is said to be one of the fastest growing and complex areas of big data.
The transaction records from an ATM and IP addresses from network logs are examples of machine data. The data is valuable for businesses because it contains critical information on user behavior, machine behavior, security threats, and fraudulent activity, among others.
Data analysts value machine data because if it’s accessed and analyzed, it can help organizations identify threats, solve problems, and use machine learning to help predict future issues.
Quantitative vs. qualitative data
Quantitative data is data that can be measured using numbers and values. It’s statistical and typically structured in nature, which means it’s more rigid and defined. For example, this data type may answer the question, “How much?” or “How many?” Meanwhile, descriptive data analysis provides the “What happened?” when analyzing quantitative data.
Quantitative data can be gathered through instruments, tests, experiments, surveys, market reports, and metrics. A few examples of quantitative data are your age, the number of visitors a website records per day, and the number of iPhones sold in 2021.
On the other hand, qualitative data describe qualities or characteristics. That’s why it can be challenging to measure and analyze. It’s categorized based on properties, attributes, labels, and other identifiers. It’s also described as nonstatistical and unstructured.
Qualitative data can be collected using questionnaires, interviews, focus groups, or observation. For example, a person’s hair color, the shape of the cookies in a box, and the type of car you drive are all examples of qualitative data.
Best data analysis techniques
There are several methods of data analysis that data analysts use. However, the methods vary depending on the type of data in question and the goal of the data analysis. The best data analysis techniques used across industries include:
- Regression analysis
- Cluster analysis
- Factor analysis
- Cohort analysis
- Text analysis
- Time series analysis
1. Regression analysis
Regression analysis is a common technique used in finance, investing, and marketing. This data analysis technique helps analysts understand the relationship of independent variables to a dependent variable. It shows how the dependent variable changes about changes in independent variables.
Regression analysis is used by businesses to understand how sales of a particular product (dependent variable) are impacted when there are changes in price, available designs, and quantity available (independent variables.)
In this example, the analysis might show that a price increase and available designs increase the demand and sales for the product. It could also show that a decrease in the quantity of available inventory increases the order and sales for the item.
2. Cluster analysis
Cluster analysis is considered a statistical method and is used to group similar data into categories. This analysis technique aims to sort data elements into groups where the degree of association between the data elements is high if they belong to the same group and low if they belong to different groups.
Cluster analysis doesn’t make any distinction between dependent and independent variables. Instead, it’s used to find hidden patterns in the data or provide additional context to a dataset.
The most common usage of cluster analysis is to develop market segments. Customers are divided into clusters based on demographics, purchasing behaviors, or other factors relevant to a company. Then, the data is used to optimize a business’s marketing efforts to give customers the best experience based on their needs or preferences.
3. Factor analysis
Factor analysis is a statistical analysis method used in data mining. Its primary objective is to reduce the number of observed and unobserved variables in datasets. The unobserved variables, also called factors, are not directly observed but are rather inferred (through a mathematical model) from other variables.
So, factor analysis is used in data interpretation to reduce the number of variables. Factor analysis allows for better visibility of patterns in the data, and it helps analysts focus and clarifies the results. For example, some uses of factor analysis in marketing include identifying the price most appealing to customers and determining brand features that influence consumer choice.
4. Cohort analysis
Cohort analysis is a data analysis process where users are grouped based on their shared traits to better track and understand their actions. After all, a cohort means a group of people with shared characteristics. So, this type of data analysis method uses historical data to examine and compare a customer’s segment based on behavior.
Google Analytics is an example of a data analysis tool that has a cohort analysis feature. It groups website visitors by acquisition date—the first time a user visits a website. You can use this feature to compare the behavior and metrics of different cohorts over time. Cohort analysis can also be useful if you’re looking to find out how your marketing efforts impact your sales to specific groups of customers.
For instance, you may decide to create two versions of a marketing campaign with different messaging, calls to action (CTAs), and landing pages. You’ll then run the campaign on different cohorts and use cohort analysis to track the performance of your campaign. The results could help you understand which type of messaging and content gets your visitors to sign up, buy, or engage with your website.
Data analysts can not only help you run analytics on your website, but they also can turn whatever metrics you’re staring at into practical, actionable information. Connect with world-class data analysts on Upwork to get started with your data analysis project.
5. Text analysis
Text analysis uses machine learning to organize unstructured text data to gain valuable insights automatically. Businesses use text analysis tools to digest online data and documents and transform the information into actionable insights. For example, by transforming human language into machine-readable data, text analysis tools can sort text by topic, extract keywords, and read for emotion and intent.
Text analysis identifies important information within the text and delivers qualitative results. For example, you can use text analysis to extract specific information, like keywords, names, or company information from thousands of emails. You can also use it to categorize survey responses by sentiment and topic.
Since text analysis tools can categorize vast quantities of information in seconds rather than days, this technique allows you to attend to more important business tasks. It’s also a game-changer when it comes to detecting urgent messages in real time. By training text analysis models to detect urgent impressions or negative sentiments, companies can flag tweets, reviews, videos, or tickets that contain such and resolve the issue immediately.
6. Time series analysis
Time series analysis is a certain way of analyzing a sequence of data points collected over an interval of time. A time series, in other words, is simply a series of data points ordered in time. The independent variable is time, and the goal is usually to make a forecast for the future.
With time series analysis, analysts record data points at consistent intervals over a set period instead of recording in response to an event, randomly or intermittently. This data analysis technique helps businesses understand why trends occur. By analyzing data over consistent intervals, companies can also use time-series analysis as a predictive technique to foresee the likelihood of an event happening in the future.
Stock market reports are excellent examples of time series analysis in action. The finance industry also uses this technique to plot reports as currency changes over time. Another example is when businesses record their quarterly sales or create projected sales reports.
See what data analysis can do for your business
Data is key to better-informed business decisions. Data can help businesses improve their customer service experience and the efficiency and security of their operations. However, the amount of data and its accessibility can be a challenge. So, your company’s ability to collect data, interpret it, and turn it into actionable insights could either hinder your growth or boost your success.
As you can see, there are several data analysis techniques to consider. However, to turn your raw data into useful information, you need to start with the kind of data you have and define what you want to achieve in the process.
It’s also best to turn to professionals who know what they’re doing. Connect with talented data analytics specialists through Upwork. We have the largest network of independent professionals. You’ll find someone whether you’re looking for an expert on data collection, qualitative data analysis, quantitative data analysis, Python, or data science.
