Data Governance Vs Data Management
Users often find the two terms confusing. Data management comprises the processes used to plan, enable, specify, create, obtain, maintain, use, retrieve and control data. On the other hand, data governance is a subset of data management. It includes processes to ensure accountability and ownership of data assets.
Information governance standards will be different for diverse domains. For instance, rules for healthcare data will be different from, say, finance or insurance.
Data security is another term that people mistakenly use in place of governance. It, in turn, is a subset of information governance compliance includes adhering to security regulations.
Kiel Mobility Digital Twin
FIWARE | City of Kiel
The digital twin of the mobility stations makes data available through open APIs and standardized data models. This enables a better accessibility of the system both, south- and northbound. Through the standardization, the digital twin of mobility hubs is scalable and can foster the usage of new mobility services and improve municipal mobility planning.A prototype of the digital twin is implemented for the mobility hub in Kiel Oppendorf.
- FIWARE NGSIv2 and NGSI-LD Context Broker
- IDS-components planned for the future
What Is Business Intelligence
Business intelligence is the combination of applications, processes, and infrastructure that provide data access and analysis to improve your decisions and performance. Modern BI tools bring together data integration, data analytics and data literacy to close the gaps between data, insights and actions.
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The Data Catalogue As The Basis For Your Benefits
The data catalogue forms the basis for a successful Data Governance initiative. This is where the knowledge about the data, its origin and use, its technical content and the responsibilities for origin and use come together. With this metadata, your company gains additional support in the evaluation and use of your data.
Who Should Lead The Way In Implementing Business Intelligence
Sharing is vital to the success of BI projects because everyone involved in the process must have full access to information to be able to change the way they work. BI projects should start with top executives, but the next group of users should be salespeople. Because their job is to keep abreast of the latest sales trends to increase sales and theyre often compensated on their ability to do so, theyll be more likely to embrace any tool that will help them do just thatprovided, of course, the tool is easy to use, and they trust the information.
With the help of BI systems, employees modify their individual and teamwork practices, which leads to improved performance among the sales teams and increased employee productivity. When sales executives see a big difference in performance from one team to another, they work to bring the laggard teams up to the level of the leaders.
Once you get salespeople on board, you can use them to help get the rest of your organization on the BI bandwagon. Theyll serve as evangelists, gushing about the power of the tools and how BI is improving their lives.
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Unclear Data Stewardship Protocol
Your procedures for handling and organizing data should be easy to understand. Training can help clear up some confusion, but you may need to revisit your governance protocols if there is a wide-scale issue. With sufficient documentation, you should find a way to reword or clarify your procedure so it’s less challenging to understand.
However, if it’s an ineffective procedure, additional revisions may be necessary. You can work with your data governance committee to create a more functional solution.
Data Quality: Tools And Concepts For Your Data Quality Management
Besides metadata management, data quality management is the second immediately beneficial product of the Data Governance initiative. The role and organisational models of Data Governance, e.g. with the data stewardship concept, ideally support the management and operational implementation of data quality. Optimally implemented, the data catalogue and data quality monitoring interlock and ensure transparency and excellent data quality management .
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Industrial Additive Manufacturing Services
IBM | thyssenkrupp | Fraunhofer ISST
Thyssenkrupp and IBM together with Fraunhofer ISST have developed a prototype that builds the foundation for further expansion of an industrial manufacturing platform. The combined use of IDS technology and Blockchain is intended to enable a higher degree of automation within the Additive Manufacturing process, as well as to provide data security and data sovereignty.
The secure platform enables the exchange of trusted data and seamless interaction between all parties along the value chain. Easy access to AM technology and services also opens up new revenue streams for small and medium-sized enterprises. In doing so, the ecosystem focuses on protecting intellectual property rights and ensuring product quality through the immutability of data,
- Creating a trustworthy ecosystem for transfer of valuable and IP-relevant engineering data
- Processing industrial AM orders in a fast, traceable and reliable manner
- Protecting IP rights and ensuring product quality
Business Intelligence Data Governance And Modeling
Secure and model your raw data with an intuitive BI solution.
Secure and Model Raw Data into Business Information
Visual Data Set Designer
Visually relate and aggregate data with the Data Set Designer.
Multiple Data Sources
Linking through databases, text files, NoSQL, cloud data sources, and web-based sources.
Extensible Security with Role-Based Permissions
Enterprise-grade security can integrate with your pre-existing authentication protocols.
Extensible Security to Match Your Data Governance Protocols
Built-in End-to-end Security
Support for Industry-standard protocols, such as OAuth2, OpenID Connect, and Active Directory, ELAP
- Identity service acts as a federation gateway for external security providers.
- Multi-level hierarchal security means granular data control that offers another layer of privacy
Wyn’s role-based security ensures the appropriate people access the correct data across all verticals.
- Role-based security to map tenant-specific data access
- Data governance and modeling to logically isolate tenant-specific data
Intuitive Dashboard and Report Designers
Wyn offers enterprise reporting, embedded analytics, and enterprise-grade data governance and data modeling.
With Wyn’s easy-to-use dashboard and report designers in the same web-based application, end-users can develop their own ad-hoc dashboards and reports based on the secured data.
Create Dashboards and Reports in Minutes
The 4 Critical Data Types For Data Governance
When taking a measured approach to governance implementation, it is important to understand the four broad categories of enterprise data involved to optimize results and impact across the organization. Effective data governance means applying it appropriately to each individual data type.
Here are the four broad data categories to understand for data governance :
1. Master Data
When determining what qualifies as master data, the two biggest factors are that it is slowly changing and widely shared throughout the enterprise across multiple data sources. Examples of this include customer name and address, product SKU number/categories, supplier locations and more.
This critical information must remain consistent across every business system for the data to remain trustworthy, thus requiring the most stringent level of governance. However, this is not to say that this process does not apply to multiple data domains.
In some instances, data governance technology may determine that a singular definition for a data attribute is not plausible throughout the organization even if the definition of a customer frequently does not match across operational source systems. If these definitions cannot be mapped to each other, then multidomain master data management technology allows the data model to be constructed accordingly.
2. Application Data
3. External/Purchased Data
4. Internet of Things /Big Data
Bi Governance Orchestrates People Processes And Tools
There are many components to consider when starting a BI governance program including: data architecture, metadata, data integration, data security, end-user information delivery, and change management. Using a holistic approach, a BI governance framework should bring all these pieces together through coordinated processes that connect each of these components with the overall BI vision.
The proposed structure of the BI governance model, depicted in Figure 1, ensures that data governance, typically run by IT, is also linked with the business capabilities. Figure 1 proposes a comprehensive structure of the BI governance that orchestrates business and IT goals under common pillars, such as data quality, ownership, stewardship, and strategy.
As a starting point, the enterprise vision, direction and desired future-state in a BI strategy should sustain the initial pillars of the BI governance model. Over time, as the organization increases its BI utilization, there should be a continuous process for measuring performance and comparing the results achieved against their business objectives. The BI governance should include refining the BI strategic direction when appropriate as the organization evolves in its BI maturity stages.
Bi Governance Iterative Process
Some of the main functions of a comprehensive BI governance framework include:
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Wind And Solar Assets Modeling
When it comes to manage efficiently and effectively the renewable assets and all the O& M actions for the different actors , the difficulties to access to data exploitable in a cross functional way by these different profiles of actors and the different digital systems mobilized represents a real challenge.
The wind and solar description model is the digital backbone to federate all the businesses and its ecosystem around one single source of truth from DESIGN, MODIFICATION to others REFRESHMENT. This structured, agnostic asset management description support business process orchestration in a context of new business model refocused on growth of decentralized and distributed energy and carbon neutrality “as a service”.
Being able to share the same abstract representation of data for the wind and solar domain would allow a better understanding of the associated operations and an obvious improvement of the processes that mobilize the processing of this information. It lays foundations of virtual plants.
- Opportunity to build comprehensive models, analytics frameworks and improve multiparty collaboration capabilities needed to support digital ecosystems. It lays foundations of virtual plants with their benefits
- Backbone for renewables operator to ensure continuity of technical data along lifecycle
- Real accelerator for Greenfields and brownfields assets to deliver more safely, more quickly more efficiently and with a lower Total Cost of Ownership
How To Build A Power Bi Governance Model
A governance model is all about defining the best practices, procedures, and responsibilities for efficient and secure usage of your Power BI platform. Microsoft already has valuable information on data governance that you can use as a starting point. But, youll also need to consider pointers that are relevant to your organizational needs.
This is how you build your own Power BI data governance model to get the most out of the platform.
1. Identify Deployment Approach
Power BI can be used in three different modesBusiness-Led Self-Service BI, IT-Managed Self-Service BI, and Corporate BI. The control over data and the way information gets handled will depend on these installation modes.
All of these modes can co-exist, depending on the business requirements and user base. So, its essential to know data governance methods for each mode.
2. Define Roles
Two fundamental types of users access the self-service Power BI platformpower users and casual users. Power users need access to advanced features to create insightful reports. Casual users require flexibility to modify existing reports, such as drill-down and field selection.
Identifying these differences, and providing the right access according to roles, ensures that users have access to information without compromising security. Power BI offers tools that help you set up access permissions per data source, per user, and individual dashboards and reports.
3. Publish and Monitor
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Business Intelligence In Real Time
The field of application of business intelligence applications is shifting more and more from classic reporting based on historical transaction data towards real-time analysis. Companies want to analyze the performance of their business units at any time in real-time and see immediately how, for example, customer preferences change. Increasingly, managers are using business intelligence applications to identify business risks and take countermeasures as early as possible.
History Of Business Intelligence
What we know today as business intelligence primarily began being developed in the 1980s when the advent of widespread computer usage made data collection and analysis possible for companies to utilize. Over the years, BI processes widened and improved to include extensive data mining, data visualization tools, and various methods of data analysis to provide business decision-makers with important insights. Such insights can be used to increase operational efficiency and to help in making key business decisions related to things such as product pricing and .
Key advances in business intelligence include the ability to collect and manage extremely large data sets, the ability to combine external and internal data, increased data sharing, and the creation of business intelligence dashboards.
BI dashboards enable individual users of business intelligence to customize reports to serve specific purposes and run queries on the data to provide more information. An important characteristic of modern business intelligence dashboards is that they offer easy-to-use data interfaces that dont require technical IT expertise.
Modern-day business intelligence processes can incorporate real-time data with existing historical data. It enables business executives to perform data analysis that includes the most up-to-date information available.
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Key Data Governance Pillars
Data governance programs are underpinned by several other facets of the overall data management process. Most notably, these facets include the following:
Data governance is also related to information governance, which focuses more broadly on how information is used overall in an organization. At a high level, data governance can be viewed as a component of information governance, but they’re generally considered to be separate disciplines with similar aims. Get an explanation of how data and information governance differ in an article by Lawton.
Key Power Bi Governance Decisions
As you explore your goals and objectives and pursue more tactical data governance decisions as described above, it will be important to determine what the highest priorities are. Deciding where to focus your efforts can be challenging.
The following list includes items that you may choose to prioritize when introducing governance for Power BI:
- Who is allowed to be a Power BI administrator.
- Security, privacy, and data protection requirements, and allowed actions for datasets assigned to each sensitivity label.
- Allowed or encouraged use of personal gateways.
- Allowed or encouraged use of self-service purchasing of user licenses.
- Requirements for who may certify datasets, as well as requirements which must be met.
- Application lifecycle management for managing content through its entire lifecycle, including development, test, and production stages.
- Additional requirements applicable to critical content, such as data quality verifications and documentation.
- Requirements to use standardized master data and common data to ensure consistency.
- Recommendations and requirements for use of external tools.
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Designing A Better System
For over 30 years IDBS has helped R& D organizations achieve faster scientific breakthroughs by providing innovative software focusing on scientific workflows and systems integration to deliver the analysis and insights that streamline operations and accelerate science.
We provide integrated workflows and processes across the business to improve lab efficiency and foster collaboration.
Our platform is seamlessly integrated with your data ecosystem to break down data silos and promote knowledge transfer.
You will benefit from high-quality, contextualized data that speeds up decision-making and provides insights that enable you to optimize processes and remove operational inefficiencies.
The best-in-class platform for scientific research and innovation
For research and innovation in all industries
Challenges Of Lack Of Bi Governance
If you do not have your BI Governance in place, you will most likely run into some of the following challenges in your daily life:
- Lack of trust in reported data
- Person-dependent workflows
- Disagreement on which report is the latest version
- Reports and dashboards are scattered in mailboxes, on the network drive or intranet and are stochastically updated
- Old, inconsistent, and complicated data sources
- Disagreement on definitions behind the figures in a report
- Few or no one possesses the competencies to use the technology or vice versa, the employees are overqualified in relation to the technology that is made available to them
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Resilience And Sustainability Data Space
Companies and organizations as users of the Resilience and Sustainability Dataspace benefit from our data-based approach of a digital infrastructure to integrate decentralized information in a protected virtual space. With this infrastructure users are either able to apply already implemented services or to develop new services supporting our users in order to gain new insights and knowledge about. The variety of datasources which are part of the Resilience and Sustainability Dataspace also facilitates cross-over domain approaches for services as also will be implemented inside the PAIRS-Plattform. Especially the supply chain use case as a cross-over domain service utilizes the different data sources coming for different . By additional search and find functionalities inside the Resilience and Sustainability Dataspace partners can connect to build research projects, exchange in a sovereign way data and make use of the existing dataspace infrastructure. In the end, athis enables users to seamlessly build their own trustworthy resilience and sustainability ecosystems.
Green Data Hub Dio: Data Space Mobility Transition
Data Intelligence Initiative | ÖAMTC | Austrian Institute of Technology | Hutchison Drei | Upstream next level mobility | Ubimet | Zühlke Engineering | Fraunhofer Austria | FH Steyr Logistikum | Forschung Burgenland | FH Joanneum | Österreich Werbung | Kuratorium für Verkehrssicherheit | AVL List | Weblyzard | Tech meets legal | Spar Business Services – | Invenium | Accenture | SpotOn Statistics | ÖBB | Pierer Innovation | Porsche Holding | Nexyo | AWS | Microsoft | Hewlett Packard Enterprise
Conventional traffic and mobility need to be converted to sustainable transport with renewable energy sources, new ways of mobility and an interconnection of different forms of individual transport and public transport. Furthermore, enormous added value can be created in the area of logistics through the interconnection of data.
The Mobility Transition Data Space brings together the most relevant stakeholders in the mobility sector. By enabling a sovereign data exchange, a sustainable mobility transition is made possible. Improved development of e-charging infrastructure, increased resilience to unexpected events in logistics, traffic management in cities, optimisation and monitoring of existing infrastructure or redistribution of public space according to needs are possible use cases.
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