Clean data is crucial and more and more companies realize it today. To make right choices and decisions in data cleanup, it is good to be familiar with its common strategies and techniques. To start with, it is necessary to understand the objective of data cleansing process. Here are some common aspects to cover.
It is important to understand the types of data is being collected and specific pieces of information that shape your business. Data fields should be analyzed to establish which fields are mandatory for your business and which are nice to have but not must have. For example, without email address or phone number a sales rep may not be able to contact the contact and therefore close the deal. However, an annual revenue data field for a company/account may not be mandatory for marketing or sales to still be effective in their jobs. Therefore, my identifying what fields are mandatory you can increase data completeness and improve your sales and marketing efficiencies.
When data is being entered it is critical to establish and maintain data standards and formats across different records. It does improve the quality of data significantly. Some formats can be automatically embedded through computer programs to prevent format errors at the point of data input. It is key to discuss and stick to data standards for all departments and teams in the company. Creating a special documentation describing mandatory formats and standards can be of great help allowing to keep data clean. Good communication especially critical when companies implement new tools and programs. Quality standardized approach to data simplifies data transformations. The required standards should be one of the priorities that keeps the data uniform, saves time and money for any organization. Example of data standardization is country and state fields that should following either to 2 digit format or the full name spelled out, but not both!
Another important aspect of maintaining clean data is automation. Be open to implement any tools, scripts and script innovations that can automate data entry. It will allow to reduce human errors as much as possible and thus, sustain clean data and save resources. Hiring experts that can help automate data processes improves data quality not once but for a long run.
Data validation is another step of data cleansing. It is important to ensure that data is accurate and consistent. It can be achieved by comparing the data to another data source. Special validation techniques can be applied. It should be mentioned that it is easier to validate the data at the time of data input rather than fixing the issues at the later stages. Sometimes internal data sources are not substantial to maintain clean data then the third party can be involved to validate the data and make sure it is clean. It is critical that the third party source is a trustworthy, reliable source. Example of data validation includes: email verification, address validation and phone number verification.
Duplications are often inevitable during data processing. Deduping is another essential data cleansing feature. It is the process of finding and replacing or sometimes deleting repeated data. It is one of the most critical features of the data cleansing process. The impact of duplicates on data processing results is crucial and it is important to keep an eye on duplications on the regular basis. StrategicDB is a leader in data de-duplication, find out why.
Missing data diminishes data quality significantly. It is core for data analytics. That’s why it is cardinal to reveal missing pieces of data and fill in the gaps as soon as possible. It is also beneficial to append missing data regularly. Needless to say that missing data creates discrepancies and distort the metrics.
The significance of clean, quality data should be communicated across the whole organization. It is vital to spread and share information regarding any data changes and data standards among all departments. Any changes have to be discussed, documented and monitored by all teams of the company.
Data cleansing should be not only monitored but well maintained on a regular basis. It can be beneficial to test random pieces of data to find any glitches timely. Caught in time the discrepancies can be fixed at the initial stages avoiding corruption of the whole database. If you have not done it already, it is advisable to create a data quality dashboard to monitory the health of your database on an ongoing basis.
Accurate, valid and cleansed data enhances quality metrics, provides valuable insights that allow refine companies strategies, save time, money and improve ROIs. Data cleansing is a constant, ongoing effort that pays off in organization success and well being. Don’t hesitate to hire professional cleansing teams to help you clean your data. StrategicDB is one of such teams and we will be happy to assist you with finding the right cleansing solution.Please contact us at email@example.com