Business data is now pouring into organisations from a wide variety of channels and includes useful insight into customers, prospects, products, inventory, finances and employees.
It is an extremely valuable commodity that helps organisations make effective decisions. These can range from executive-level decisions about mergers and acquisitions to a call-centre representative making a split-second decision about customer service.
Business decisions can obviously have a significant effect on the organisation’s ability to satisfy customers, reduce costs, improve productivity or mitigate risks.
High data quality and integrity can be used by organisations to increase efficiency, enhance customer service and drive profitability, which can result in a competitive edge. However, data that is outdated, duplicated or invalid can result in conflicting business intelligence, such as unusable reports and customer misinformation, which can lead to inaccurate decisions being made.
Organisations need to make sure that all this data is being integrated between systems to ensure that each part of the business has access to the most up-to-date data.
Data is a dynamically changing beast though as organisations obtain new customers, products and inventory on a daily basis, and often add new data sources from new channel partners or new customers through acquisitions.
Data quality measures therefore need to be in place to make sure that the data being distributed between the systems is correct and relevant. Business systems are only as useful as the data they hold. Data problems like broken hierarchies, missing data and inconsistent data standards prevent good decisions being made and can interfere with the workflow of an organisation.
One of the most common data quality issues is duplication of data. For example, a customer may have an entry in an accounting or ERP system but may have a different customer profile or number in a CRM system. This conflicting data can hinder the level of service provided and have a detrimental effect on the relationship.
A process needs to be put in place in which the data is constantly monitored in order to cleanse the information stored. Performing this manually is obviously a laborious task and one that will be prone to human errors. It is also unlikely to be a top priority for an IT systems manager. Automating the entire process will help resolve these issues and eliminate the exposure to risk.
Monitoring and Checking Databases
Codeless Platforms’ BPA Platform can monitor each and every database to check for inconsistencies, when data is updated or when new data is added, and then send data management alerts and notifications to the relevant personnel or department to inform them of the situation. This will assist in the cleansing and integrity of the data and make sure that it is consistent across the entire business.
Alerts can be generated for data cleansing purposes such as when:
- An email bounces from the CRM system
- Someone leaves the company in the CRM system
- Fields are not entered on Account Creation
- Data enrichment needs to fill additional fields
- Data has become x days/weeks/years old
- A contact is added to a key account
- Payment details need checking
- A data field for compliance purposes is not completed
Alerts can be generated for data integrity purposes such as when:
- An incorrect value is entered into a field e.g. number in a field waiting from text
- Sensitive data is changed
- Sensitive data is deleted
- Data is imported
- Critical data is missing
- Duplicate contact in the same account has been entered
These are just a few examples, but the BPA Platform makes it extremely easy to automate any number of data management alerts for a host of different scenarios, thanks to its drag and drop functionality and simple integration with any business system. The process consists of four simple steps/tasks.
The first task detects a specific change of data in a database and then outputs key pieces of information about the change as variables. A Database Query (ODBC) is then used to get more information. It reads the database of the business system to find additional details to include in the alert. This step consumes the variables from the previous step and outputs a recordset of data. A recordset is like a table of data.
The recordset results can then be formatted as HTML and presented for use in an email. The output from this step is a document set. Each row in the recordset creates a document. An email is then sent containing the formatted information. Each document in the document set is sent to a different email address which originates in the recordset.
The Benefits of Data Management Alerts
Automating notifications and alerts to help with data management is a simple process to incorporate into any business system and can make a considerable difference to the running of an organisation: improving company performance, improving customer service and ultimately ensuring 100% visibility of the correct information to help people make decisions and plans.