"Data management is the use of methods, procedures and organizational, architectural and procedural aspects in handling the raw data for the increase of the production factor knowledge"
Companies have decades of mass data that can be stored in data warehouse platforms and analysed by Business Intelligence systems. Big Data describes data that can be limited processed with traditional databases and data management tools based on their variety, velocity and volume. The volume of the world's available data doubled current biennial. The data is resulting from the digitization, acquisition of measurement, control and communication data and new business models of the knowledge society (e.g. Social, Health Monitoring) or upcoming internet of things (IOT). The economic value of company-relevant data is increasingly recognized. Findings and decisions that can be generated from data can represent a competitive advantage.
The data processing in the company is historically grown whereby the flow of data between applications and their interfaces are not always known exactly, methods and technologies are different, so the risk is high to get unexpected data errors by small changes in the source systems or application interfaces. The effort in the IT in dealing with the data integration of mass data is already high and is facing the challenge to provide flexible and quick new data for analysis and reporting purposes. The demand to integrate external data sources rises. A project to expand the data warehouse to a Big Data platform does not resolve this dilemma:
- The requirements of actuality of data increases for analysis purposes, but the classic data supply with ETL / ELT in data warehouse environments is often a batch processing
- Corporate data in the data warehouse are sometimes refined, aggregated and filtered in ETL / ELT routes, so the impact of data quality and data transformations on analysis results cannot be estimated
- The establishment of additional interfaces or the use of data federation does not reduce the cost of IT and complexity; without consolidation existing interfaces
- Scalability of data management processes
- Effective protection against data abuse
- The data strategy must be consistent with the business strategy
- Statement as Bits & Bytes assembled into data, data into information and information into knowledge: through transparent data source, clear definitions of the data, clear responsibilities, knowledge of factors influencing data and an effective data quality management
- Implementation of legal regulations, e.g. fulfilment of principles for the effective aggregation of risk data and risk reporting (BCBS 239) in banking
- Clearly defined rules, methods and procedures for handling data must valid for all IT projects and all levels of the organization (data governance)
Our Offerings:
We support companies in the introduction, adaptation and implementation of their data management
Procedure examples:
- Analysis of existing data governance rules and processes, professionalization of Data Governance (KPI's for the verification of compliance with governance rules, centralized repository on the raw data, handling of critical data elements, data security and data quality)
- Analysis of interfaces and ETL / ELT tools, verification and updating existing data repositories, identification of measures to reduce complexity in interfaces and data transformations, consolidation of interfaces and ETL / ELT Tools
- Analysis of new data types (Big Data), gap analysis and adaptation of concepts, policies, methods and, if necessary tool selection for processing new data types (Big Data), implementation and testing of new interfaces
- Analysis existing reporting and analysis solutions, elimination of isolated solutions and develop comprehensive solutions
- Professionalization of data management processes (Service Strategy, Service Design, Service Transition and Service Operation, Data Governance) to increase the agility to fulfil requirements
- Development of "Data Lakes" with raw data of internal and external data sources in a central data store (Scalable) with easy access for analysts and power users in compliance with data governance rules
How can we help you? Depending on the situation in the form of consultancy, interim management, executive coaching and training.
Project examples:
- Consulting: Data management concept for a data warehouse
- Interim management: Project management for the introduction of an ETL tools in a data warehouse environment
- Interim management: Project management for the transformation of proprietary ETL tool by a commercial ETL tool together with consolidation of existing interfaces