In the world of data processing there is one saying
“Garbage in – Garbage out”
It means your results are only as good as the data you’re using to get them. Incorrect or inconsistent data leads to false conclusions and false conclusions have bad impact on your business. This is true if you are a researcher, small business owner or a large enterprise.
If you make your decisions based on incorrect or inconsistent data, you can be sure that the business results will not be good. You may lose clients, business opportunities, time and money.
Data cleansing is referred to as data cleaning or data scrubbing. Data cleaning are steps to clean data before using data for analysis. This is accomplished by removing or modifying data that is incomplete, incorrect, irrelevant, duplicated or inaccurate. This technique minimizes the risk of wrong or inaccurate conclusions or results.
Steps for cleansing data :
The techniques used for data cleaning may vary according to the types of data your company stores.
Following are the basic steps for cleaning data :
#1. Removing duplicate or irrelevant data :
Duplicate observations will happen most often during data collection. When you combine data sets from multiple places or receive data from clients or multiple departments, there are chances of creating duplicate data. Deduplication of data has to be considered in this process.
Irrelevant data are those observations that do not fit into the specific problem you are trying to analyze. For example if you are analyzing data regarding young customers, but your data set includes older generations, then in such case you have to remove those irrelevant observations. This can make analysis more efficient.
#2. Structural errors :
There are different types of structural errors from typos to inconsistent capitalization. This can create problems when categorizing or grouping data, so they need cleansing. For example “gender” is a categorical variable, usually of two classes, male and female, but you may encounter more than two different categories of the variable such as : *m; *male; *F; *fem. Data cleansing helps to recognize such mislabeled or inconsistently capitalized classes. Also review you data collection and data transformation process to prevent data issues.
#3. Handling missing data :
‘Missing data’ is a tricky issue. Just be clear that you cannot simply ignore missing values in your data set. Deciding whether to drop, impute or flag missing data. Using/not using the missing data affects the accuracy of your analysis.
- Imputing : It means working out the missing value based on the other data. The pattern will be re-created that the observations have already created.
- Dropping : Dropping observations that have missing values when analyzing statistical data. Study shows dropping is better than imputing values.
- Flagging : Flagging means telling your ML algorithm about any missing value. Flagging is done when the data is missing continuously, rather than randomly.
#4. Filtering outliers :
Another thing you have to remember during the process of data cleansing are outliers. Outliers are values that are totally very different. For example, you are researching your app user’s age and find entries like 72 and 2. The former might be a senior citizen who is up to date with the technology. But the latter is mostly likely an error since toddlers don’t use apps. If an outlier proves to be irrelevant for analysis or proves to be a mistake, it should be removed, in doing so you can increase the performance of the dataset.
#5. Standardization of data :
Cleansing your data includes standardizing it, to have a uniform format for each value. For example, all values of height should be in the same unit, so you may need to convert from feet to meters or vice-versa, to achieve uniformity.
Make sure that you use a standardized unit of measurement. These include weight, distance and temperature. As for dates, choose either the USA style or the European format.
#6. Validate the data :
In the conclusion of the data cleaning process, you should be able to answer these questions:
- Does the data make sense?
- Is the data is appropriate with regard to its field?
- Does your data help to develop your next theory?
False results, as a result of incorrect data, may inform poor strategy and decision making. Conversely, data cleansing can help achieve a long list of benefits which may lead to maximize profits.
Pull up :
Monitoring errors and better reporting to see where errors are coming from, Making it easier to fix incorrect or corrupt data for future applications. Clean data helps in taking effective and efficient decisions, resulting in increased productivity and revenue. Using tools for cleansing will make for more efficient business practices and quicker decision-making. Therefore cleansing data from time to time is advisable, for a good result.