Analyzing Survey with Clean Data for Sound Business Decision

In an ever-changing world, data analysis is a key method for businesses to gain information to succeed in the marketplace. Entrepreneurs need to dig deep into relevant data and understand their consumers’ behavior and patterns on the market.

For many entrepreneurs, deciphering data and converting it into stirring practice has been a significant challenge, many of whom lack the skills needed to carry out their research with accurate data. Before making any decisions about your brand, it is necessary to purge the data. Lack of accuracy and unreliable data can be a problem for companies to make informed decisions. In this respect, analyzing clean data is required.

This article will provide a brief overview of data analysis with clean data for business decisions.

What is Data Analysis?

“Data analysis is a process of inspecting, cleansing, transforming, and modeling data to discover useful information, informing conclusion and supporting decision-making,” according to Wikipedia.

It is vital to access precise information before one can analyze the data. That will depend on your research objective and survey questions. For example, are you looking to gather information on brand awareness? So, you conduct your survey, and the data is now available. It’s time to analyze your data, but before doing so, there are parameters you should consider.

Using Data to Make Sound Business Decisions

It is important to make sound business decisions around quality data. Sometimes, however, you may hear of “dirty data,” which refers to unreliable, incomplete, conflicting, or duplicate data. Dirty data can affect your brand if used to make business decisions.  It can be costly, deplete capital, resulting in wasted opportunities affecting operational efficiencies. Dirty data such as your CRM and sales data can be found in your company systems and processes.  So, what causes dirty data? It can result from data input, lack of a sound strategy for data collection, or failure to understand the value of quality data for decision making.

“I think you can have a ridiculously enormous and complex data set,  but if you have the right tools and methodology, then it’s not a problem [Therefore, it is imperative to invest in data cleaning].”
– Aaron Koblin, entrepreneur in data and digital technologies.

Using Clean Survey Data

Clean data allows companies to make strategic decisions to capitalize on the marketplace. However, there are both good surveys and bad surveys. That’s why it is critical to scrub your data for accuracy. It will help brands increase profitability, cut costs, lower risk, strengthen relationships with customers, and make the most of the information.

There are several ways to clean your survey data. However, clean data should be:

Complete.  That means the data set incorporates all pertinent information. There is no missing or erroneous information in the survey data set.  You should filter your data to ensure completeness and delete anyone that does not meet the actual condition.

Unique.  There is no existing relationship between the data in the data set.  That is to ensure that the same respondent does not take the same survey.  For authenticity, remove duplicates and respondents who complete the survey using the same IP address. 

Timely.  Respondents should not spend excessive time taking the survey, neither should they rush through the survey, without answering adequately the questions.  Consider calculating the average survey response time and remove outliers; anyone spending too much time or insufficient time to complete the survey.  Timely data should be available, updated, and accessible for business decisions. 

Valid and Accurate.  It means that the data in the data set does not interfere with other fields, is reliable and factual, and will measure the intent.  You could identify respondents who mostly or strongly agree to the appropriate questions. Also, those who are neutral and those who mostly or strongly disagree and remove those respondents, as this will skew the data.

Consistent. Review the data to ensure that everyone passes the quality checks.    Consistent data should adhere to respondents passing the conditions set and is the same across different sets of data or within the same collection of data.

After completion of the survey, expropriate the poor survey data using the business criteria established so it is incorporated consistently.

Conclusion

The collection of survey data is a powerful method for entrepreneurs to analyze and make successful business decisions. Clean and accurate data is suitable for sound strategic accord in achieving productivity and reducing business risks. Whereas dirty data includes inaccurate details and adversely affects those decisions. Dirty data can also be costly and laborious.

So, what’s next!

Although getting clean data for your analysis is vital, it is also necessary to understand the type of data analysis you will undertake for optimum business decisions.

What type of data analysis will you perform now?

Learn more about data analysis and the types you can perform for your business. 

Please also take a look at my last article. 

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