What is data analysis

Data analysis is a key business process. It involves the cleansing, transformation, modeling, and inspection of data to retrieve information crucial to decision-making for businesses. Data analysis basically involves processing data, facts, and figures from the past to make better decisions for the future. Many businesses outsource their requirements to a data analysis company.

Big organizations deal with hundreds and thousands of customers and clients over years, collecting all their data – such as their location, service provided, transactions made, customer feedback, and so on. An example of data analysis would be to identify the location most of your customers are based out of, and targeting those areas for future sales.

While some companies have an in-house data analysis team, many organizations prefer outsourcing data analysis services to save time and money and get specialized services. Since there are specialized organizations providing such services, it is always advisable to outsource data analysis services to such organizations.

How data analysis can help your business

Data analysis can really help businesses succeed in today’s digital age. Regardless of your broader business domain, it is imperative that you are competing with thousands of organizations worldwide. Data analysis can help study trends and design strategies for the future.

1. Study customer trends

Your first-hand customer data is a golden resource for data analysis. Many individuals and companies pay millions of dollars to get hold of first-hand customer data for all kinds of purposes. The main reason behind this is the honesty and authenticity involved with this kind of data. Paying patterns, peak seasons, reasons for cancellation and refunds, and many such trends can be studied using data analysis.

2. Predict future trends

Data analytics solutions help predict future trends on the basis of customer data. For example, if you own a hotel or airline booking website, data analysis would help you reach a conclusion that a substantial chunk of your customers prefer booking for travel and accommodation around Christmas and New Year. You know that is a regular pattern and you can now design your strategy accordingly.

3. Design business strategy

You can create business strategies from the data derived using data analysis. Continuing from the last example, you can introduce big discount packages and start targeting customers towards the end of the summer and during the fall time. You can have special discounts around Halloween, Thanksgiving Day, and throughout December. While this is a very generic example, you can design business strategies by analyzing your own business data.

4. Increase business productivity

A good business strategy always leads to an increase in productivity and sales. Again continuing from the above example, introducing discounts during peak season could lead to bulk sales. While customers would get their services at lower prices, a huge number of customers means increased sales and profits.

It is important to study some of the most renowned and widely accepted data analysis techniques. After gaining knowledge about the various data analysis techniques, you will be able to choose the technique(s) that suit your business best. You can then apply these techniques and derive the best ones as you try different ones.

Data analysis techniques

Various data analysis techniques have been derived by experts after years of working on huge amounts of data. Some of the most useful data analysis techniques are text analysis, statistical analysis, descriptive analysis, inferential analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. You can outsource data analysis services to help identify the best data analysis techniques for your business model and goals.

1. Text Analysis

Text analysis is one of the most crucial techniques used in data analysis. While there might be many numerical analysis tools and techniques you already know of, analyzing subjective text can be difficult – especially in the case of big data. Natural Language Processing (NLP) helps organize unstructured text data to derive qualitative insights that could be crucial to your business.

For example, your customer feedback forms might include multiple paragraphs and huge writeups. Going through each feedback manually could take years, but data analysis helps you derive the pain areas and sentiments of the customers on the whole. Data analysis could help you derive how many of your customers are satisfied, dissatisfied or providing constructive criticism.

2. Statistical Analysis

Statistical analysis is the most common data analysis technique but is equally crucial. Processing your data into graphs, tables and charts shows you exactly the direction in which your business is heading. Besides, big data can be analyzed in complex ways to study insights that might not be visible distinctly.

For example, a business selling multiple products can derive its most profitable products after adding discounts using statistical analysis. Besides analysis and interpretation, the statistical analysis also involves modeling and presentation of data. Two major types of statistical analysis are descriptive analysis and inferential analysis.

3. Descriptive Analysis

Descriptive analysis is the more common form of statistical analysis. It involves the quantitative analysis of data. Existing data is used to calculate several parameters, such as percentage, median, mean, mode, range, and frequency.

Revenue reports and dashboards can be generated using descriptive analysis. Descriptive data helps derive absolute numbers and figures, but not the reason for the generation of those numbers.

4. Inferential Analysis

The inferential analysis is the part of statistical analysis that continues from the descriptive analysis. Statistics of entire populations, customers, and client base can be compared using inferential analysis. Comparative analysis is an integral part of the inferential analysis.

For example, while analyzing a full population, the comparison between the data of young and adult populations is a part of the inferential analysis. The inferential analysis is of great help during market research.

5. Diagnostic Analysis

Diagnostic analysis or root cause analysis is used to derive the reasons behind particular trends. The results of statistical analysis are used to derive the root cause by studying regular trends as well as deviations. For example, if your travel bookings declined during February and March, it could be because of the end of the Holiday season or because of a decline in discounts. Diagnostic analysis helps you study the correlation between possible causes and data points.

6. Predictive Analysis

Predictive analysis, as the name suggests, helps predict future events. Demographic and purchase data is analyzed to predict future action. For example, an increase in discounts, as well as a customer’s buying capacity, can probably help your sales as well. The predictive analysis involves guesswork, but more data would lead to better predictions.

7. Prescriptive Analysis

The prescriptive analysis is more complex and helps you identify the best action to take to overcome an issue. Data analytics service providers perform prescriptive analysis by utilizing the insights from text, statistical, diagnostic, and predictive analysis to make the best decision. Handling of data is key to this technique as well as all the other techniques mentioned above.

Best Data analysis tools

Thanks to technology and the data revolution, we have dozens of highly advanced and sophisticated tools to derive data analytics solutions. These tools can perform multiple functions and are regularly updated to meet current business requirements. As a business owner, it is important for you to identify and use data analysis tools that are most likely to help you achieve your goals.

While there are many tools available in the market today, we will discuss six of the most renowned and useful tools. These are Excel, R and Python, SAS, Power BI, Tableau, and Apache Spark. We will have a look at some of the key features of each tool.

1. Excel

As easy as it might sound, Excel can perform highly complex functions that can help in key business areas. Some of the main features of Excel are VBA, PivotTable, form creation, etc. Excel is one of the best tools for small and medium data. The best thing is that with the help of plugins, it can handle big data quite efficiently as well.

2. R and Python

R and Python are robust and flexible tools created to equip data scientists. Many customized functions can be performed using R and Python. The tools have smooth learning curves, create great graphs and data presentation is quite smooth. The data derived from these tools is advanced and high level and can be easily shared with colleagues.

3. SAS

SAS is statistical software. Mostly used for predictive analysis and data management, it has an easy interface and is a great tool for interactive dashboards and reports. Some of the world’s biggest tech companies, search engines, social media websites, OTTs, pharmaceutical companies, banking companies, and weather forecasting agencies use SAS. Some examples are Apple, Google, Facebook, Twitter, Netflix, and Novartis, and Covance.

4. Power BI

Yet another powerful data analysis tool, the best feature of Power BI is its frequent updation. PowerPivot and Dax language help implement the highly complex advanced analysis. Another great feature of Power BI is its adaptability to different business models and its ability to provide all types of data solutions.

5. Tableau

Tableau is a good data analysis tool for data cleansing, intelligent analysis functions, operation, and visualization. Tableau easily handles huge amounts of data and is mobile-friendly as well. Perhaps the best thing about Tableau is its visual aspect. Any good data analysis company can help you understand and implement Tableau.

6. Apache Spark

Apache Spark is best suited for big data analysis and is used by researchers and developers. It is open source and free to use. It can run on most platforms such as Apache Mesos, Hadoop, etc. It includes libraries such as GraphX, MLlib, SparkSQL, and SparkStreaming for various types of data processing. Many widely used app companies use Spark.

Outsource data analysis services

As an organization working on several projects, you have to work on huge amounts of data. Handling this could be a tiring ordeal, but you can now outsource data analysis services to companies and experts that have been handling big data for years now.

SuntecData is one of the leading data analytics service providers in the market today. They provide the best solutions with customized packages and the least turnaround time. You can get a free quote by emailing all your requirements to info@suntecdata.com.

FAQs

1. What is data analysis? How is it useful for businesses?

Data analysis is basically the process of utilizing and processing data to make decisions on the basis of its results. All major organizations in every industry domain use data analysis. Data analysis helps study customer and user trends, predict future trends, design a robust business strategy and increase business productivity.

2. What are the various data analysis techniques?

There are various techniques to approach data analysis. The technique you use depends on the nature of the data and the requirement. Some of the most widely renowned and commonly used data analysis techniques are text analysis, statistical analysis, diagnostic analysis, descriptive analysis, inferential analysis, prescriptive analysis, and predictive analysis.

3. What are some of the top data analytics tools?

There are many really useful data analytics tools in today’s time, many of which are adept at processing huge amounts of data. Some of the top Data Analytics tools in 2021 are Excel, R and Python, SAS, Power BI, Tableau, Apache Spark, Rapidminer, Knime, etc.