Data is a collection of representation of facts as text (news), numbers (figures), graphics (datasheet), images, sound or video. Information is data in context. Without a context, data is absolutely meaningless or clueless to interpret; we create meaningful information by interpreting the context around data collected from different sources. The resulting information helps to take decisions. So, data architecture (DA) is an integral component for defining the information architecture (IA); it is concerned with the design and intent to use data in a given structured format (like databases or data sources and file systems). A data architect models the data in stages (conceptual, logical and physical), and must relate the data to each process that consumes (uses) it.
IA is the starting point for data modelling, design, and development to support business process needs. Any new models or changes to existing models must be consistent with the definitions of existing information objects. IA imposes coherence and consistency in both the definition and use of common data objects. Data management involves data architecture, information architecture, data management and governance, data security and data life cycle management.
Modern data analytics and management require high resources like CPU, memory and storage, and it is a costly investment to handle this on-premises. A cloud platform easily enables agility, scalability, reliability, availability and security for data management solutions.
Data analytics in the sports industry
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