What Is the Process of Analyzing Data to Extract Information Not Offered by the Raw Data Alone?

Data analytics is the procedure of analyzing raw information to draw out meaningful insights. These insights are so used to determine the best course of action. When is the best time to whorl out that marketing campaign? Is the current team structure every bit constructive as it could be? Which customer segments are most likely to buy your new production?

Ultimately, data analytics is a crucial driver of any successful business strategy. Simply how practice data analysts actually turn raw data into something useful? There are a range of methods and techniques that information analysts use depending on the blazon of information in question and the kinds of insights they desire to uncover. You lot can get a hands-on introduction to data analytics in this free short course.

In this post, we'll explore some of the most useful data assay techniques. By the end, you lot'll have a much clearer thought of how you tin can transform meaningless data into business organization intelligence. We'll cover:

1. What is data analysis and why is it important?
2. What is the difference between qualitative and quantitative data?
3. Information analysis techniques:
   1. Regression analysis
   2. Monte Carlo simulation
   3. Factor analysis
   4. Cohort analysis
   5. Cluster analysis
   6. Fourth dimension serial assay
   7. Sentiment analysis
4. The data analysis procedure
5. The best tools for data analysis
6.  Central takeaways

The offset half dozen methods listed are used for quantitative data , while the last technique applies to qualitative data. We briefly explain the deviation between quantitative and qualitative data in section two, but if you want to skip straight to a particular analysis technique, but utilize the clickable menu.

i. What is information analysis and why is information technology important?

Data analysis is, put simply, the process of discovering useful information past evaluating information. This is done through a process of inspecting, cleaning, transforming, and modeling data using belittling and statistical tools, which nosotros will explore in detail further along in this article.

Why is data analysis important? Analyzing data effectively helps organizations make business organisation decisions. Present, information is collected past businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.

These information will announced every bit different structures, including—but not limited to—the following:

Large information

The concept of big data —data that is so large, fast, or complex, that information technology is difficult or incommunicable to process using traditional methods—gained momentum in the early 2000s. Then, Doug Laney, an industry analyst, articulated what is now known as the mainstream definition of big data equally the three Vs: book, velocity, and multifariousness.

• Book: Equally mentioned earlier, organizations are collecting data constantly. In the non-likewise-distant past it would take been a real issue to shop, merely nowadays storage is cheap and takes up little space.
• Velocity: Received data needs to be handled in a timely fashion. With the growth of the Net of Things, this can mean these data are coming in constantly, and at an unprecedented speed.
• Variety: The information being collected and stored past organizations comes in many forms, ranging from structured data—that is, more traditional, numerical data—to unstructured data—think emails, videos, sound, and and so on. We'll encompass structured and unstructured data a trivial further on.

Metadata

This is a form of data that provides information about other information, such equally an image. In everyday life yous'll find this past, for example, right-clicking on a file in a folder and selecting "Get Info", which volition evidence you information such as file size and kind, date of creation, and then on.

Real-time information

This is information that is presented as soon as information technology is acquired. A adept example of this is a stock marketplace ticket, which provides data on the almost-active stocks in real time.

Auto data

This is data that is produced wholly by machines, without man instruction. An case of this could be call logs automatically generated past your smartphone.

Quantitative and qualitative data

Quantitative data—otherwise known equally structured information— may appear every bit a "traditional" database—that is, with rows and columns. Qualitative information—otherwise known as unstructured data—are the other types of data that don't fit into rows and columns, which can include text, images, videos and more. Nosotros'll discuss this farther in the side by side section.

2. What is the difference between quantitative and qualitative information?

How yous analyze your data depends on the blazon of data y'all're dealing with— quantitative or qualitative . So what's the difference?

Quantitative data is anything measurable , comprising specific quantities and numbers. Some examples of quantitative data include sales figures, email click-through rates, number of website visitors, and per centum revenue increase. Quantitative data analysis techniques focus on the statistical, mathematical, or numerical analysis of (usually large) datasets. This includes the manipulation of statistical data using computational techniques and algorithms. Quantitative analysis techniques are often used to explicate certain phenomena or to make predictions.

Qualitative data cannot be measured objectively , and is therefore open to more than subjective estimation. Some examples of qualitative information include comments left in response to a survey question, things people have said during interviews, tweets and other social media posts, and the text included in production reviews. With qualitative data analysis, the focus is on making sense of unstructured data (such every bit written text, or transcripts of spoken conversations). Oft, qualitative analysis will organize the information into themes—a process which, fortunately, tin be automated.

Data analysts piece of work with both quantitative and qualitative data , so information technology'south important to be familiar with a variety of analysis methods. Let's take a look at some of the nearly useful techniques now.

3. Data analysis techniques

Now nosotros're familiar with some of the different types of information, let'due south focus on the topic at hand: different methods for analyzing data.

a. Regression analysis

Regression analysis is used to estimate the human relationship between a set of variables. When conducting any type of regression analysis , y'all're looking to run across if in that location'south a correlation between a dependent variable (that's the variable or effect you want to mensurate or predict) and whatever number of independent variables (factors which may have an bear on on the dependent variable). The aim of regression analysis is to estimate how ane or more than variables might impact the dependent variable, in social club to place trends and patterns. This is especially useful for making predictions and forecasting future trends.

Let'due south imagine you work for an ecommerce company and you lot desire to examine the relationship betwixt: (a) how much coin is spent on social media marketing, and (b) sales revenue. In this case, sales revenue is your dependent variable—information technology'south the factor you lot're near interested in predicting and boosting. Social media spend is your contained variable; you want to make up one's mind whether or not it has an touch on sales and, ultimately, whether it's worth increasing, decreasing, or keeping the same. Using regression analysis, you'd be able to see if at that place'southward a relationship betwixt the two variables. A positive correlation would imply that the more y'all spend on social media marketing, the more sales acquirement you make. No correlation at all might propose that social media marketing has no bearing on your sales. Understanding the human relationship between these 2 variables would help you to make informed decisions well-nigh the social media budget going forward. However: It's important to notation that, on their ain, regressions can only be used to determine whether or not in that location is a relationship between a fix of variables—they don't tell you anything nearly crusade and consequence. And then, while a positive correlation between social media spend and sales revenue may suggest that one impacts the other, it's impossible to draw definitive conclusions based on this assay alone.

There are many different types of regression analysis, and the model yous use depends on the blazon of information you have for the dependent variable. For instance, your dependent variable might be continuous (i.e. something that can be measured on a continuous scale, such as sales revenue in USD), in which case you'd use a different type of regression analysis than if your dependent variable was categorical in nature (i.east. comprising values that can be categorised into a number of distinct groups based on a certain characteristic, such every bit client location by continent). You lot can acquire more than about different types of dependent variables and how to cull the correct regression assay in this guide .

Regression analysis in action: Investigating the human relationship betwixt clothing brand Benetton'south advertising expenditure and sales

b. Monte Carlo simulation

When making decisions or taking certain deportment, there are a range of different possible outcomes. If you take the bus, you might get stuck in traffic. If you walk, you lot might go defenseless in the rain or bump into your communicative neighbour, potentially delaying your journey. In everyday life, nosotros tend to briefly weigh up the pros and cons before deciding which action to accept; still, when the stakes are high, it's essential to calculate, every bit thoroughly and accurately as possible, all the potential risks and rewards.

Monte Carlo simulation, otherwise known as the Monte Carlo method, is a computerized technique used to generate models of possible outcomes and their probability distributions. Information technology essentially considers a range of possible outcomes and and then calculates how likely it is that each item outcome volition exist realized. The Monte Carlo method is used past data analysts to conduct avant-garde gamble analysis, allowing them to improve forecast what might happen in the future and make decisions accordingly.

So how does Monte Carlo simulation work, and what can information technology tell usa? To run a Monte Carlo simulation, you lot'll starting time with a mathematical model of your data—such every bit a spreadsheet. Within your spreadsheet, y'all'll have one or several outputs that you're interested in; profit, for case, or number of sales. You'll also have a number of inputs; these are variables that may touch your output variable. If you're looking at profit, relevant inputs might include the number of sales, total marketing spend, and employee salaries. If y'all knew the exact, definitive values of all your input variables, yous'd quite hands be able to summate what profit you'd be left with at the cease. Notwithstanding, when these values are uncertain, a Monte Carlo simulation enables you to calculate all the possible options and their probabilities. What will your profit be if yous make 100,000 sales and rent five new employees on a bacon of $50,000 each? What is the likelihood of this event? What will your turn a profit be if you only make 12,000 sales and hire five new employees? And then on. Information technology does this by replacing all uncertain values with functions which generate random samples from distributions determined by you, and so running a serial of calculations and recalculations to produce models of all the possible outcomes and their probability distributions. The Monte Carlo method is one of the most popular techniques for calculating the result of unpredictable variables on a specific output variable, making it platonic for risk analysis.

Monte Carlo simulation in action: A case study using Monte Carlo simulation for run a risk assay

 c. Factor analysis

Cistron analysis is a technique used to reduce a large number of variables to a smaller number of factors. It works on the ground that multiple separate, observable variables correlate with each other because they are all associated with an underlying construct. This is useful non only because it condenses large datasets into smaller, more than manageable samples, but too considering information technology helps to uncover hidden patterns. This allows yous to explore concepts that cannot exist easily measured or observed—such as wealth, happiness, fettle, or, for a more business-relevant example, customer loyalty and satisfaction.

Allow's imagine yous want to get to know your customers better, so you transport out a rather long survey comprising 1 hundred questions. Some of the questions chronicle to how they experience most your company and product; for example, "Would you recommend u.s.a. to a friend?" and "How would yous rate the overall customer experience?" Other questions ask things similar "What is your yearly household income?" and "How much are you willing to spend on skincare each calendar month?"

Once your survey has been sent out and completed by lots of customers, you end up with a big dataset that essentially tells you one hundred different things about each client (assuming each client gives i hundred responses). Instead of looking at each of these responses (or variables) individually, y'all tin use factor assay to grouping them into factors that vest together—in other words, to relate them to a single underlying construct. In this example, gene assay works by finding survey items that are strongly correlated. This is known as covariance . So, if there's a strong positive correlation between household income and how much they're willing to spend on skincare each month (i.due east. as one increases, so does the other), these items may exist grouped together. Together with other variables (survey responses), you may observe that they tin exist reduced to a unmarried factor such as "consumer purchasing ability". Likewise, if a client feel rating of 10/10 correlates strongly with "yes" responses regarding how likely they are to recommend your product to a friend, these items may be reduced to a single cistron such every bit "customer satisfaction".

In the cease, yous have a smaller number of factors rather than hundreds of individual variables. These factors are then taken forward for further assay, allowing you lot to larn more nigh your customers (or whatever other area you're interested in exploring).

Cistron assay in action: Using gene assay to explore client behavior patterns in Tehran

d. Cohort analysis

Cohort analysis is divers on Wikipedia as follows: "Cohort analysis is a subset of behavioral analytics that takes the information from a given dataset and rather than looking at all users equally 1 unit, it breaks them into related groups for analysis. These related groups, or cohorts, usually share common characteristics or experiences within a defined time-span."

So what does this mean and why is information technology useful? Let'southward intermission down the above definition farther. A cohort is a group of people who share a common feature (or activeness) during a given fourth dimension menstruum. Students who enrolled at university in 2020 may exist referred to every bit the 2020 cohort. Customers who purchased something from your online store via the app in the calendar month of December may besides be considered a cohort.

With accomplice analysis, you're dividing your customers or users into groups and looking at how these groups conduct over time. And so, rather than looking at a unmarried, isolated snapshot of all your customers at a given moment in time (with each customer at a different point in their journey), you're examining your customers' behavior in the context of the customer lifecycle. Equally a result, yous can start to place patterns of beliefs at various points in the customer journeying—say, from their first ever visit to your website, through to e-mail newsletter sign-up, to their first purchase, and so on. As such, cohort analysis is dynamic, assuasive you to uncover valuable insights about the customer lifecycle.

This is useful considering it allows companies to tailor their service to specific customer segments (or cohorts). Let's imagine you run a 50% discount campaign in lodge to attract potential new customers to your website. Once y'all've attracted a grouping of new customers (a cohort), you'll want to rails whether they actually buy annihilation and, if they do, whether or not (and how often) they make a repeat purchase. With these insights, you'll start to gain a much better agreement of when this particular cohort might do good from another discount offering or retargeting ads on social media, for case. Ultimately, cohort analysis allows companies to optimize their service offerings (and marketing) to provide a more targeted, personalized feel. You can learn more near how to run cohort assay using Google Analytics here .

Cohort analysis in action: How Ticketmaster used cohort analysis to heave revenue

east. Cluster assay

Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort dissimilar information points into groups (or clusters) that are internally homogeneous and externally heterogeneous. This means that information points within a cluster are similar to each other, and dissimilar to information points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.

There are many real-world applications of cluster analysis. In marketing, cluster assay is commonly used to grouping a big customer base into singled-out segments, allowing for a more targeted approach to ad and communication. Insurance firms might utilise cluster analysis to investigate why sure locations are associated with a loftier number of insurance claims. Another common application is in geology, where experts will use cluster analysis to evaluate which cities are at greatest risk of earthquakes (and thus try to mitigate the risk with protective measures).

It's important to note that, while cluster analysis may reveal structures inside your data, it won't explicate why those structures exist. With that in mind, cluster assay is a useful starting point for agreement your data and informing further analysis. Clustering algorithms are also used in machine learning—yous can learn more than about clustering in machine learning here .

Cluster analysis in action: Using cluster analysis for customer segmentation—a telecoms case study instance

f. Time series analysis

Time series analysis is a statistical technique used to place trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time (for example, weekly sales figures or monthly e-mail sign-ups). Past looking at time-related trends, analysts are able to forecast how the variable of interest may fluctuate in the future.

When conducting fourth dimension series analysis, the main patterns you'll be looking out for in your information are:

• Trends: Stable, linear increases or decreases over an extended time flow.
• Seasonality: Anticipated fluctuations in the data due to seasonal factors over a short period of time. For instance, you might see a superlative in swimwear sales in summer around the same time every year.
• Cyclic patterns: Unpredictable cycles where the data fluctuates. Cyclical trends are not due to seasonality, but rather, may occur as a result of economical or industry-related conditions.

Every bit yous tin can imagine, the power to brand informed predictions near the futurity has immense value for business. Fourth dimension serial analysis and forecasting is used beyond a variety of industries, about commonly for stock marketplace analysis, economic forecasting, and sales forecasting. There are different types of time serial models depending on the data you're using and the outcomes you want to predict. These models are typically classified into three broad types: the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models. For an in-depth await at time series assay, refer to this introductory report on fourth dimension serial modeling and forecasting .

Time series assay in action: Developing a time series model to predict jute yarn demand in People's republic of bangladesh

chiliad. Sentiment analysis

When you recollect of data, your mind probably automatically goes to numbers and spreadsheets. Many companies overlook the value of qualitative information, only in reality, there are untold insights to be gained from what people (especially customers) write and say about y'all. And then how do yous go nearly analyzing textual data?

One highly useful qualitative technique is sentiment analysis, a technique which belongs to the broader category of text analysis—the (usually automated) process of sorting and understanding textual data. With sentiment analysis, the goal is to interpret and classify the emotions conveyed inside textual information. From a business perspective, this allows yous to define how your customers feel about various aspects of your brand, product, or service. There are several different types of sentiment assay models, each with a slightly unlike focus. The three primary types include:

• Fine-grained sentiment assay: If yous want to focus on stance polarity (i.e. positive, neutral, or negative) in depth, fine-grained sentiment assay will allow you lot to practise and so. For example, if you lot wanted to translate star ratings given by customers, yous might apply fine-grained sentiment analysis to categorize the various ratings forth a scale ranging from very positive to very negative.
• Emotion detection: This model oft uses complex machine learning algorithms to pick out various emotions from your textual information. You might use an emotion detection model to identify words associated with happiness, anger, frustration, and excitement, giving yous insight into how your customers feel when writing about you lot or your production on, say, a product review site.
• Aspect-based sentiment analysis: This blazon of analysis allows you lot to identify what specific aspects the emotions or opinions relate to, such as a certain product characteristic or a new ad campaign. If a customer writes that they "detect the new Instagram advert so abrasive", your model should detect non but a negative sentiment, but also the object towards which it's directed.

In a nutshell, sentiment analysis uses various Natural language Processing (NLP) systems and algorithms which are trained to associate sure inputs (for instance, certain words) with sure outputs. For case, the input "abrasive" would be recognized and tagged as "negative". Sentiment analysis is crucial to understanding how your customers experience well-nigh you and your products, for identifying areas for comeback, and even for averting PR disasters in real-fourth dimension!

Sentiment analysis in action: five Real-earth sentiment assay example studies

4. The data analysis procedure

In order to proceeds meaningful insights from data, data analysts will perform a rigorous stride-by-step procedure. We go over this in detail in our stride by stride guide to the data analysis process —but, to briefly summarize, the data analysis process generally consists of the following phases:

Defining the question

The first step for any data analyst will be to define the objective of the analysis, sometimes called a 'problem statement'. Essentially, yous're asking a question with regards to a business trouble you're trying to solve. One time you've defined this, you lot'll so demand to determine which data sources volition help yous answer this question.

Collecting the information

Now that you've divers your objective, the next footstep will be to prepare up a strategy for collecting and aggregating the appropriate information. Will you be using quantitative (numeric) or qualitative (descriptive) data? Do these information fit into starting time-political party, 2nd-political party, or 3rd-political party data?

Learn more than: Quantitative vs. Qualitative Data: What's the Departure?

Cleaning the data

Unfortunately, your nerveless data isn't automatically ready for analysis—yous'll have to clean it first. As a data analyst, this stage of the process will take up the virtually time. During the information cleaning process, you will likely exist:

• Removing major errors, duplicates, and outliers
• Removing unwanted information points
• Structuring the data—that is, fixing typos, layout bug, etc.
• Filling in major gaps in data

Analyzing the data

At present that nosotros've finished cleaning the data, information technology's time to clarify information technology! Many analysis methods have already been described in this article, and it's upwards to you lot to decide which one will best accommodate the assigned objective. It may fall under one of the following categories:

• Descriptive analysis , which identifies what has already happened
• Diagnostic analysis , which focuses on understanding why something has happened
• Predictive analysis , which identifies future trends based on historical data
• Prescriptive assay , which allows you to make recommendations for the future

Visualizing and sharing your findings

Nosotros're near at the terminate of the road! Analyses have been made, insights have been gleaned—all that remains to be done is to share this information with others. This is usually done with a data visualization tool, such equally Google Charts, or Tableau.

Learn more: 13 of the Nearly Common Types of Data Visualization

Every bit yous can imagine, every phase of the information analysis procedure requires the information analyst to have a diversity of tools under their belt that assist in gaining valuable insights from information. We comprehend these tools in greater detail in this article , but, in summary, here's our best-of-the-all-time list, with links to each product:

The top nine tools for information analysts

• Microsoft Excel
• Python
• R
• Jupyter Notebook
• Apache Spark
• SAS
• Microsoft Ability BI
• Tableau
• KNIME

6. Key takeaways and further reading

As you lot tin see, at that place are many different data assay techniques at your disposal. In order to turn your raw data into actionable insights, it'southward of import to consider what kind of data you have (is it qualitative or quantitative?) likewise equally the kinds of insights that will exist useful inside the given context. In this post, we've introduced seven of the about useful information analysis techniques—just there are many more out at that place to be discovered!

So what now? If you haven't already, we recommend reading the case studies for each assay technique discussed in this post (you'll detect a link at the end of each section). For a more hands-on introduction to the kinds of methods and techniques that information analysts use, try out this gratuitous introductory information analytics short class. In the meantime, y'all might too want to read the following:

• The All-time Online Data Analytics Courses for 2022
• What Is Time Serial Data and How Is It Analyzed?
• What Is Python? A Guide to the Fastest-Growing Programming Language

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Source: https://careerfoundry.com/en/blog/data-analytics/data-analysis-techniques/

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