Could you name a few data cleansing best practices that you follow, and how this has helped you become a better Data Consultant?
A lot of companies ask their technology employees about why data cleansing matter to them. The answer is simple. Good quality data will help a company identify potential customers, provide better customer service, run better quality sales and marketing campaigns, and know how much they are spending and tracking to determine ROI's.
Data Consultants should have expertise with a variety of business intelligence tools like Informatica, Power BI, Cognos, and Microstrategy. All these tools help the company manage data so it can be used within the company for many purposes.
What the hiring manager is seeking out of this question is best practices that you follow. Everyone seems to have a way of doing things their way, and because it's their way, it's not necessarily the best way. This is precisely why the hiring manager wants to hear about your best practices method. Has your method made you a better consultant? The best practices method that you share should convey a message of progressive learning and experience gained in the field while being given more responsibilities for larger and more complex projects. Don't forget to list your best practices and the data cleansing strategy that you use as well.
"In my eyes, data cleansing practices are an important part of my job duties and responsibilities. At the high level, I start with a data cleansing strategy, so I know what the goals and expectations are for the data cleansing initiative. Here is a list of best practices that I follow that has helped me become a better data consultant.
1.Develop a data quality plan - (Create data KPI's, know where data errors occur, determine where the data is coming from, and perform root cause analysis on data health).
2.Standardize customer contact data - (Check important data at the point of entry and create a standard operating procedure).
3.Validate and verify the accuracy of data - (Use manual, web, and email verification).
4.Identify duplicate entries - (Manual and automated systems).
5.Append data for accuracy - (Company name, contact first name and last name, title, phone, email, location, revenue, product lines, company stock, etc.)."
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