Being a data-driven business is important, but what does that mean exactly?
Data-driven businesses make decisions based on data, which means they can be more confident that their actions will bring success since there is data to support them.
So what is data analysis?
In simple words, data analysis is the process of collecting and organizing data in order to draw helpful conclusions from it. The process of data analysis uses analytical and logical reasoning to gain information from the data.
The main purpose of data analysis is to find meaning in data so that the derived knowledge can be used to make informed decisions.
How is data analytics used in business?
Data analytics is used in business to help organizations make better business decisions. Whether it’s market research, product research, positioning, customer reviews, sentiment analysis, or any other issue for which data exists, analyzing data will provide insights that organizations need in order to make the right choices.
Data analytics is important for businesses today, because data-driven choices are the only way to be truly confident in business decisions. Some successful businesses may be created on a hunch, but almost all successful business choices are data-based.
What are examples of data analysis?
Data analysis is a somewhat abstract concept to understand without the help of examples. So to better illustrate how and why data analysis is important for businesses, here are the 4 types of data analysis and examples of each.
- Descriptive Analysis: Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, and more.
- Diagnostic Analysis: Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason. A business may see that leads increased in the month of October and use diagnostic analysis to determine which marketing efforts contributed the most.
- Predictive Analysis: Predictive data 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 has gone up 20% every year for many years, we would 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.
- Prescriptive Analysis: Prescriptive data analysis combines the information found from the previous 3 types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where the data-driven choices are made.
These 4 types of data analysis can be applied to any issue with data related to it. And with the internet, data can be found about pretty much everything.
But how do you get that data from the web into a usable format for your team to derive insights from? We’ll tell you in the next section about data analysis methods.
What are the methods of data analysis?
Since our expertise at Import.io is in data from the web, we’ll discuss the methods of analysis for data from the web. The steps leading up to web data analysis are: identify, extract, prepare, integrate, and consume. In traditional manual data analysis each of these steps take a substantial amount of time to perform.
Identifying the data you need can be challenging with the vast 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.
Extracting data from the web has traditionally required a web scraper that is coded to scrape data from a certain website according to certain parameters. For example, traditional Twitter sentiment analysis might use a web scraper that is coded to scrape tweets that mention your brand name. Creating and running these web scrapers takes time. And even once it’s finished, it’s possible the data could be incomplete or inaccurate. The parameters for which tweets will be scraped could be missing a rule, resulting in missing crucial data.
Preparing data for analysis requires many steps that each take a long time to do manually. The data must be cleansed, standardized, transformed, etc. This is where a lot of the outdating happens. By the time the data is ready, it is not as recent and there is 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.
How to make data analysis more efficient for your organization
You know that the main purpose of data analysis is to make business decisions that are backed by data, so why would you let this process take so long that the insights are outdated by the time you get them?
Import.io knows that traditional web scraping and data analysis methods are time consuming to the point where their value is diminished by the time they take. That is why we created Web Data Integration.
Web Data Integration automates all 5 steps of web data analysis, allowing you to get insights from data while it’s fresh. Rather than outdated insights as a base for your business decisions, you can use insights from real-time data.
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, WDI has built-in quality control, so the data will always be complete, accurate, and reliable.
Make data analysis more efficient for your organization by eliminating inefficient processes. Get data insights in minutes rather than hours, days, weeks, or months.
Contact a data expert to learn how your organization can utilize Web Data Integration.