In the world of digital marketing, you will hear over and again that content is king. In the world of business, especially for a startup, you will hear that location is everything.
But in today’s digital world of business development and growth, data is king.
Why did I say so?
With data, you can analyze and predict the outcome of trends in the marketplace. With data, you can spot opportunities that can enhance your business performance. With data, you can make intelligent and informed business decisions. And with data, you can stay competitive and relevant in the marketplace.
Thus, if you are a business owner that does not place a premium on data gathering, storage, and analysis, then your business is on its way to oblivion.
However, despite the emphasis on data collation and analysis in today’s world of business, you must be cautious about the accuracy of the data you are analyzing.
If you get it wrong with data gathering, you will get it wrong with your data analysis. And if you get it wrong with data analysis and interpretation, it is definite that your decision will be faulty.
And a faulty business decision, as you quite know, will have a negative ripple effect on the overall success of your business.
It is simply garbage in, garbage out, as it is said in the world of computing.
In view of the above, wisdom demands that you should engage in data cleaning before analyzing, interpreting, and using the data for any business purpose.
For the benefit of the doubt, data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data. And the goal is to improve the quality of data, making it more accurate, complete, and consistent, so that it can be used for analysis or other purposes.
To achieve this, there are basic rules you must follow. Here they are.
- Define your objectives: Before commencing the data cleaning process, you need to define the objective of the whole exercise. In addition, there should be clear criteria or yardstick for you to define what is considered clean data.
- Handle inconsistent data entries: Inconsistent data entries, such as different spellings of the same word, can cause errors in the analysis. So, you need to identify and correct them.
- Remove irrelevant data: Irrelevant data, such as duplicate data or data that is not needed for the analysis, should be removed to improve the efficiency and accuracy of the result.
- Handle categorical data: Categorical data, such as gender or job title, should be handled carefully. It’s important to ensure that the categories are defined consistently and that there are no overlaps or gaps between categories.
- Validate data: Data validation involves checking that the data values fall within expected ranges or conform to predefined rules. This helps to identify data entry errors and ensure that the data is accurate.
- Perform data profiling: Data profiling involves analyzing the data to understand its structure, quality, and relationships between variables. This can help to identify errors and inconsistencies in the data. To do this, ensure you have one header and there is no empty roll or column in your analysis spreadsheet.
I know this may sound or look technical for someone that is not numerate or mathematically inclined.
Nonetheless, as an entrepreneur or a leader, you must understand that all skills are learnable. And you must always be open to learning, unlearning, and relearning.
I will talk to you in my next post.
Balancing motherhood and career