A lot has been said about the fundamental role of data in business nowadays. If data is supposed to be used, after its transformation to information, to support or enable business operations and making operational as well as strategic decisions, its quality determines the ultimate success. Among many others, the following data quality issues are common:
- Incomplete data about leads and clients limits the capability to convert leads to clients and to service clients.
- Incorrect data about address information or birth numbers directly lead to inefficient marketing operations and poor customer experience.
- Inconsistent data about products with accounting data (general ledger) brings inconsistent conclusions about business, made by accounting vs. product departments.
- Duplicate data about the same contact information for a single lead or client makes Master Data Management unnecessary complex.
- Missing lead or customer data limits the ability to convert leads to clients and to service clients; more quality information you have about your customer, the better service.
- Outdated data leads to making inadequate decisions.
how we help
Our team of technology independent consultants help clients to really manage data as quality asset by helping them to build culture, processes, organization, and systems for continuous Data Quality management.
We believe any sustainable improvement of the quality of organization’s data must be framed by a Data Quality system, adopted by the organization, where the implementation of such a system must be tackled as a specific case of a process engineering project, i.e. it demands a “holistic” and gradual approach, respecting both people and technology aspects, and sheltering both strategic and operational viewpoints.
Our service may be either shorter engagement helping a client to improve the data quality of specific areas negatively impacting a selected business process (e.g. marketing, sales, accounting or reporting processes) or a more complex project establishing the capability of Data Quality management.
our approach
Building Data Quality Management System
Our typical approach for establishing the capability of Data Quality management comprises the following four phases. Since building the new capability from grounds up sometimes is a radical organizational change, we frame our mission by the Kotter’s 8 Step Change Methodology.
our solutions
Application System
Implementing systems and tools enabling effective and efficient Data Quality management.
Process System
Implementing a suitable and sustainable process system for continuous Data Quality management.
Data Cleaning
Cleaning selected data based on Data Assessment or requirements and implementing preventions.
Data Assessment
Preparing a report of data characteristics to be used as a source to gain insight into the data, clean data and define new controls.
Organization Assessment
Reviewing organizational aspects of Data Quality, e.g. some data areas with unassigned responsibility or missing knowledge.
Client story
Multi-tenant Data Warehouse
The goal of this international project was to design and implement a complex Data Quality Management System for a leading financial group, striving to operate the Data Warehouses of its subsidiaries by a unified platform.
free Assessment
Data Assessment
Preparing a report of data characteristics to be used as a source to gain insight into the data, clean data and define new controls.
Organization Assessment
Reviewing organizational aspects of Data Quality; e.g. some data areas with unassigned responsibility or missing knowledge.