Information management

Business will increasingly be based on the innovative and effective use of information. Information management processes and systems are the foundation of a modern digital company. To optimise sales, operations and growth, organisations must actively align data with business goals, handle large volumes of information and manage data as a valuable shared asset. Additionally, a modern way of working with data protection is a business necessity, a legal requirement and a foundation for daring to share information.

The amount of data is growing hugely. You must be able to manage big data and diverse information from various sources, and to make use of them in real time, flexibly and in a scalable way. The core of information management lies in business-driven and high-quality modelling, integration, enrichment, quality, master data management, governance and living up to regulatory demands and security.

Modern information management plays a vital role not only in enabling reporting and analytics, but in industria internet solutions and predictive analytics, as well as in e-commerce and the development of every digital and data-driven service. By neglecting information management, companies risk undermining their analytics, digitalisation, data science ambitions and goals.

Our services

  • How information should be managed and governed to meet the organisation’s strategic goals, including an assessment of maturity, business benefits, organisation, governance and the required transformation roadmap.

  • How roles and responsibilities for governing data within the organisation in the legal, regulatory and security contexts should be implemented. It’s vital to success that the data governance framework is aligned with the overall business priorities and steering.

  • How we should architect and model our data, the rules that apply to our information and how information should be grouped and named. To accelerate data and digital transformations, a defined information architecture and data model practice is required.

  • How data quality can be maintained over its lifecycle of creation, storage, usage and finally deletion. To manage data quality, it is vital that the data requirements are clearly specified, and that there are a framework and processes to maintain and monitor the data over time. A lack of data quality is also one of the key hindrances for organisations in gaining value from analytics and AI initiatives.

  • How Master Data Management, MDM – central information should be used throughout the business, across business processes and systems. This is critical to constantly keeping information consistent, updated and synchronised.

  • How systems and modern data catalogues can drive successful information management and data governance. Systems should enable integration of data management practices with day-to-day business workflows and activities. Modern tools allow data discovery, data lineage, ways of operationalising policies, collaboration and stewardship to actively engage frontline users.

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