Optimising data potential is crucial in empowering organisations to make informed, data-driven decisions, enhance operational efficiency, and drive innovation. High-quality, integrated data provides valuable insights, enabling the development of new data products. Additionally, robust data security and governance practices mitigate risks and ensure regulatory compliance, protecting the organisation's reputation and financial stability. By leveraging their data assets effectively, organisations can quickly adapt to market changes, maintain a competitive edge, and achieve sustained growth and success.
Establishing Trusted Data
Finworks is trusted to manage and optimise the potential of data. Trusted data forms the foundation of any data-driven strategy. Without trust, data initiatives can falter, leading to poor decision-making and inefficiencies. The key steps to establish trusted data are given below:
- Data Validation - Implement rigorous validation processes to ensure data accuracy and consistency.
- Source Verification - Regularly verify the sources of your data to ensure they are reliable and credible.
- Transparency - Foster transparency in data processes by documenting data lineage and maintaining an audit trail.
- Stakeholder Engagement - Engage stakeholders across the organization to build trust and ensure alignment with business goals.
Breaking Down Data Silos
Data silos occur when data is isolated in different systems or departments, leading to missed opportunities for comprehensive insights. Data silos pose a challenge as they inhibit efficient information sharing, decision-making, and operational effectiveness. Successful strategies to break down data silos include centralising data, using data integration tools, and fostering data collaboration within departments or teams.
Data management platforms can consolidate data from disparate sources and have data integration tools to facilitate seamless data exchange between systems. Within an organisation, the establishment of a data strategy can give direction for data sharing and the creation of data products. Implementing the data strategy progresses organisational goals and data accessibility.
Ensuring Data Quality
High-quality data is crucial for making informed decisions and driving business value. Poor data quality can lead to errors, misinterpretations, and costly mistakes. Best practices for maintaining data quality include:
- Data Profiling - Regularly profile your data to understand its structure, content, and quality.
- Data Cleaning - Implement automated data cleaning processes to remove duplicates, correct errors, and fill in missing values.
- Quality Metrics - Establish and monitor key data quality metrics such as accuracy, completeness, consistency, and timeliness.
- Continuous Improvement - Foster a culture of continuous improvement by regularly reviewing and refining data quality processes.
Security and Governance
As operations become increasingly reliant on digital systems and communication networks, cybersecurity is a growing concern. Governance structures must prioritise security measures to protect sensitive information and reduce the risk of compromise of operational capabilities. Data security is paramount in maintaining compliance with regulations. Additionally, breaches can lead to severe financial and reputational damage. Two essential data security practices are access controls and encryption. Implementing strict access controls ensures only authorised personnel can access sensitive data. Encryption protects data both at rest and in transit.
Effective data governance in a data management platform ensures that data and information are managed appropriately, providing the right people with the right information at the right time. Data governance can help to extract value from data assets, enabling greater data access, sharing and integration and increasing overall efficiency and accountability.
Implications and Measures of Success
By focusing on these key areas—establishing trusted data, addressing data silos, ensuring data quality, implementing robust security measures, adhering to governance standards, and managing change effectively—organizations can unlock the full potential of their data. This holistic approach not only enhances decision-making and operational efficiency but also drives innovation and competitive advantage.
It is important to set out the measures of success for your transformation project, so you know what success looks like. By adopting innovative design, technology and security, there are positive impacts in terms of costs, operational efficiency, resource empowerment and good governance.
The implications of optimizing data potential are seen over multiple facets:
- Financial – cost reduction and the ability to reduce costs in the future through efficiency gains.
- Operational – Optimising processes saves data managers time daily. Continued innovation and agile solution provision.
- Resource – Complete, accurate, consistent, and up-to-date information about organizational resources. Integration with up-stream and downstream applications, creating efficient and responsive ecosystems.
- Governance – Enable the highest level of security, data stability and data quality.
Finworks Data Management Experience
What sets Finworks apart is our ability to manage change while working in really challenging environments in terms of deadlines and budget. Finworks is who large organisations turn to when they have tried other companies but need to get a successful workable data management solution.
Our exceptional depth of expertise and experience in design and implementation brings significant value to our customers who trust us to optimise the management of their data. An emphasis on creating a long-standing partnership allows in-depth planning of changes to not only understand the technical impacts but also how it affects the data team, operations and delivery partners, suppliers, or clients.
Case Study - Data Management Platform Installation
To demonstrate a successful approach to optimising data, a Finworks case study is presented. The client is a major European financial institution, with affiliated institutions in each of the EU member states.
Background
The background to the initial implementation was the following:
- No existing modern data architecture
- Need for accurate, detailed, quality data
- Data analysis had to be extrapolated
- Quality assurance issues
- Delays due to manual processing
Our Solution
The data platform links and manages multiple sources of data. A data quality framework cleanses, enriches and transforms data within a single data management hub. Finworks helped to create the data framework to support continued change and transformation whilst having best in class data security. A recent achievement was the cloud migration of complex big data products with high levels of governance and risk mitigation.
The Outcome
The outcome was a trusted, cloud-based, user-friendly data architecture framework for accessing sovereign financial data infrastructure and enabling Europe's financial institutions, financial technology, public stakeholders, and research institutions to exchange information and promote innovation.
Notable results were:
- Cleansed, enhanced suite of added value data
- Real-time, STP processing
- Optimised performance in dealing with large data sets
- 25% productivity improvement per day, for example, reporting was reduced from 2 hours to minutes every day.
Contact one of our experts today to learn how our solutions can help overcome your data challenges and achieve your data goals.
