mbraco

What's in a name? mbraco name is a unique derivative of “imbracatura” meaning “harness” in Italian, and the word “embrace”. It reflects our approach to harnessing data — a business centric approach. #business #consulting #data #datamanagement #mbraco

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Foundational Thinking Pt. 3: Building The Foundation Continuing to build on our previous post we are highlighting the DMBOK “Wheel” as the foundation for our data management work. Ever increasing cyber threats require evolving approaches to Data Security. Ensuring the security of your data against breaches and other cyber threats is paramount. This means implementing advanced security measures such as encryption, access controls, and regular security audits. It’s not just about protecting your data, but also about safeguarding your company’s reputation and the trust of your customers. Large enterprises typically operate across a variety of platforms and systems, which often leads to the challenge of data silos. Effective data management necessitates a cohesive approach to Data Integration & Interoperability, where data from multiple sources is made to work coherently. Processes such as extracting, transforming, and loading data, along with various middleware solutions, are essential in facilitating this integration, ensuring that data is unified and easily accessible. Structuring data is important for all businesses, as businesses scale, structuring becomes ever more critical. Document and Content Management involves the storage, organization, and retrieval of all types of documents and multimedia content in a way that is efficient, secure, and easily accessible. Systems needs to support version control, audit trails, and permissions management to ensure that documents are up-to-date and accessible only by authorized personnel. Reference and master data management (MDM) are critical to ensure the consistency, accuracy, and accountability of enterprise-wide data standards. Master data encompasses critical business entities such as customers, products, employees, and suppliers, while reference data includes the set lists and categories that these entities are classified by. Managing this data effectively ensures that it remains a reliable, single source of truth that can be used across various systems and processes within the organization. In our next post, we will finish the foundational elements and considerations. If you have any questions or interest in discussing further, let's talk. #datamanagement #consulting #datagovernance

Foundational Thinking Pt. 2: Digging In   In our previous post we highlighted the DMBOK “Wheel” as the foundation for our data management work. We now want to provide a high-level view of each area starting with Data Governance. Governance is the cornerstone of effective data management. It involves setting internal standards and policies to control data usage and ensure compliance with external regulations. A strong governance framework provides a clear roadmap for data usage across the organization, ensuring consistency and accountability. Deciding where and how to store your data is crucial, Data Architecture drives the decision-making process. Today’s businesses are often deploying cloud first strategies for their scalability and cost-effectiveness. However, the choice between on-premises, cloud, or hybrid solutions must be aligned with the business’s specific needs, considering factors like data volume, compliance requirements, and operational flexibility. Effective data management starts with robust Data Modeling & Design. This process involves creating a detailed blueprint of how data is structured and interrelated within your organization. Data models not only define data elements but also set the relationships between them, serving as the foundation for building scalable and efficient systems. For large enterprises, data modeling must address complexity and ensure that databases are optimized for both performance and analytics. Choosing the right Data Storage solutions and managing the day-to-day Operations of those systems are critical components of effective data management. Data storage solutions must not only provide high performance, scalability, and reliability but also align with the company's compliance and security policies. Large enterprises need to consider data storage options that can handle high volumes of data and support high concurrency and rapid scalability. This might involve a combination of on-premises data centers and cloud-based storage solutions, tailored to specific types of data and usage patterns. In our next post, we will continue the foundational elements and considerations. If you have any questions or interest in discussing further, let's talk. #datamanagement #consulting #datagovernance

Foundational Thinking Pt. 1: Data Management In today's business environment, more than “just your data”, your data is key to your success. It is the backbone of an enterprise driving innovation and strategic decision-making. Managing vast amounts of data effectively has its challenges certainly. From ensuring data accuracy and accessibility to implementing solid security measures, the demands of effective data management are complex but critical to business success. Data management encompasses a range of practices aimed at acquiring, validating, storing, protecting, and processing essential data to ensure the accessibility, reliability, and timeliness of the data for its users. Our approach is founded on the DAMA “Wheel”, 11 key knowledge areas for sustainable data management. ·      Data Governance ·      Data Architecture ·      Data Modeling & Design ·      Data Storage & Ops ·      Data Security ·      Data Integration & Interop. ·      Document & Content Management ·      Reference & Master Data ·      Data Warehousing & BI ·      Metadata ·      Data Quality In subsequent posts we will breakdown key elements and considerations for the approach. If you have any questions or interest in discussing further, let's talk. #datamanagement #consulting #datagovernance

Great post from Brian Pedlar re: AI in healthcare, wound care in particular. The integration of AI in wound care underscores the critical role of data quality, trust in AI as well as ethical use of AI. 

We've discussed extensively the necessity of high-quality, private and publicly available, data sets as a crucial foundation for training AI systems and ensuring their quality output. With the EU AI Act mandating not only quality data sets but also robust data governance, the emphasis on AI optimization has reached a new level.   Level 3 High-risk AI systems, which rely on data-driven model training, must adhere to stringent data management and governance standards. Essentially, Level 3 AI systems are expected to utilize quality data sets to reduce risks and prevent discriminatory outcomes.   The Act states, "Appropriate data governance and management practices shall apply to the development of high-risk AI systems," and "Training, validation, and testing data sets shall be subject to proper data governance and management practices."   We commend these initiatives and suggest that there are further aspects to consider, such as establishing your company's AI ethical standards. This goes beyond technology to encompass your workforce, people development, and AI ethical governance. If you're seeking insights on the AI Act and AI ethics, let's talk. #datamanagement #ai #consulting #euaiact #datagovernance #ethicalai

The EU Artificial Intelligence Act compliance overview. Initially proposed by the European Commission on April 21, 2021, was passed on March 13, 2024. It will become effective twenty days after its publication and will be fully applicable 24 months thereafter. It is anticipated that other many countries will adopt the guidelines or something similar over time. There are several exceptions: - Bans on prohibited practices will take effect 6 months after enactment. - Codes of practice will be introduced 9 months after enactment. - General-purpose AI rules, including governance, will be implemented 12 months after enactment. - Obligations for high-risk systems will commence 36 months post-enactment. The Act categorizes AI risks into four levels, ranging from minimal to unacceptable: Level 1, Minimal or No Risk, includes applications like AI-enabled video games and, according to EU guidance, comprises the vast majority of AI systems currently in use within the EU. This category faces no restrictions. Level 2, Limited Risk, addresses transparency issues in AI utilization. The AI Act requires clear identification of AI interactions and content to foster trust and informed decision-making among users. Level 3, High Risk, demands significant compliance efforts from businesses using AI in critical areas such as infrastructure, safety, employment, essential public services, and law enforcement. High-risk applications must undergo a conformity assessment to meet the requirements outlined in Title III, Chapter 2, and upon approval, will be registered in the EU AI system database. This assessment includes risk management, data governance, technical documentation, record-keeping, transparency, user information provision, human oversight, and ensuring accuracy, robustness, and cybersecurity. Businesses will have 36 months to comply and register. Level 4, Unacceptable Risk, pertains to AI systems posing a direct threat to people's safety, livelihoods, and rights, and will be banned. *Image generated by DALL-E

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