- What is Data Analysis?
- Benefits of Data Analysis
- Data Analysis Research Process
- Data Analysis Research Methods and Techniques
In times past, data analysis(DA) wasn't a top-rated career path. Although companies used it’s research methods to develop their products and systems, it wasn't ranked as an emerging job opportunity until Big Data became a thing.
According to Forbes, the data analytics profession is exploding as the employment of data analysts increases. Reports from Udacity and Bscholarly say 53% of companies globally employ data skills like Data analysis to ensure better accuracy and accelerate decision-making.
With the evolution of technology, businesses worldwide are seeing reasons to find and analyse data for their day-to-day activities.
Most businesses leverage data analysis research methods to produce targeted results. From marketing and product development to software development, companies see a need to gather data for effective decision-making.
Although there's so much data, few understand the research methods and techniques needed to collate accurate information amid multitudes.
But before we delve into DA research methods, let's quickly understand data analysis.
What is Data Analysis?
Data analysis is extracting valuable information from data to make informed decisions.
Data analysts gather data, organise it, and pinpoint trends and patterns with various software tools before producing visualisation and reports for decision-making.
Businesses aren't the only ones to analyse data. As individuals, we analyse data for our daily actions. Each time we assess the past and future consequences of a specific course of action, we engage in a basic form of DA.
So, what can your business gain from data analysis?
- Improved Customer Targeting: With data analysis(DA), you can identify people interested in your offer and know the best channels to spread your message. You don't want to waste your company's money and resources developing marketing campaigns for people who aren't interested.
- Reduces Operational Costs- DA reveals which areas of your company require more time and money and where to cut costs.
- Better Decision Making- DA can make better decisions based on the information gathered. It helps businesses get accurate and valuable information to create new marketing or business plans or realign the company's vision or mission.
Data Analysis Research Process
With the increasing number and complexity of data in the marketplace, there is a need to develop an efficient process to harness data. So, let's look at the several iterative steps of DA.
Clarity and identification:
Before diving into your data analysis research, you need to identify what data you're looking for and why. Then, ask questions that clarify what to look for in your research. For example, if you want to embark on a marketing DA, you can ask questions about what interests your customers or their daily purchasing habits; It will guide you from veering off the track.
At this stage, you begin to collect the data sets you need to get the information you need. Your information set could be internal, like your CRM software or directory. On the other hand, it could be external such as questionnaires, surveys, government databases or even from online sources like social media.
Once you've gathered all the necessary information, it's time to edit for proper analysis, as not all data will be helpful. Editing your raw data will erase errors and inconsistencies with wrong formatting or duplicating records. This way, you can avoid tainting your analysis with inaccurate data.
At this stage, you can analyse your data using DA techniques such as statistical analysis, neural networks, text analysis, and other methods that will help you conclude your raw data. In addition, look out for trends, patterns and any indications that can help you answer the questions you created in the identify stage.
The last step, which is also one of the most important, is to figure out what your results mean. At this stage, you decide what to do next based on your findings. For example, you could find out if your customers prefer luxury products to day-to-day products etc. You can also find some limitations on your business at this stage and work on them.
Now that we've known the basic DA processes let's look at the techniques employed.
Data Analysis Research Methods and Techniques
Data analysis research methods lie in two major types of analysis: Quantitative and qualitative.
Quantitative data analysis derives data in numerical forms used in statistical analysis and mathematical computations.
On the other hand, qualitative data analysis derives its raw data through symbols, words or observations. You use it to analyse behavioural data, histories, events, and other intangible scenarios.
Data Analysis Research Methods
- Artificial Intelligence and Machine Learning Related Methods
AI is on the rise and has proven a valuable tool in data analysis.
Artificial Intelligence (AI) and Machine Learning are one of the latest technologies changing the way we do business. You can find several AI techniques employed in data analysis. Some of them are:
Artificial Neural Networks
It is a type of analytics that aims to comprehend, with minimal intervention, how the human brain generates insights and predicts values. Neural networks evolve and progress with each piece of data received.
With the Neural Networks' intelligent algorithm, businesses can make predictions based on historical and current data imputed.
In the decision tree method, as the name suggests, you weigh the different outcomes and consequences of the decisions on your table. Then, like in a flowchart, you outline and compare the pros and cons of each decision. In the end, you can select the most preferred based on your analysis.
For instance, if you want to open a new business location in one of two places, you can compare the costs, time, revenue and other factors to consider for both places. That way, you can see which of the two is more realistic for your business.
To learn more about Artificial Intelligence: Check out this article.
- Mathematics and Statistics Methods
Factor analysis, also called "dimension reduction", is a data analysis used to explain the differences between correlated variables observed in terms of a smaller number of unobserved variables called "factors."
An excellent way to understand this way of looking at data is to think about how a customer views your company brand. You could have factors like product usability, durability, accessibility, most frequent purchase location and usage. The list can go on and on, depending on what you want to track. In this case, factor analysis comes into play by putting these variables into similar groups. For example, you could put usability, durability and accessibility into a single latent variable called the "Customer Experience."
Regression looks at past data to figure out how one or multiple changes affected the value of a dependent variable. By knowing how each variable affects the others and how they have changed in the past, you can predict what might happen and make better choices in the future.
Imagine your analysis in 2019 revealed that branding and marketing affected your overall revenue. Then, in 2020, you would want to review that factor and check how the COVID-19 pandemic also affected your revenue.
Time Series Analysis
The time series analysis looks at data collected over a certain amount of time. Analysts use this method to keep track of data points over a set period instead of just checking them now and then. However, time series analysis is not only used to collect data over time. Instead, it helps researchers determine if any variables changed during the study, how the different variables are related, and how they got to the final result.
Time series analysis can study seasons' effects on behaviours or trends. For instance, you can gather data on how people buy a particular product in summer and winter. Then, these two variables can predict purchasing decisions for such seasons.
Other Data Analytics Techniques to apply
Cohort data analysis, like regression analysis, uses historical data to look at and compare the behaviour of a particular group of users. These users can then be put together with others who have similar traits. This method gives you a fuller understanding of what works for a particular group or set of customers.
Cohort analysis is a great way to analyse marketing because it lets you see how your campaigns affect certain groups of customers. For example, let's say you send out an email campaign telling people to sign up for your site. You make two different versions of the campaign, each with a different design, call to action (CTA), and ad content. Later, you can use cohort analysis to track how well the campaign is doing over a more extended period and figure out what kind of content is getting customers to sign up, buy again, or interact with you in other ways.
Text analysis, also called "text mining" in business, refers to simplifying large amounts of data.
With text analysis, you can discover how people feel about something, thanks to machine learning tools with intelligent algorithms.
This method lets you figure out positive, negative, or neutral feelings and then you can give it a score based on certain factors and categories that are important for your brand. For example, you can use text analysis to keep an eye on the reputation of a brand or product and to figure out how well the customer experience is going.
Today's businesses depend on big data, and different data analysis methods can help you turn insight into action.
Data analysis methods vary, but any good one will make your business more efficient, cohesive, insightful, and successful.
We've considered the data analysis definition and examined data-centric analytics' practical applications. One thing is clear: By organising your data and using your metrics, you can turn raw data into action that will boost your business.