What is Data Analysis?

data analysis

The practice of extracting information from data to guide better business decisions is known as data analysis. Five iterative phases typically comprise the data analysis process:

  • Choose the data you want to examine.
  • Gather the data
  • Clean up the data before analysis.
  • Study the information
  • Interpret the analysis’ findings

Depending on the question you’re attempting to answer, data analysis might take on various shapes. More information on different data analysis types is available here. In a nutshell, descriptive analysis explains what happened, diagnostic analysis explains why it occurred, predictive analytics offers future projections, and prescriptive analysis generates actionable recommendations for what to do.


The digital systems in our surroundings are being flooded with more and more data every second. Your phone or laptop is always creating and consuming data, whether you’re using the internet search bar to look for something new or simply leaving your device (with the internet turned on) on the table. The sum becomes limitless when the quantity of data we’re referring to is multiplied by the size of the world’s population (ANTHONY, 2020).

Have you ever considered the ultimate purpose of all this data flow in our data-driven culture, and how we can be more conscientious in how we act? Data analysts are crucial since they are the experts that assess this data and its potential uses. There is no task that data analysis does better than evaluating how well your goods or company are performing in the marketplace, you can choose the markets and clients you will focus on after you are aware of which products are appropriate for specific clientele (Sandra, 2018).


1. Gathering relevant data

Every part of business has data, which frequently overwhelms personnel. The abundance of data from various channels makes it challenging for employees to dig deep and find the crucial insights. And as a result, they wind up evaluating the data that is easily accessible rather than the data that actually brings value to the company.

2. Choosing the appropriate tool

The abundance of technologies on the market today presents the second most frequent challenge in data analytics.

Which is better for data storage, MongoDB or Cassandra? Should you use Microsoft’s Power BI for data analytics, or is RapidMiner a better option?

There’s a danger that you’ll waste time, energy and resource if these questions aren’t answered appropriately.

3. Combine information from several sources.

The sources of the data are dispersed and fragmented. You will need to gather information, for instance, from your website, social media pages, CRM portals, financial reports, emails, the websites of your rivals, etc. Naturally, the majority of these reports will have different data formats.

One of the common obstacles in data analytics is combining and evaluating them in one location. If carried out manually, it can end up being more difficult. Additionally, it raises the possibility of inaccuracy, rendering the data inaccurate.

4. The level of the data’s quality

In data analytics, inaccurate data might be more damaging than anything else. The output can never be trusted if the quality of the raw data is poor and inaccurate. Data input errors, often known as human errors, are one of the main causes of erroneous data.

The difference in the data is another factor contributing to its poor quality. Asymmetric data will result if your data operator modifies one system but forgets to modify another system exactly as they did in the first.

5. Fostering a data culture within the workforce

The largest barrier to becoming a data-driven corporation, rather than technologies, is an organization’s culture, claims a research. A pitiful 9.1% of executives have identified technology as a barrier to effective data analysis.

Although senior management frequently recognizes the value of data analysis, they frequently fail to provide their staff with the necessary assistance. One of the biggest problems with data analytics is the constant pressure and lack of support from upper- and lower-level employees.

6. Data protection

Businesses begin concentrating on storing, comprehending, and analyzing big data once they recognize how important it is. They frequently fail to consider the dangers that could arise from the massive data sets’ security and privacy.

One of the most terrifying challenges in data analytics is securing the data that belongs to your firm. Unprotected data sources can easily serve as a hacker access point.

7. Data visualization

Until the numbers tell a story, data analytics has no relevance for you or your stakeholders. After all, you spend the time, money, and effort gathering and protecting the data so that you can make wise decisions and achieve your ROIs. Data visualization is therefore difficult and essential to data analytics.


It is simpler for us to deal with issues when we are aware of them. You may begin putting data analytics into practice in a more systematic manner now that you are aware of the problems that firms confront with them and their remedies. You can become a data analyst for free.

Finally, none of the difficulties with data analytics are severe enough to prevent you from using big data’s advantages.


Sandra, H. (2018) ‘5 Reasons Why Data Analysis is Important for Every Business’ Available at: 5 Reasons Why Data Analysis is Important for Every Business – Business Partner Magazine  (Accessed on 17 September 2022)   



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