Mastery of the Data Management discipline is an essential part of any digital transformation journey. It is part of being a data-driven organization and it is a pre-requisite for success in the exploitation of Artificial Intelligence.
Aiconomica can support you in developing and maturing your advanced data and analytics capabilities, and in putting the resulting insights to work for you through intelligent applications and processes.
1- What is Data Management?
Data management is a broad capability to process available data enterprise-wide and to derive business value from it by turning it into actionable insights. All within the framework of your overall digital business strategy and to support key digital transformation initiatives.
Data Management covers 10 disciplines as seen in this DAMA Wheel diagram, with Data Governance at the core. Note that in a typical engagement the majority of these disciplines will be in scope to ensure no critical loopholes or project risks are created. We devise the project plan using agile incremental roadmap based on your current and target maturity levels, as assessed and scored using the CMMI framework.
a) Data Governance
At Aiconomica we understand the importance of initiating or maturing your Data Governance competencies and practices. We have developed this as a core expertise and we are available to advise and assist in driving it forward at your organisation.
Data Governance emphasizes treating data as a corporate asset and ensuring data quality, security, privacy, and adequate accessibility for all business requirements. It is a fundamental capability to leverage data (the new oil) effectively and mitigate associated risks. Several data governance frameworks are available and we will help you adopt one (or a hybrid of several) as your reference for best practices.
b) Data Architecture
With data volumes and variety increasing, and with requirements for real time data processing, you need to ensure you have a solid and modern data architecture that can serve all your business requirements and not become an impediment or a bottleneck in your key business processes. Under this topic we will also include best practices for data ingestion, storage, openness, and lifecycle management.
c) Data Warehousing and Business Intelligence
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 perform autonomous actions.
2- Smart Systems and Processes
Data management is just a mean to an end. The end we seek is to have measurably smarter systems and processes that act on the insights the data provided to deliver our optimized products and services. Aiconomica covers the capabilities you need to make the insights you generate actionable. For example making the insights embedded in core business processes and all along your customer journey.
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