Five tips to improve data quality
Tips to improve data quality for business enterprises to achieve improved decision making and bring about innovations in the business.
One of the most valuable assets of a business enterprise is data. It forms the backbone of various operations and drives crucial financial and non-financial decision at different levels in the functioning of the business. As there is so much at stake, having complete, accurate and consistent data is extremely vital. Business enterprises rely on data for their prospecting and retention efforts. The absence of accurate data can cause business enterprise to execute marketing campaigns which are not that effective and fail to fulfill customer expectations.
An Experian QAS study has revealed that close to 45% of business enterprises do not possess a data quality strategy. Such enterprises feel that implementing a data quality strategy can turn out to be a formidable task. With various areas such as inventory management, contact data, constituent records and many more needing improvements, business enterprises are left pondering over implementation and other aspects. Business enterprises must let go of apprehensions and implement a clear data quality strategy.
Here are the tips to improve data quality for business enterprises:
Have a good understanding of the data:
Data is not free as it takes a large number of resources for collecting and maintaining the data. It can be quite a costly affair for business enterprises if they collect data that is not required as it involves various processing and storage fees. This can even make it hard for the enterprises to accurately track the data that is needed. Business enterprises must take some time out to understand each piece of data that is collected and make sure that they are of some use. Unused data gets accumulated and decreases the overall data quality. Data that is generated today must be for the sole purpose of satisfying business goals. Business enterprises must be forward-looking and must focus only on data that is necessary for accomplishing the goals and must also ensure that the data is coming from a knowledgeable and trusted source.
Check the data before entering into the database:
To get clean data, business enterprises must follow the basic step of validating data systematically or manually according to the business rules. If clients are engaged, correcting contact information becomes easier. A telephone call, web form or live online chat can be used for customer engagement. Obvious errors in email or mailing lists can be easily corrected by using scrubbing programs. Data from Global Data Quality Research 2016 has shown that in the past twelve months, close to 81% of companies encountered email deliverability challenges. A data validation service must be used for the purpose of checking. Data verification tools usually prompt the audience member or the staff representative to type in any missing details before formatting the address to comply with the email standards.
When sales and marketing professionals manually enter information of clients who are not engaged, verification of the data that is being entered can ensure accurate and faster data capture. To prevent bad data from entering the business database, incomplete elements are highlighted by the software as the information is being keyed in by the staff. Once this is done, the business database must be checked to ensure that the information is complete and standardized.
Look out for duplicate records:
Duplicate records are usually associated with email addresses but in B2B instances an additional control can be set up by using the full name of the prospect. Duplicate contact records usually have multiple fields of duplicate data which can confuse sales, cost the business enterprise money and drastically affect the marketing automation process. The duplicate data must be identified and deleted immediately so that no activity or history is associated with it. This can be achieved by setting up trigger alerts so that notifications are received automatically. The search function in the database must be used to scan different fields in the entries and find any discrepancy so that they can be subjected to manual scrutiny.
The records of existing duplicates must be used to run weekly reports so that sources and processes that generate duplicates can be easily investigated which can help in preventing and fixing systemic issues. If a large number of duplicate records are found while running the reports, a specialized tool can be used to assign logic to master records so that a one-time merger action can be performed quickly.
Prevent CRM Sync Fails:
CRM synchronization can be easily overlooked by software programs which can result in costly implications for the business enterprise. If there is failure in syncing a new lead, it might not show up in the sales queues and views. The failure to sync an existing lead will result in not only new information but also data field updates such as product interest, additional profiling and requests for information (RFIs) being lost. Sync failures can be caused by various issues such as bandwidth limitations, performance slowdown, non-matching validation rules, field-level visibility to name a few. To pinpoint and quickly fix sync issues, top syncing field reports must be requested from the support engineer and to uncover failed syncs of leads from the previous day a static list with daily reports can be utilized.
Perform periodic data reviews:
To really understand data and ensure data quality, business enterprises must periodically review the data so that anomalies can be uncovered. Reviewing the data can help in developing a long-term understanding of what is normal and what is not. A regular data quality strategy must be set up so that regular database checkups can be carried out. Real-time verification efforts can be supplemented with regular bulk processing to ensure contact data integrity. These updates provide the business enterprises with a better chance of measuring the quality of data. The database checkups also ensure that the outdated data is constantly refreshed and maintains high quality throughout.
The Global Data Quality Research has revealed that close to 69% of business enterprises do not have a centralized approach to data quality. Data quality is extremely important for supporting management decisions, having a database for future business development and brings about improvements and innovations in the business. By having a clear cut platform for accurately capturing and updating contact data, business enterprises can drastically improve data collection, interdepartmental activities and data tracking which can be instrumental in enhancing communication accuracy and streamlining operational activities. Bringing about improvements in the quality of data does not have to be challenging and only requires proper measurement, planning and a clear understanding of the business value of the data.