Strategies employed to clean up bad data

1. Strategies employed to clean up baddata: Since, it’s already established that data is a crucial aspect for most businesses to arrive at insightful business decisions, identifying the relevant and valid data from a huge volume of data amassed for business purposes can be time consuming anddaunting. For data to be valid, relevant and accurate, businesses will have to collect, analyze and organize data by cleaning it up and removing bad data for it to be useful. For example, there have been instances of companies claiming inefficiencies resulting from bad data, thus triggering losses forthem.

2. How will you identify if your business has baddata? • Bad data, it is presumed can be a corrupted file or document, an inaccurate data field, duplicate values etc. Let’s look at some categories of bad data,below: • Data that is not compliant: It does not follow the company’sstandards • Data that is incomplete: Some of the information is missing or not filled in from the datafields • Data that is considered irrelevant: If any of the required information is not valid or entered into the wrongfield • Data that is not accurate: If the data is not entered or updated with the required information properly. Makemistakes • Data that is duplicate: If the same information is available in various database records or if it comes multiple times in a particulardatabase. • In the meanwhile, it is also see how bad data can affect the efficiency and reputation of the business, also leading to missed opportunity, cut back on staff morale, inefficient customer service, inaccurate predictions, bad business decisions etc. This could also result in loss of revenue forbusinesses. • Methods employed to clean updata • From all this, you can see the significance of using valid, relevant and accurate data for your business requirements. Below, you can take a look at some steps to clean up data and assure its relevance andvalidity. • Prevent: The first step is to control and reduce the bad data, before entering the information into yoursystem • Remediation: Second step is to monitor and clean up the data, after it is fed into the system, at the same time, focussing on complying with qualitystandards. • It is also seen that most businesses opt to prevent bad data by looking up duplicate entries, before feeding the data into the system and cleanse it up. They would also fill in the complete requiredinformation. • Thus, you can infer from the above points, how outsourcing the data processing services to a global professional in the field of data management is the best option for quality service, cleaning up the data and ensuring its validity, relevance andaccuracy. More clarifications send mail inquiry tosales@outsourcedataworks.com