Nexla’s Post

We understand what it's like to work at a tech-savvy company. You're committed to the AI journey, you've secured the budget to get started, yet you're still facing the following top 5 challenges around data quality and accessibility: ◻Silos and Fragmention - your data might be spread across various departments or systems, which creates data silos. This fragmentation can make it very challenging and time consuming to gather a comprehensive view of your data. ◻Data Governance and Standardisation - We all know how frustrating it can be to create standardisation of data input across large organisations, from naming conventions, formats, structures, through to quality levels - this creates inconsistencies in your data and makes it difficult for you to integrate and use it effectively. ◻Legacy Systems and Integration Issues - Perhaps your company has existing legacy systems which don't easily align with modern AI-driven data processing requirements. Integrating these systems in a compatible way, whilst - most importantly - maintaining data quality, can be a significant challenge. ◻Data Security & Compliance - Ensuring that your data is accessible whilst maintaining security and compliance with regulations like the GDPR and industry-specific standards like Consumer Duty is crucial. Balancing open access with the right level of security measures is a delicate task. ◻Dynamic Data Ecosystems - With constantly evolving data sources and technologies, it can be a exhausting just trying to keep up with the changes let alone work out how to implement them and ensure they meet quality standards. Addressing these challenges is not just a financial challenge, it requires a holistic approach that analyses your data, products and processes; identifies your company objectives and opportunities, and set's out a roadmap and shopping list that fits your companies goals. Gain a deeper understanding of how A.I. can grow your business with Assess from Raiven A.I. 🗨✉ Comment or DM us for more information on how we can help you meet your commitments. #DigitalTransformation #DataCollection #DataDriven #AIForBusiness #RaivenAI

Artificial Intelligence is a data driven technology. The better your data strategy, the better the results, and the easier the transition. With the growth in A.I., now is the time to get on top of your data! Here's some key things to remember: You Have More Data Than You Realise - every organisation underestimates their data footprint. Your data is more than just what you put in your database, you're collecting and generating data in all you do. How much of it are you using? Keep Data at the heart of the Development Process - the more applications rely on your data, the harder it is to go back and fix any issues. If your systems are built on shaky foundations, they will begin to crack when deploying at scale. How are you ensuring your data is managed efficiently? Make Your Actions Measurable - collecting data is one thing, but why collect it unless you are using it to add value. Ensure your actions and metrics are measurable, and validate them with your data!

Data Validation: A critical process that is often overlooked when migrating data. With the focus on digital transformation, customer/employee experience and AI, data is getting more attention than ever. This makes it even more surprising that one of the most overlooked processes when migrating data is validation! But, difficult to believe or not, it is the case and one of several reasons that, as Gartner research has shown, 83% of data migration projects either fail outright or fail to meet time and budget expectations. Click here to read the full blog: https://lnkd.in/ev67v-fM #ITSM #ServiceManagement #PrecisionBridge #DataMigration #DataArchiving #BlogPost #DataValidation

Data Drives Delivery: Are We Getting It Right? In our technology-driven world, data is the lifeblood of every system we engage with.  From personal interactions to professional services, quality data is more crucial than ever.  Yet, many of us face daily frustrations due to poor data management. Who hasn’t been irritated by these common scenarios? * Repeating your details despite authenticating on a system? * Re-explaining an issue every time you seek help? * Finding incorrect information about yourself with no easy way to fix it? * Making multiple calls to resolve the same problem with no progress? * Having to look up information you’ve already provided just to continue a process? These experiences are often the result of sub-standard or mismanaged data.  Sometimes, the friction is even intentional, designed to frustrate users into giving up.  Organisations claiming professionalism should be held accountable for such tactics. More often, however, these issues stem from systems that weren’t designed with a first-principles approach.  These inefficiencies affect not just personnel but also partners and participants, leading to a slow “death by a thousand cuts.” The consequences?  Degraded user experiences, impaired growth, increased costs, and lost opportunities. No organisation—whether B2B, B2C, or B2B2C—can afford this. The root cause is often understandable.  Systems that worked well initially fail to scale, or products are developed reactively without considering the bigger picture.  Missing elements like Additional Data Derived (ADD), Imported & Transformed (IT), and Unifying Protocols (UP) lead to missed opportunities. But these problems are fixable.  Addressing them is less daunting and more beneficial than ignoring them.  By examining and refining our systems, we can improve not just organisational efficiency but also partner and participant experiences. This evolutionary approach enhances organisational strengths, weeds out frustrations, and highlights areas for improvement, including user experience and accessibility. Understanding how to effectively manage data within a Common Object Repository for the Enterprise (CORE) brings immense benefits—governance, security, privacy, recoverability, and resilience.  It opens new opportunities for growth and expansion. Ignoring these basics inhibits growth, obscures opportunities, and alienates allies.  Data Drives Delivery—let’s get it right. What are your thoughts? Have you experienced similar frustrations? For the full post, check out the link here: https://lnkd.in/eJPDdEsf #DataDriven #AI #DataQuality #CustomerExperience #TechInnovation #BusinessGrowth #DigitalTransformation #Efficiency #SystemsThinking #Leadership #ProcessImprovement #B2B #B2C #NICEApproach #CorePrinciples

Is your financial institution struggling to navigate the complexities of data management? Our new blog series, "Data Catalog in the Financial Services Industry," dives deep into how a robust data catalog can streamline operations, enhance compliance, and drive innovation. In Part 1, we explore the critical role of data catalogs in today's data-driven world. Discover how Hoonartek's expertise can help your organization: - Unleash the power of your data assets - Improve decision-making - Meet regulatory requirements Stay tuned for Parts 2 and 3 where we'll delve into implementation strategies, challenges, and success stories. In the mean while, feel free to contact us to readily answer your data challenges. Visit - https://lnkd.in/gVcAjmwj #DataCatalog #FinancialServices #DataManagement #DataGovernance #Hoonartek #DataSolutions #Data #DatatoAI #AI #ML #FinTech

There are many reasons that you may be looking to upgrade your data platform. Clearly defining your objectives will help you choose the right approach, allowing you to quickly realise benefits such as improved performance, cost savings, better integration, enhanced security, and the incorporation of modern capabilities like AI. This clarity will also help you stay on track throughout the process. In this month's Digital Pulse, Mohighmin(Mo) Bashir and I break down the advantages and challenges of various high-level upgrade approaches to help you choose the right one for you. Find out more here: https://pwc.to/3A0m9J6  #DataPlatform, #DataStrategy, #StakeholderEngagement, #IterativeDevelopment

In our earlier outreach, the feedback was that you wanted to know more of the technology we develop for ours customers which is also Open-Source, so this is a pretty long-due post on Xorcery and what you can harvest from the Xorcery codebase, here is what ChatGpt tell us: Introducing eXOReaction's Next-Gen Analytics Platform For data engineers, analysts, and architects who've grappled with the complexities of real-time data analytics within multi-tenant environments, eXOReaction unveils a groundbreaking solution that redefines the boundaries of data processing capabilities. Our platform uniquely integrates: Real-Time Streamed Data Querying: Leveraging cutting-edge technology to process and query streamed, semi-structured data in real-time. This capability ensures instantaneous analytics and insights, directly influencing decision-making processes as events unfold. Dynamic, Fine-Grained Access Control: A first-of-its-kind implementation that allows dynamic access control policies at an unprecedented granularity—per field, per user—within the same query. This innovation not only secures sensitive information but also customizes data views for individual users, aligning with both privacy regulations and user-specific data needs. Comprehensive Analytical Functions: From advanced aggregations to detailed histograms and time-series analyses, our platform offers a wide array of analytical functions. These tools are designed to work seamlessly with real-time data, providing both the breadth and depth of insights required to navigate today's data landscapes. Native Multi-Tenancy Support: At its core, our solution is built for scalability and isolation, accommodating multiple tenants without compromising on performance or security. This aspect is crucial for SaaS providers and enterprises seeking efficient data analytics solutions that can scale with their growth. This convergence of features—each innovative on its own, yet unparalleled when combined—marks a new era in data analytics. By facilitating real-time insights with granular access control and comprehensive analytical tools within a multi-tenant architecture, eXOReaction's platform is not just an analytics solution but a transformative tool for businesses ready to leverage their data in real-time.

On September 24th, we (Qbiz Inc.) hosted an executive discussion on #datagovernance in Palo Alto. Our experts summarized the conversation into the key points below. Leadership in the data world joined us from multiple industries, especially #banking and #finance. How does this line up with what you and your teams are discussing? 1. #AI and #Data Governance: • Concerns over AI governance, especially in safeguarding responses from chat-GPT style prompts to prevent unauthorized access. • #GenerativeAI is being leveraged (e.g., Pinterest) for automating metadata and data dictionary creation. • Monte Carlo's ML harnesses help with immediate data quality (DQ) safeguards during tool onboarding. 2. Evolving Tool Landscape: • Tools like Alation, Soda, and OneTrust were mentioned, but discussions showed participants were overwhelmed by the variety. • Catalog and lineage tools are prioritized as foundational for data quality. 3. Measuring Data Governance Value: • Most are navigating this without strict methodologies. • MDM (Master Data Management) overlaps with data governance, but its placement is unclear. 4. Data Cultures: • Financial services focus on #compliance, while high-tech and retail sectors prioritize #enablement and #efficiency. 5. Evolving Policies for AI: • Concerns about enforcing policies on AI usage and monitoring #LLM (Large Language Model) integrations. • Calls for improved visibility in data catalogs regarding AI/ML #models used. 6. AI in Data Governance Tools: • Vendors are integrating #AI to reduce manual data stewardship. • Human validation is still needed to oversee automatically generated recommendations. 7. Challenges with Data Governance and Management: • Tension exists between data management and data governance roles, with data governance leads advocating for oversight of these tools. 8. Measuring ROI in Data Governance: • Operational metrics include data quality and catalog completeness. • ROI is often intangible, focusing on risk prevention and compliance. 9. Data #Enablement: • Balance between limiting access to necessary data and simplifying access for self-sufficiency. Gustavo Bermudez Jeff Rosen Scott Mitchell

With the emergence of next-generation technologies, data is a vital need for any modern organizations. However, many organizations face challenge disparate systems, lack of processes, duplicated data, poor quality, and non-compliance. Experts at Info-Tech Research Group and Semarchy sat down to discuss the need for Master Data Management and how it plays a huge role for modern businesses that want to grow. Watch the replay: https://hubs.ly/Q02SzTdD0 #mdm #GenAI #AI #datamanagement #masterdatamanagement #data #dataplatform

The semantic layer plays a crucial role in enabling the development of data products that can be discovered, understood, and trusted, without which their value is unlikely to be realised