Ways to Keep Your Data Entry Clean and Accessible

by Vladimir Ilic podroomcreative

Data has become a very important aspect in business. Today, it is no longer common practice to lead a business on a gut feeling, experience, or intuition, and companies look to gather as much data as possible to analyze their situation and make sensible future business decisions that have a high chance of succeeding.

In the highly competitive business environment we have today, where more and more organizations are popping out every day, it is important for companies to make the most of their resources and point their focus in the right direction. This is where data comes into play, and all companies perform data entry of some sort today.

Still, these actions carry certain difficulties with them. For example, you need to make sure that you add correct data to your database which can be valuable and useable. Here is how you can ensure that your data is always clean.

Prepare for data gathering

One of the reasons why so many companies are unable to keep their data clean is because they don’t think about their data until they’ve stored it. A lot of statisticians and data analysts don’t know how they will use the data or which statistical tests they will utilize before they gather their data.

This means that they don’t care how data is gathered, stored, and which factors are being considered. This can creates a lot of problems later on. On top of that, this kind of data can be difficult to clean as well, and you will just end up wasting a lot of time and money.

Before you start collecting data, think about whether the data that you get will be able to fit into all the categories and boxes that you created. This is essential in order to be able to draw conclusions from your data. It is also a good idea to test your current setting and gather a certain amount of data to see if it works.

How to collect data

Despite the fact that you can clean your data, you need to collect it as cleanly as possible to reduce the workload later and make it easier on yourself. Still, if you inherited the data from somebody, you won’t be able to do much, but if you are in charge of collecting, you need to set a couple of standards in order to make your job easier.

If you have small pieces of data, add them to a single worksheets. If you try to spread out small amounts of data on multiple spreadsheets, you will have problems sorting it and you will make mistakes that will be tough to fix. The best rule is to use a single column for a different variable, as most analysis packages and statistics can work with data like this.

Also make sure that you use the right numbers, or measurements, for all of the variables you are entering. Make sure that you create a template and test it so that there are no incorrect values.

Data cleaning

It is called data cleaning, but it isn’t really about cleaning. It is all about organizing unstructured data properly. After you’ve started data cleaning, you will also have to clean new data that is flowing into the system with workflows and scripts.

You can run them batch per batch or in real-time, depending on what kind of data you are working with, how much of it, and what your needs are. You can apply this routine both to the data you had stored previously and to new data.

If you encounter records which cannot be corrected automatically, such as emails and phone numbers, you will have to find ways to get the missing data. This is where you have to do this manually: check Google, reach out to your contacts, visit sites, or simply hire a 3rd party that can help you.

Data drives a business forward. Make sure that you start utilizing it as soon as possible and apply proper measures to ensure that you have correct and usable data you can rely on.

Sponsor Ads

About Vladimir Ilic Junior   podroomcreative

0 connections, 0 recommendations, 5 honor points.
Joined APSense since, November 17th, 2017, From Nis, Serbia.

Created on Apr 24th 2018 09:40. Viewed 495 times.


No comment, be the first to comment.
Please sign in before you comment.