SMEs need to handle their data quickly to succeed in the digital marketplace. Here, we have everything you need to know in order to make the best decisions with data analysis. See our other blogs on data analysis for the background information and mitigating risks. 

Contents

Best Types of Data Analysis Methods

Applicable Data Analysis Techniques

 

The Ten Best Types of Data Analysis Methods

In today’s world, data is everywhere. Many businesses and organisations have revolutionised because of their access to data. However, as the quantity of data increases exponentially, so does its complexity. This has made it difficult for businesses to find a way to extract value from their data and make sense of it at a glance. If you’re looking for ways to revitalise your business or organisation through data analysis, you’ve come to the right place! Here are some of the best methods available to help unlock insights into your data and discover new angles that can transform your business.

Analysing data visually will enable you to participate in the data consumption process by taking well-arranged data sets and using them as a powerful solution to problems in several key sectors, such as marketing, sales, customer experience, HR, fulfilment, finance, logistics analytics, and others. Let’s quickly go over the main analysis categories before diving into the essential types of methods.

Descriptive Analysis – What happened

The starting point for any analytic reflection is descriptive analysis, which seeks to answer the question ‘What happened?’ by organising, manipulating, and interpreting raw data from various sources to generate valuable insights for your company. It is critical to perform descriptive analyses, as they allow us to present our insights impactfully. While it is important to note that this type of analysis will not allow you to predict future outcomes or answer questions like ‘Why did this happen?’, it will leave your data organised and ready to pursue further investigations.

Exploratory Analysis – Exploring Data Relationships

An exploratory analysis investigates the data and variables before connection. Before this phase, no idea of the connections between data and variables existed. After examining the data, you can hypothesise and produce solutions to specific issues via an exploratory analysis. The most common usage is data mining.

Diagnostic Analysis – Why it Occurred

Knowing the cause and effect of a phenomenon enables analysts and decision-makers to develop a nuanced understanding of its implications. Among its other vital organisational functions, retail analytics, one of the world’s most important research methods, provides direct and actionable answers to specific questions.

Predictive Analysis – Forecasting Future Events

Using the results of the previously discussed descriptive, exploratory, and diagnostic analyses, machine learning, and artificial intelligence, the predictive method allows you to peer into the future and discover what will occur. For example, you may use your data to find future trends, potential issues, inefficiencies, connections, and casualties. Predictive analysis can help you gain an advantage by enhancing various operational processes. By understanding why a trend, pattern, or event occurred through data, you can make a more informed forecast about how things might play out in specific business areas.

Prescriptive Analysis – How it Will Happen

It’s one of the most effective analysis methods in research. Prescriptive data techniques cross over from predictive analysis, utilising patterns or trends to produce efficient and responsive business strategies. You will play an active role in the data consumption process by taking well-organised sets of visual data and using them as practical solutions to emerging issues in several key arenas, such as marketing, sales, customer experience, HR, fulfilment, finance, logistics analytics, and others.

Now, for the moment you’ve all been waiting for. Here are the top 10 best types of data analysis methods.

Cluster Analysis

Cluster analysis is a type of data analysis that groups similar items based on common characteristics or belongingness. In marketing research, it is used to segment customers based on demographics, psychographics, and other characteristics. Cluster analysis can be helpful for market segmentation and segmentation of product features. It can also analyse customer satisfaction depending on their previous buying patterns. This type of analysis is also beneficial for identifying categories with low sales or high customer complaints and putting them into a segment to understand why it’s happening.

Cohort Analysis

A cohort analysis studies different groups of people by their demographics or other characteristics (such as location, education, etc.). It determines how certain groups are affected by trends and helps predict future trends. For example, an e-commerce company might be interested in studying where its customers come from. By examining the locations of each customer, they can create cohorts and determine which regions are providing the highest sales. This type of analysis is prevalent in marketing and public relations or other fields such as sociology and demography.

Regression Analysis

Regression analysis is one of the most popular statistical modelling and prediction data analysis methods. It finds a relationship between one or more independent variables and one or more dependent variables. This type of analysis predicts customer behaviour based on past trends of recent events. For example, a sports apparel retailer might want to know how factors like weather, seasonality, and sports events may affect sales in their online store. Market research with regression analysis can help the company predict sales and make decisions based on their findings.

Neural Networks

A neural network is a computer system inspired by the human brain that learns, recognises, and makes connections between things. Marketing research finds patterns or relationships between different parts of a business, such as customers, products, or sales channels. It can help predict customer behaviour, find ways to improve products and services and analyse customer satisfaction. Neural networks are similar to other machine learning tools that are gaining popularity in data analysis.

Factor Analysis

Factor analysis identifies underlying themes or concepts in a large raw data set. It helps summarise large bodies of information and is common in sociology, psychology, and marketing research. For example, a market research analyst may use factor analysis to find underlying themes or concepts in customer satisfaction ratings. This can help the analyst understand what customers like and dislike about a product or service. Factor analysis is sometimes combined with other data analysis methods, such as cluster analysis.

Data Mining

Data mining finds hidden patterns and correlations in large volumes of data. It helps predict future trends and optimise business processes. Data mining is applied in various fields, including marketing research and business analytics. For example, a market research analyst may want to know what types of customers are purchasing a particular product. Data mining can find hidden patterns in past customer data and provide an answer.

Text Analysis

Text analysis is a type of data analysis that analyses text and creates statistical data. It helps understand what customers say about your business and can be used with multiple data types, including social media posts, comments, and reviews.

Time Series Analysis

Time series analysis is a type of data analysis that looks at a time series graph of specific data over time. It helps understand long-term trends and variability in data over time and can be applied to many data types, including sales figures and product usage. For example, a business may want to know how sales figures have changed over the past ten years. Time series analysis can be applied to the sales data to determine how they’ve changed over time.

Decision Trees

A decision tree is a visualisation tool and analytical method used to make predictions and find patterns in data. It helps understand customer behaviour, predicts future behaviour, and makes business predictions. Decision trees are sometimes used with other data analysis methods, such as cluster and factor analysis. For example, a market research analyst may use a decision tree to understand why customers buy a particular product and whether certain factors drive their decision.

Conjoint Analysis

Conjoint analysis determines how consumers value certain products and features. This analysis helps optimise outcomes, determine customer preferences, and understand customers’ motivations. For example, an apple-growing company might want to know what features customers value the most in their apples. 

In conclusion, data analysis is an essential aspect of business that can help companies make smarter business decisions. With these 10 data analysis methods, you can unlock insights into your data and discover new angles to transform your business.

The Best Applicable Data Analysis Techniques‍

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Data analysis is a crucial part of any data science project. It’s where all the information you find from your data is valuable and actionable. There are many different ways to analyse data, but not all are useful for every project. Some may be more suitable than others, depending on your specific needs. Here we look at various data analysis techniques and explain when it’s best to use each.

Collaborate Your Needs

The success of any project is determined by how well it meets the needs of its users. The same is accurate with data analysis. Before starting your data analysis, you must be sure of the needs and questions of the stakeholders. Who is going to use the insights? What kinds of questions do they have? Collaborating on these needs and questions will help set a clear path for the data analysis. It will ensure the investigation is focused and generates valuable insights.

Establish Questions

You need to go beyond asking what your analysis will look like. You must also ask who, what, when, where, and why. This is especially important if you’re unsure what to find from the data. This predominantly applies if you work with unstructured data like text or images. Once you’re confident that you understand the questions you want to ask from your data, you can move on to democratising your data.

Data Democratisation

This is the process of taking all the data you’ve collected from different sources and making it reusable and accessible. This step is essential since most data projects collect data from many different places. It will allow you to reuse data in new ways, make it easier to get data as needed and make it easier to manage your data. Having all your data in one place makes cleaning and processing easier. There are a couple of ways to go about democratising your data. You can centralise your data by putting it all in one place. Or you can normalise your data by making sure your data has the same structure in different areas.

Think of Governance

There are many ways to organise your data but to do so, you need to consider governance. This entails figuring out how your team will use and share the data. What data will be used across the team? What data will each group work with? How will data be sourced, managed, transmitted, stored, processed and secured? These questions will help you determine the best data governance structure.

Regarding data storage, it’s best to use a cloud solution since most businesses are mobile and have no fixed location. For data processing, it’s best to go with a combination of managed services and custom code. And for data sharing, it’s best to use a data management platform like a data lake or cloud data warehouse.

Set Your KPIs

Key performance indicators, or KPIs, are metrics that measure success. They help show how well an initiative or project is doing and inform future decisions. KPIs are especially important for data analysis since it’s a way for business stakeholders to understand what their data means. Once you know your KPIs well, you can build a data management roadmap. A data management roadmap is simply a flowchart that shows how data is collected, stored, processed, and analysed. It highlights areas for improvement and optimisation.

Omit Useless Data 

Collecting data is often a byproduct of doing business, but the collected data doesn’t all have to be used. Some data can be considered irrelevant or unusable. For example, an online store may have a rewards points system, but the data collected from this program is useless because it doesn’t affect sales. Or an airline may have data on weather patterns, but the data is useless because it isn’t specific to one location. This data would be better off omitted because it doesn’t serve a purpose for the business. Once you’ve omitted useless data, you can move on to handling your data. Data can contain errors, but it’s best to leave them alone unless they’re permanent. If data is incorrect, but it’s just a one-time thing, it’s best to leave it as is. If data is inaccurate but recurring, you can fix or discard it.

Build a Data Management Roadmap

Once you know how your data is collected, stored, processed, and analysed, you can create a roadmap to help keep track of all these things. This is beneficial because it will show you what happens to your data throughout the process and where improvements can be made. There are a couple of ways to build your data management roadmap. You can go with a top-down approach by creating a data architecture diagram. Then you can go with a bottom-up approach by compiling different flow charts. Whichever process you go with, include the following: The role of data in the business, your data sources, the state of your data, the lifecycle of your data, how it is processed, how it is accessed, and how it is secured.

Integrate Technology

Ensure your analysis is thorough and accurate and that your tools are integrated and compatible. This means you should collect and store your data in a suitable format. And that it is processed in the right way. And that the right person is accessing it. It’s also essential to ensure your data is backed up and stored securely. This is especially true if you collect unstructured data like text or images. These forms of data are usually held in the cloud, so they’re likely to face some kind of outage at some point.

Answer Questions

You can begin your analysis once you’ve done all the previous steps. During this time, you should pay extra attention to detail. Make sure you’re being thorough and are following a process. Don’t rush through any part of the data analysis. This is especially true for the visualisation process. Data visualisation is the process of using visuals to represent data. These visuals can be in the form of graphs, charts, or tables. It’s essential to make sure that your visuals accurately portray your data. And that they’re understandable to your audience. Most importantly, ensure that your data analysis meets your stakeholders’ needs.

Visualise Data.

When drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-organised sets of visual data and using them as a powerful solution to emerging issues in a variety of critical sectors, including marketing, sales, customer experience, HR, fulfilment, finance, logistics analytics, and others. Data visualisation online is a powerful tool as it allows you to tell a story with your metrics, allowing users across the organisation to extract essential insights that help the organisation evolve – and it covers all sorts of data analysis. A dashboard or platform will make your whole organisation smarter and more knowledgeable.

Primary KPIs:

The Chief Marketing Officer (CMO) dashboards are an online, visual, dynamic, and interactive tool that can help them assess whether they achieved their monthly objectives. With this dashboard creator example, you can view interactive charts for monthly revenue, cost, net income, and net income per customer; all data is compared with the previous month to see how it changed. Furthermore, you can see the total number of monthly users, customers, SQLs, and MQLs to gain a clear picture and extract relevant insights or trends for your marketing reports. C-level management can benefit immensely from the CMO dashboard, as it allows them to monitor the strategic outcome of their marketing efforts and make data-driven decisions to benefit the company.

Exercise Caution

When performing data analysis, data interpretation is an essential part. It gives substance to the analytical information and attempts to draw a concise conclusion from the results. Since companies typically collect data from several sources, misinterpretation can be avoided by correctly interpreting the information at this stage.

Build a Narrative

We’ll examine how you can use these elements to boost your company – beginning with data storytelling. The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualised your most valuable data using BI dashboard tools, you should strive to tell a story with a clear beginning, middle, and end. Having done so, you will make your analytical efforts more accessible, digestible, and universal, enabling more people within your organisation to utilise your findings to their advantage.

Consider Using Autonomous Technology

The development of autonomous technologies, such as artificial intelligence and machine learning, is aiding the improvement of data analysis. According to Gartner, 80% of emerging technologies will be built on AI foundations by the end of this year. This shows the ever-increasing significance and value of autonomous technologies. These technologies are currently revolutionising the analysis industry. Examples include neural networks, intelligent alarms, and sentiment analysis.

Share the Load

Having the right tools and dashboards enables you to present your metrics in a digestible, value-driven format, allowing almost everyone in the organisation to connect with and utilise relevant data to their advantage. Today’s dashboards gather data from various sources and offer a wide range of insights in one convenient location, whether you need to track recruitment data or generate reports for several departments. In addition, these cutting-edge tools let everyone in the company connect to dashboards from various devices and share the workload, allowing for remote connection with recruitment data. Once everyone can work with data in a data-driven mindset, your business will be catalysed in ways you never imagined. Working with data, this kind of collaborative approach is crucial.

Data Analysis Tools

Using the right programs and tools for data analysis is critical to getting the best results—for example, Tableau or PowerBI. Consider 4D, the best value-for-money option on the market.

Constantly Refine Your Process

Look back at your work and consider what could be done better. Once you’ve extracted the necessary information, you should always reflect on your project and consider how to improve it. As you’ve read throughout this long list of data processing techniques, data analysis is a complex procedure that requires consistent improvement. You should always strive to do even better in the end.

Bonus Advice

The most important thing to remember when analysing data is to be patient. Data analysis can take days, weeks, or even months, depending on the project. You must stay organised and focused and follow a process during this time. Make sure you’re following best practices. You should have a clearly defined goal, know your audience, and understand what your data means. Also, be aware of how far your data goes back, how often it’s collected, and how often it is refreshed. And most importantly, know who has access to the data.

Conclusion

Data analysis is a crucial part of any data science project. It’s where all the data information from your data is made valuable and actionable. There are many different ways to analyse data, but not all are useful for every project. Some may be more suitable than others, depending on your specific needs. There are many benefits to implementing these best data analysis techniques, including better insights and more actionable data. Ensuring that your data analysis meets your stakeholders’ needs is crucial. Once you do this, your data analysis can generate insightful, meaningful insights that your business can use to make better, more informed decisions.