Data management is summarized as “the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.”
AIQUAL focus is on Data Governance, Data Architecture, and Data Analytics, the 3 sub-domains of Data Management critical for digital business success, and critical for avoiding costly over-runs and other pitfalls.
a) Data Governance
The rise in importance of data analytics as the enabler of new winning business models means we should treat data as a corporate asset. An asset that requires proper governance to both protect it and ensure its optimal utilization. In other words, data governance main mission is to ensure your data quality, security, privacy, and adequate accessibility for all business requirements. It is a fundamental cornerstone for companies that want to leverage data (the new oil) effectively and mitigate associated risks.
Several data governance frameworks are available and you will typically adopt one as your reference for best practices. External consultants such as Aiqual can play a facilitating role in putting your cross-functional data governance plans on a fast track to success. This is achieved in 3 main ways:
- Leveraging best practices to produce a data governance roadmap
- Facilitating cross-departmental collaboration by bridging the business/IT divide
- Implementing measurements then ensuring all stakeholders meet the agreed KPIs
b) Data Architecture
Legacy data architectures do not meet current business requirements anymore:
- Increases in data volumes and variety
- Requirements for real time data processing
- New analytics workloads
You need to ensure you have a solid and modern data architecture to meet all your analytical requirements and serve your key business processes:
- Data virtualization is used to ensure stability of the new complex and fast-evolving data platforms. The data analysts and other data platform users are shielded from the ongoing evolution of the data sources.
- Include Blockchain in your data architecture toolbox and leverage it as applicable.
c) Data Analytics
Data value is brought to the surface only when properly analyzed to derive relevant business insights. And these insights are best monetized if they are made actionable, meaning that the insight will help trigger a valuable action. At the lower end of the data analytics spectrum we find traditional BI reports. While at the upper end we have machine learning algorithms that are used to train machines to become artificially intelligent and capable of taking autonomous action.
Smart Systems and Processes
Data management is just a mean to an end. The end we seek is to have smart systems and processes that act on the insights the data provided to deliver our optimized products and services. This section titled Smart Systems and Processes will cover the capabilities we need to make the insights we accumulated actionable, all the way to the bank.