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With AlphaData, put your data quality concerns to rest
Does your organisation suffer from data quality issues?
We can help!
As you know, bad data can impact you in many ways. There are obviously the missed sales opportunities resulting from marketing campaign letters being sent to the wrong addresses, the inaccurate reporting and analytics due to incorrect pricing data and the damage to reputation that your organisation can suffer from when a client, supplier or employee is mishandled due to missing or inexact data.
Less obvious are the detrimental effects poor data quality can have on your staff’s productivity and morale, and the impediment to your enterprise’s ability to reorganise itself through IT projects and/or respond to new regulations being introduced (KYC, AML…).
By providing you with the methodology and industry best practice, AlphaData ensures data quality management becomes part of a cost effective, controlled process that can then be extended to all parts of your organisation. No longer a headache, it becomes a culture.
"If the state of quality of your company's data was the same level of quality as your company's products and services, how much more profitable would your company be? "
- Mehmet Orun
Whether your data quality needs come as part of a MDM, BI, data migration project or simply result from a standalone data quality initiative, we are able to join your teams and rapidly add value. Our staff is ITILv3 and CIMP certified and understands data management from both a business and IT perspective.
Our 3 step methodology to data quality can be applied in organisations of all sizes and will yield tangible results:
This provides us with much needed information on the general state of the data, the given rules according to which it should comply, and the area to be targeted in priority.
These will be used to define the error types to be sought and the scripts to be used to identify the the discrepant records.
This tool will not only be used for reporting but can also be integrated with a standalone dashboard, or within your BI architecture, for up-to-date reporting on progresses made.
Once the errors have been identified, correcting them is only part of the solution.
Using the information gathered in the previous steps, we are able to pinpoint the root causes, whether in inadequate processes, technological mishaps, or simple human error, and provide ways to prevent further data corruption.
By iterating these last two steps, the data quality rules can be further refined and data quality be brought under control.