The world is going digital and using data analysis in business is very important because that is the only way you get to make seemingly real decisions. Your ability to collect data, analyze it, obtain a level of accuracy doesn’t only boost the confidence of your business but also increases your productivity. When you try to make a comparison, you will see that data-driven businesses top the list in today’s market.
As a beginner or small business owner, it is ideal you grow your business based on real facts or information. Many start-ups do not have any knowledge on how to make decisions based on available data. But in this article, we will discuss everything you need to know about data analysis and why you need it in your business.
In simple words, this is the process of collecting and organizing data in order to get helpful conclusions from it. The process of data analysis uses analytical and logical reasoning to gain information from the data. Also, the purpose of data analysis is to find meaning in data, then using the derived knowledge to make decisions.
How to use Data Analysis in business
You can use this tool in several ways as far as the growth of a business is concerned. Also, you can use it for decision making, product choice, customer satisfaction, staff experience, and more. You can also use it to provide a wealth of opportunities for businesses that are ready to digitalize. In addition, you will also get first-hand information about your customers, how they feel about it, and their level of satisfaction. This will help you personalize and customize their experiences and build long-term relationships. Also, you can use this tool to evaluate your staff experience, because that will determine how much energy they put into work to grow your business.
Note that, insights gotten from qualitative data analysis gives room for more powerful marketing. You can also use it for strategic planning, to determine your marketplace, income and total expenditure. Data analysis is a tool every business needs in their toolbox, how to use it shouldn’t be a problem. You can hire an expert who has experience and be rest assured to get the most profitable insights to work with.
Types of data analysis
Data analysis is a somewhat abstract concept to understand without the help of examples. So to better explain how and why this tool is important for your business. There are several types of data analysis techniques that exist based on business and technology. However, the major types of data analysis are:
- Text Analysis
- Statistical Analysis
- Diagnostic Analysis
- Predictive Analysis
- Prescriptive Analysis
Text Analysis is referred to as Data Mining. This method is employed to discover a pattern in large data sets using databases or data mining tools. You can use it to transform raw data into business information. Business Intelligence tools are present in the market which you can also use to take strategic business decisions. Overall it offers a way to extract and examine data, derive patterns, and finally interpret data.
Statistical Analysis shows “What happen?” by using past data in the form of dashboards. It includes collection, analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.
- Descriptive Analysis
This analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.
- Inferential Analysis
It analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.
This type of data analysis aims at determining the reason behind an occurrence. Once descriptive analysis shows that something negative or positive happened, the job of diagnostic analysis is to figure out the reason. If you notice that business leads increased in the month of October, you can use diagnostic analysis to determine which marketing efforts contributed the most. Also, you can use diagnostic analysis to understand the behavioral pattern of data in your business. You can identify problems with this analysis.
As the name implies, predictive analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, to predict next year’s revenue, data from previous years will be analyzed. If revenue goes up by 20% every year for many years, you can predict that revenue next year will be 20% higher than this year. This is a simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales forecasting, or qualifying leads. Forecasting is just an estimate. Its accuracy however depends on how much information you have.
Prescriptive data analysis combines the information from the previous three types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where data-driven choices and decisions are made.
The above mentioned types of data analysis can be applied to any data-related issue. And with the internet, you can find data about almost everything.
But how to get that data from the web into a usable format is the big deal. For your team to derive insights from data, you must follow the ideal data analysis process.
Data Analysis Process
This involves the gathering of necessary information by using proper application or tool which allows you to explore the data and find a pattern in it. With that, you can take decisions, or you can get satisfactory conclusions.
This process consists of the following phases:
- Requirement Gathering
First of all, you have to think about why you want to do this data analysis. After that, you consider all you need to find out the purpose or aim of doing the analysis. You hae to decide which type of data analysis you want to carryout. In this phase, you will have to decide what to analyze and how to measure it. You also need to understand why you are investigating and what measures you have to use to do this Analysis.
After requirement gathering, you will then get a clear idea about the things you have to measure and what should be your findings. This is the time to collect your data based on requirements. Once you collect your data, remember that you must process and analyze the collected data must. As you collected data from various sources, you must keep a log with a collection date and source of the data. That will give you a clear description of what you are doing.
Now whatever data you collect may not be useful or relevant to your aim of Analysis, hence it should be cleaned. The data you collected may contain duplicate records, white spaces or errors. The data should be cleaned and error free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.
Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you will definitely find that you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements.
After analyzing your data, then it is time to interpret your results. You can choose a specific way to express or communicate your data analysis. You can either use simple words or maybe a table or chart. Then use the results from your process to decide your best course of action.
Data visualization is very necessary in your day to day business life. They often appear in the form of charts and graphs. In other words, data shown graphically is easier for the human brain to understand and process it. You can use data visualization to discover unknown facts and trends. By observing relationships and comparing datasets, you can find a way to find out meaningful information.
Methods of Data analysis
The methods of analysis for data from the web is quite something different on its own. We will discuss the steps leading up to web data analysis. They include:
However, in traditional manual data analysis each of these steps take a substantial amount of time to perform.
The method of identifying the data you need can be challenging with the large amount of data on the web. You may choose a data source that isn’t reliable or miss crucial data sources that should be part of your research. Reliable and complete data is necessary for accurate data analysis. Also, this method requires extreme carefulness so you will identify the right data needed for your analysis. Misplaced data can result to wrong information.
Extracting data from the web traditionally requires a web scraper that is coded to scrape data from a certain website according to certain parameters. For example, traditional Twitter sentiment analysis can use a coded web scraper to scrape tweets that mention your brand name. Creating and running these web scrapers takes time. And once it finishes, the possibility of an incomplete or inaccurate data is very high. The parameters for which tweets will be scraped could be missing a rule, resulting in the absence of crucial data.
Preparing data for analysis involves many steps that requires a lot of time especially when you have to do it manually. Here, the data must be cleansed, standardized, transformed, etc. This is where a lot of the outdating happens. By the time such data is ready, it is not as recent and there is already a newer data out there.
Integrating the data with your data analysis software can be an issue depending on which software your organization uses. And it needs to be integrated so that it can be consumed. That is to say, without integration, such data can’t be consumed.
How to make data analysis more efficient for your organization
Remember that the main purpose of data analysis is to make realistic business decisions with data as your basis. So do not let this process take so long that the insights are outdated by the time you get them. Many companies know that traditional web scraping and data analysis methods are time consuming to the point where their value an diminish. That is why there is something like Web Data Integration.
Web Data Integration automates all the five steps of web data analysis. This allows you to get insights from data while it is still fresh. Rather than getting out-of-date insights as a base for your business decisions, you can use insights from real-time data.
In addition, Web Data Integration is not only quicker than traditional web data analysis, but is also more accurate and reliable. Rather than using hand-coded rules to extract the web data, you can use the built-in quality control. With that, your data will always be complete, accurate, and reliable. Furthermore, you can make data analysis more efficient for your organization by eliminating inefficient processes. Get data insights in minutes rather than hours, days, weeks, or months.
It is not advisable to base your business decisions on guess work. No business thrives with guess work. If lack experience in data analysis, you can hire someone, an expert to do the job for you. In cases where you do not have the finance to hire a data analyst, you can use enroll in free online courses while you still run your business. You can also use software analysis tools, ask questions and be open to learning.
Do well to leave your contributions and questions in the comment section.