If you can’t trust your data, you can’t trust your decisions, your strategy, or your compliance. Hence, effective data management can be a real business advantage. The reason so many organizations fail to treat data as an asset is that data, its processes, and interconnections are very complex and difficult to handle.
Data Strategy into Action
To unfold the full power of your data, you need the strategic ability to control it. Successful organizations link their data strategy to the general business strategy to develop a comprehensive and sustainable transformation plan based on the overall organizational targets.
When developing a Data Strategy, the use of a framework may allow stakeholders to assess each dimension involved in the Data Strategy process, such as Data Agility, Data Value, Data Usage, Data Control and Data Vision. One of the main challenges on the way to transform to a data-driven organization is linked to risk and regulatory requirements. Organizations need to conform with the regulation standards, and achieve efficient regulatory compliance, as well as accurate analysis and audit.
A comprehensive data strategy enables organizations to take sound decisions, decrease time to action, increase trust in data and operationalize insights. It not only enhances customer engagement, but also creates new business opportunities such as data-driven products and services.
Capgemini Invent supports clients by combining end-to-end strategy, people, operations, analytics and technology. The result? They become data-driven organizations that create and operationalize insights. We help our clients set clear data visions and objectives in alignment with business strategy. Based on numerous use cases, we support them to enable data-driven decision making in a scalable process. This results in designating roles and responsibilities to manage data and implement the role of a chief data officer (CDO), establishing cross-functional processes from data generation to usage and defining and maintaining policies, standards and guiding principles to ensure that data is sufficiently protected.
We offer a range of options to solve the data challenge
Analytical Data Quality Management (ADQM)
Data quality management defines the trust in your data. To use your data for critical decision and processes it’s not enough to have your data right. You must also be sure it’s right. Data quality management requires DQ policies where the data users define the data quality requirements.
Our Analytical Data Quality Management (ADQM) approach deals with these challenges by incorporating advanced analytical methods such as ‘Anomaly detection’ and ‘Root cause analysis’. Methods like these allow us to identify anomalies in the data quickly and allow for the implementation of more refined Data Quality rules because the root cause is known.
The efficiency and accuracy benefits of ADQM can be crucial for the success of banks, helping to minimize financial costs, increase productivity, safeguard regulatory compliance, increase organizational innovation, improve the decision-making process and prevent loss of reputation.
Data Governance & Data Dictionaries
Data Governance brings it all together and ensures data is correct, consistent and trustworthy. Without effective data governance, data inconsistencies in different systems across an organization might not get resolved, affecting the accuracy of business intelligence, analytics and enterprise reporting.
This is where data dictionaries come into play. Data dictionaries are lists of key business terms and metrics with definitions. They serve as a “single point of truth” for all metadata, providing the common language for the understanding of terms, definitions, and central data models and anchoring data roles and defining responsibilities. While it sounds simple, it is possibly one of the most valuable assets within the organization’s data strategy.
Capgemini Invent helps clients increase the value of information by building the right connections. Innovative approaches to connecting data will reveal new ways of understanding it. We ensure efficient integration in organizations by establishing an agile project approach and developing a constant flow with sprints, engaging the governance structure of data owners and stewards by a corresponding present workflow. Leveraging agile project management methods helps to implement data dictionaries more efficiently and more sustainably.
Master Data Management
Master Data Management (MDM) aims to reduce errors and redundancy in business processes. It creates one single master reference source for all critical business data, improving business processes to support corporate functions. Organizations that make use of MDM solutions ensure accurate, up-to-date and consistent data throughout the entire enterprise. MDM especially helps companies with segmented product lines, preventing disintegrated customer experiences. Having multiple sources of information is a widespread problem, especially in large organizations, and the associated costs can be very high.
Comprehensive Master Data Management is a response to the growing need for integrated information solutions. For example, MDM makes sure that when customer contact information changes, organizations do not attempt sales outreach using both the old and new information.
Capgemini Invent supports targeting of the full lifecycle of Master Data Management services that effectively link businesses and customer information. We also help leverage the power of AI and ML to ensure you can locate, access and utilize trusted data where it’s needed the most. By supporting the development of end-to-end MDM solutions, we include data quality, data integration, business process management and data security capabilities, enabling our clients to gain visibility into data, relationship patterns and variations as well as creating a trusted view for analytical and operational use cases.
Automated Root Cause Analysis
- Traditional Root Cause Analysis requires deep understanding of data flows or data lineage and a very narrowed scope to achieved quick results
- Analytical and statistical methods have the potential to overcome this limitation and to improve data quality more efficiently
- Statistical models enable a broader perspective on data quality issues by filtering for significant impact factors
- Gaining understanding of interdependencies between different data sources avoids gaps during the data creation process
- Finding root causes of data quality issues enables stakeholders to take sustainable actions and eliminate issues
- Machine Learning and AI methods offer a new realm of possibilities when it comes to identify, analyze and solve data quality issues – and vice versa strong data quality is key to success with ML and AI
- Anomaly detection finds unknown data quality issues by learning regular historic data patterns and facilitates continuous data quality improvement and proactive remediation
- By finding data quality issues before they cause process failures, costs and penalties can be avoided
- The complexity of data is created by its multi-layered connections
- Innovative approaches to understand and document these connections will reveal new ways of managing our data
- With AI driven data mapping, a consistent data lineage can be created efficiently to get an end to end understanding of your data flows