Data Analysis: Complete Guide to Unlock Success

Despite the massive amount of data that exists, less than 0.5% of it gets analysed (and thus, properly utilised). Data analysis is a necessary step to transform raw data into insights for better business decision-making. The process of data analysis includes ensuring that the quantity of collected data is also of good and relevant quality.

Here, we will dive deeply into absolutely everything you should know about data analysis so that you can be sure to collect, process, clean, and visualise data that will benefit your business. While the idea of data analysis can sound overwhelming, we provide general and specific information about automation tools so you can get a sense about how software can completely reshape your business strategy and data approach for the better.

Coming Up

1. What is Data Analysis?

2. What is the Data Analysis Process?

3. What are the Barriers of Data Analysis?

4. What are the Benefits of Data Analysis?

5. What are the Best Data Analysis Tools?

6. What are the Types of Data Analysis?

7. How Automation & Technology Makes Data Analysis More Effective?

8. What is the Importance of Variance Analysis?

9. Why Is Real-Time Analytics Important?

10. Wrap Up

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What is Data Analysis?

Data analysis is a process that involves cleaning, interpreting, analysing, and visualising data. This means that you are able to take raw data (from photos, text, websites, documents, and more) and leverage the information to answer questions that impact how your business operates.

Through data analysis, businesses can better serve their customers, provide greater value, decrease costs, and optimise outputs.

Since data analysis begins with data collection and ends with visualisation, it also encompasses how you store, manage, and organise data. Given the mass volume of data available to businesses these days, most organisations are opting to use automation solutions to take care of all these important considerations. (More on this to come.)

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What is the Data Analysis Process?

The process of data analysis begins with adequate thought and extends into its execution. Although data analysis sounds difficult and high-level, it can be performed with automation solutions that manage all the heavy lifting for you.

The automation solution will do the work and even create data visualisations, and your team must be involved to define the data strategy and plan before you begin collecting data.

Let’s take a look at the steps that make up the data analysis process:

Set Data Goals

Firstly, the opportunity and possibility to collect data is practically limitless in potential. This being said, it’s of utmost importance to define your data goals and objectives. By understanding the purpose and needs of your data, you can be sure to collect and analyse the right, or most well-suited data, for your intentions.

Data Collection

Once you (and your team) have discussed your business’ data goals, you can begin collecting the raw information from your audience. Given the variety of sources from which you can get data, it’s a good idea to use a tool that can collect and store data in a centralised location for you.

This way, once you know where you’re getting data, you can connect the solution and rest assured knowing that all the data will be coming into one place to be processed and reviewed. APIs and integrations help to make this doable.

Data Preparation and Cleaning

With data in a centralised location, the data processing stage can begin. This is also known as “pre-processing” because data is organised and checked for errors. There’s no need to store and use duplicate records, incomplete data sets, or data that is simply incorrect.

To reap optimal and accurate insights from raw data, this data preparation/cleaning step is necessary to remove some irrelevant records.

Data Analysis

Now, your software solution has exactly what’s needed to perform data analysis. There are various ways to analyse data, but some commonly used tools include programming languages like Python or R, to name a few.

Data Interpretation

Based on what the system turns out, you can interpret and review the data to find answers. Based on what you’re trying to express, you can choose how to display the insights from the data (for example, using words or charts).

Data Modelling & Visualisation

Since data is used for decision-making, data must be understood. Through interpretation, you get to choose the best way to communicate and share data with visualisations. It makes it easy for people to understand what’s going on in terms of patterns, trends, and facts.

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What are the Barriers of Data Analysis?

For many organisations, the idea of data analysis can seem daunting. This is because, in the past, there have been some barriers to being able to perform adequate data analysis.

Before the innovations that automation solutions have brought to the table, data analysis necessitated highly-skilled experts with the ability and technical skill to perform analysis. Not only was this costly to find, but manual data analysis was also rife with error and required a lot of time to conduct.

Furthermore, data analysis requires that the proper collection methods are used and that data is of good quality so that findings are reflective of reality.

These major challenges have been resolved with technology and data automation solutions. Thanks to machine learning, APIs, and artificial intelligence, data can be collected, stored, and analysed with high accuracy and very rapidly.

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What are the Benefits of Data Analysis?

Small and large business decisions are made on a daily basis. Having data and insights at your fingertips to support your choices makes them easier to make and more fruitful.

Data analysis provides what you need to know to better serve customers and protect business operations. Along with decision-making, it offers a way to truly understand your clientele so that you can make choices that align with everyone’s best interest.

Let’s review the benefits of data analysis in general, as well as how data analysis can positively impact the financial and banking industry specifically.

Tailored Customer Communication and Care

Big data makes it possible to get a robust view of each customer. Even when you have a large customer base, data analysis creates a way to segment and organise the needs of many into manageable buckets.

Additionally, by utilising data about one’s location, for example, you can create and offer tailored products based on needs to provide personalized customer service.

Risk Management

Data analysis can pull information from the past to forecast and predict what will happen in the future. Using predictive analysis, for example, can help organisations in the financial sector better handle their risk management operations.

Risk is inevitable, but through predictive analysis, it becomes more certain to gauge the potential likelihood and impact of a risk taking place. This way, businesses can better prepare for how they choose to deal with risk, whether it be through transference, acceptance, avoidance, or mitigation.

Decreased Fraud

When it comes to any business, fraud is a possibility. However, the financial services sector is most at risk. Through the use of data analysis, it’s easy for automation software to pick up on anomalies or errors in real-time. This can help to preclude or quickly deal with fraud.

Additionally, since all data is stored in the same system, it allows for increased transparency between departments. In this way, everyone can be aware of what’s happening and better secure transactions and information. Data analysis also helps to identify potential pitfalls in operations or vulnerabilities that can lead to fraud, which could be rectified through process improvement, for example.

Employee Satisfaction and Engagement

Employees want to realise their impact in a business setting. When they have the opportunity to leverage an automation tool that will take over the manual, tedious, and monotonous work they once had to do so that they can focus their time and energy on high-level and creative tasks, they gain satisfaction.

Additionally, with data analysis, they can better service customers’ needs, which, in turn, improves their ability to perform their job well.

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What are the Best Data Analysis Tools?

When considering what data analysis tools to implement within your organisation, you have a lot of aspects to factor in. From budget to scalability and more, you’ll want to perform adequate research before introducing a software system for your team to use.

We’ve compiled lists comparing the best data analysis tools before,  including the best data analysis tools for CFOs, but we will summarise some findings here for easy access:

Sisense

Sisense is a customisable analytics software that detects patterns and trends using NLG (natural language detection). Machine learning helps to spot errors and users can choose from various data visualisation options. However, the software is relatively inflexible and uses a single server.

Zoho Analytics

Zoho Analytics is a cost-effective option for business users who are looking for a business intelligence tool on a budget. However, this means that there are some limits in its scope. For example, users are limited on the number of data imports and query tables. There is also an enterprise solution, but that comes at a higher cost and may require some coding to set up specific reports.

SolveXia

Solvexia is a human analytical automation tool that’s purposefully designed for finance teams and businesses to transform raw data into insights easily. Information can be stored in the cloud and is easily accessible to any team member with access in the system.

Real-time dashboards are customisable and easy-to-use through the no/low code platform. Users can design processes to run automatically with a pre-built library of drag-and-drop functions. Additionally, mass amounts of data can be collected, cleansed, transformed, and analysed in seconds.

Qlik

Qlik offers a data analytics solution with easy-to-build visualisations and dashboards. The software supports multiple data sources, and users benefit from its drag-and-drop functionality. However, connectors can be expensive and the system does not have full governance capabilities.  

This list is abbreviated to provide some insight into the tools on the market. When performing research, it’s best to keep in mind the following list for what you should be looking for.

How to Choose Data Analytics Tools

When comparing data analytics tools, keep in mind:

• The security features
• Product cost
• Customer service and availability
• The amount of training required
• How the user interface is set up
• Automation abilities
• Report scheduling
• Vendor reviews
• Customisation
• What software and hardware is needed to use the tool

What are the Types of Data Analysis?

Whether performed manually or with the help of an automation tool, there are several different ways to conduct data analysis.

Let’s take a look at the various methods and what they can be used for:

Text Analysis

Text analysis, which is also known as text mining or data mining, takes large sets of textual data and organises it to be used. For example, a system can analyse text to deduce what kind of sentiments the authors feel.

So, a business can sense whether a customer feels angry, satisfied, happy, neutral, etc. based on the word choice they’ve used. It can be useful in survey responses, social media communications, product reviews, articles and more.

Statistical Analysis

Statistical analysis can be broken down into either inferential analysis or descriptive analysis. Inferential analysis aims to confirm or deny hypotheses that are created by people to describe what happened. Inferential analysis depends on a sample that must be taken randomly and be used to represent a larger population.

This type of analysis is common in market research. Descriptive analysis describes and summarises patterns and findings from existing data and calculations. While it can’t be used to describe why something happened or how, it can be useful for KPI dashboards and providing a baseline by which to judge data.

Diagnostic Analysis

Diagnosis analysis is as it sounds because it looks to diagnose and answer why something took place. It’s also known as root cause analysis. It is conducted by using data to derive patterns and assess anomalies.  It’s best utilised in its application to understand customer behavior.

Predictive Analysis

Using historical data, predictive analysis looks to provide ideas about what may happen in the future. For example, a business can use predictive analysis to forecast sales. While guesswork is involved, the more data a business has, the better the predictions can be.

Prescriptive Analysis

Representing the most advanced type of analysis, prescriptive analysis pulls together all your data to perform analysis and model outcomes. This gives businesses what they need to know to be able to make the most informed decisions and guide their actions.

These models offer a look at the various scenarios that could take place, along with the probability that they will. At the heart of prescriptive analysis sits artificial intelligence and machine learning.

Quantitative vs Qualitative

Different types of data can be used to perform analysis. The two main categories of data are qualitative and quantitative. Qualitative data is numerical in nature and can be counted. Qualitative data, on the other hand, is descriptive and is based on characteristics rather than numerals.

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How Automation & Technology Makes Data Analysis More Effective?

Data changes quickly based on user behavior, as well as external factors. If you have an immense amount of data that is stored, but never updated, then you’re using old facts to make current decisions. The way by which you collect and cleanse data plays a major role in its analysis.

To overcome the timing factor of having to work with data for it to be relevant, you’ll want to conduct analysis quickly once you have cleansed data available. Automation solutions and technology make this achievable.

‍Automation solutions can pull data in real-time to collect raw data that is complete and accurate. Through the use of real-time data, software systems can perform analysis on the spot so that you never miss a beat and can make decisions in a timely manner.

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What is the Importance of Variance Analysis?

There’s one major type of data analysis that seems to be ubiquitous in most businesses and that is variance analysis. Variance analysis is essentially the difference between estimates and actuals. It plays a large role in budget and provides a way for businesses to assess if they are performing well or poorly.

Importance of Variance Analysis

The benefits of variance analysis are wide-ranging. Variance analysis allows for a business to better plan and manage their budget. It also helps to control where money is allocated and to which department and why based on using historical data and insights to better understand ROI.

By being able to set benchmarks and realistic expectations, business leaders can monitor their operations more wisely.

Types of Variance Analysis

There are different ways to conduct variance analysis. The types of variance analysis most commonly used include: purchase price variance, labour efficiency variance, variable overhead efficiency variance, labour rate variance, fixed overhead spending variance, and material yield variance.

How Automation Can Help with Variance Analysis

While variance analysis proves to be highly useful, there are some hurdles to overcome. Luckily, automation tools help to resolve the challenges.

Variance analysis is based on inputs and outputs, but there may be reporting delays that can lead to the failure of being able to catch red flags in time. Or, the information needed to perform variance analysis may not be readily available to a finance team in which case the analysis won’t be feasible.

Automation solutions resolve these issues by connecting all data sources in real-time so there’s never a piece of information that is delayed or missing. Additionally, with real-time dashboards and alerts, anyone with access control to the system can view what they need to and have all the data ready to process in seconds.

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Why Is Real-Time Analytics Important?

Throughout this article, we’ve brought up the concept and importance of having real-time analytics several times.

Challenges of Real Time Analytics

Real-time analytics refers to the ability to view, analyse, and assess data as it enters the system. In order for real-time analytics to work, an organisation must first be able to agree upon the definition of what constitutes real-time.

Additionally, the data tool used must be able to process data quickly and scale as it grows. Lastly, for the system to work, it must be implemented properly, which is why many organisations choose tools that are no/low code (like SolveXia) so that it can be deployed out-of-the-box without needing an IT team.

Benefits of Real Time Analytics

By utilising real-time analytics, businesses gain an immense competitive advantage. When you have the knowledge to review benchmarks and see how your organisation is performing as it happens, you can make strategic decisions without delay to reap immediate rewards.

Furthermore, if you want to roll-out a small or big change, you can test it out first and see how the changes perform before causing damage that is uncontrollable or irreversible.

Real-time analytics can be applied to monitor customer behavior and allows business leaders to visualise data as it enters the system. What’s more is that rather than having to manually transform raw data to gain insights, machine learning comes to the forefront and provides more accurate outputs as more information becomes available.

Tools equipped with real-time analytics lead to cost-savings by precluding the wasted time, energy, and effort that would otherwise be applied to collecting data that is irrelevant.

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Wrap Up

Data is as numerous as the stars in the entire universe. But, what’s most interesting about the vastness of all the data is the impact it can have on businesses when it is being utilised properly. In fact, for the typical Fortune 1000 company, it’s estimated that having just a 10% increase in data accessibility can result in an additional net income of $65 million.

While the monetary impact of data analysis and accessibility varies on a business-to-business basis, there is absolutely no doubt that a real-time data analytics and automation solution can only allow your business to perform better.

By transferring raw data into insights automatically and scheduling automated reports, automation software provides business leaders and employees with all they need to know at all times.