Francesco Morini的动态

?? The Value of Data: In the bustling ecosystem of modern organizations, data is the lifeblood that flows through every department. But here's the kicker: its value transforms like a chameleon, taking on different hues depending on who's looking at it. To truly unlock this potential, companies must embrace four critical strategies that elevate data from a mere resource to a strategic powerhouse. For our intrepid Data Engineers, the value of data is in the magic they weave, transforming raw information from disparate sources into a cohesive tapestry within data lakes and warehouses. This is where data integration becomes crucial – connecting various sources to provide immediate, comprehensive datasets that can be used to drive organizational agility. ?? Zoom over to the Data Analysts, and you'll find them crafting compelling narratives from these harmonized datasets. They're the storytellers, weaving business context, definitions, and processes into insights that captivate and inform. Their work is a testament to the importance of establishing a data-driven culture, where every team member understands and values the power of data-driven decision-making. ?? Our Data Scientists emerge as modern-day alchemists, leveraging advanced analytics and predictive technologies to turn data into gold. They sculpt algorithms that address specific business needs with laser precision, transforming raw information into predictive models that anticipate market trends, customer behaviors, and potential challenges. ???? At the pinnacle, Senior Leadership harnesses these insights, making strategic decisions that propel the organization forward. But their success hinges on a critical foundation: robust data quality and governance. Ensuring data accuracy, protecting against biases, and maintaining a consolidated view becomes the bedrock of their strategic planning. ?? Here's the magic: this value chain is as interconnected as a spider's web. Without the Data Engineer's foundation, the Analyst's story might lose its plot. Without the Analyst's insights, the Scientist's model might miss its mark. And without all of them, Leadership's decisions could be sailing in foggy waters. It's a beautiful, symbiotic dance of data that requires continuous investment and strategic thinking. Quality is the secret sauce in this data value meal. As information flows from one process to the next, its integrity becomes paramount. By embracing real-time integration, fostering a data-driven culture, leveraging predictive technologies, and maintaining rigorous data governance, companies can transform data from a passive resource into an active driver of innovation and growth. Let's raise a toast to the power of strategic data management! ??#DataValue #BusinessIntelligence #DataDriven #Analytics

Building an effective Data Strategy: The Key to Unlocking Business Value In today’s data-driven world, organisations that thrive are those that transform raw data into actionable insights. But where do you start? The answer lies in a well-crafted data strategy—a comprehensive plan that ensures your data initiatives align with your business goals. Here’s how to build an effective data strategy: 1. Define Clear Business Objectives : Align your data strategy with the big-picture goals of the organisation: whether that is to optimise operations, improve customer experience, or drive revenue growth by identifying tangible problems in each.? A clear understanding of objectives ensures that every data initiative delivers tangible business value. 2. Conduct a Data Inventory: A comprehensive data inventory involves identifying all available data sources (internal and external), assessing the quality of the data (e.g., accuracy and completeness), and determining ownership and responsibilities. This process helps uncover gaps, redundancies, or silos that could impact your data initiatives and ensures you understand what resources you have at your disposal. 3. Establish Data Governance: Data governance provides the rules and frameworks to manage data effectively. This may include setting up policies for the use of data, assigning roles like data stewards or custodians for accountability, and ensuring that operations comply with regulations such as GDPR, HIPAA, or local privacy laws. Governance ensures your data remains secure, consistent, and trustworthy. 4. Invest in the Right Technology: The right toolset is important to enable the data strategy. Technologies of a data warehouse make information centralised; analytics platforms enable insights through visualisation, and integration tools simplify the flow of data across all systems. Your technology stack should simplify processes, increase productivity, and support scalability. Having good technology is not all without a good process. 5. Cultivate a Data-Driven Culture: Success in data strategies requires organisational buy-in. This may mean training employees to interpret and use data correctly, collaboration among departments to break down silos, and a need for leaders to step up in championing the cause of the value of data. When data becomes core to making decisions, the whole organisation benefits. 6. Measure and Optimise Continuously: A data strategy is not static—it will change with your business. Continuously measure the outcome with KPIs, such as data quality improvement, reduction in time-to-insight, or ROI on data initiatives. Use this feedback to further refine and adapt the strategy as goals, challenges, regulations, policies or market conditions change. #DataStrategy#DataGovernance#DigitalTransformation#DataDriven#DataManagement #DataCulture? #Analytics #BigData

Do you agree that quality of data is a challenge for insights and/or process improvement? If yes, please give a ?? If no, please give a ?? If you have a story or example to share, please do so, either below, in a direct message, or just give me a call. Kjartan Nesse Trond Ove H?ie Probal Sikder Sensa AS #TrustedData #QualityData

My Personal Learnings on Common Data Strategy Pitfalls After years of crafting, adjusting and implementing data strategies in various contexts and organizations, here is my very personal summary of the most common derailers and pitfalls to watch out for. While essential aspects such as IP, architecture & tech, security, governance and standardization are key elements of any organizational data strategy, there are pitfalls lurking beneath the water-line that can make any ship sink if not being kept in focus and check: Siloed Focus A siloed organizational structure with vertical focus on KPIs can easily create multiple versions of "the truth" with data on the same asset residing in separate databases, using different data models, identifiers or naming conventions. This makes it hard to get an end-to-end view on a single product or asset across the organization. Data needs to seamlessly travel horizontally through the organization, test for it! Decision Making Culture The best data strategy and analytical capabilities won't have much impact if the organization hasn't embraced a data driven decision making culture. Leaders and teams are well advised to repeatedly examine their decision making process and how data / fact driven it truly is. Company Identity Recently built tech companies see their data as a strategic asset, there is no doubt about its value and the need to maximize its value and impact. For companies that historically are producing "tangible products", the notion of data having any value beyond its primary reason for creation is not straight forward. Special attention is required to transparently show the causal links of quality data (or lack of!) through the entire value value chain to the front desk. Trust The necessity of data seamlessly traveling across an organization has already been mentioned. A major determinator of a successful implementation is the trust people and teams have built in sharing "their" data with each other. Understanding that "your data" actually is the company's data and we all are its custodians with the mandate to care for it and make it as available as possible is key. Data Specialists Workforce Data without context (i.e. metadata), standards and ontology is only of limited use. This limitation will only be exacerbated if the company's ambition is to move towards automation and AI. Adding these elements to your raw data requires hands on deck, experts who have the mandate and fit-for-purpose tools to do their job. There is a risk to solely focus on algorithms and insights, assuming that a data strategy and tech will take care of the foundations. Your data managers, engineers and curators are the essential backbone that hold your data strategy upright! Curious to hear your thoughts and feedback. How are you tackling the data icebergs?

Beyond the Medallion: Why Raw, Curated, Analytics is the Key to Data Democratization Those that know me well , know my thoughts and feelings about the medallion model. The model is inadequate to support true data democratization. In today's data-driven world, where every team needs access to insights, it's no longer enough. The medallion model, while useful for structured data and BI reporting, often falls short when it comes to true data democratization. Enter the Raw, Curated, Analytics approach – a more flexible and accessible framework that empowers everyone in your organization. The Limitations of the Medallion Model Slow to Adapt: The rigid structure of bronze, silver, and gold layers can be slow to accommodate new data sources and evolving business needs. This creates bottlenecks and delays access to critical information. Limited Data Variety: The medallion model is primarily geared towards structured data. It struggles to handle the diverse formats (semi-structured, unstructured) that modern businesses rely on, such as social media feeds, sensor data, or text documents. Centralized Control: Data transformation is often controlled by a central team, creating dependencies and limiting the ability of individual teams to explore and analyze data independently. The Power of Raw, Curated, Analytics This modern approach emphasizes flexibility and accessibility, making data a shared asset across the organization. Here's how it works: Raw: All data is ingested in its raw format, preserving its original context and detail. This creates a single source of truth accessible to everyone. Curated: Data is curated for specific use cases, but not necessarily transformed into a single, rigid schema. This allows for flexibility and supports diverse analytical needs. Analytics: Data is readily available for exploration, analysis, and visualization by different teams using a variety of tools and techniques. Benefits for Data Democratization Faster Time to Insight: Teams can access raw data directly and curate it for their specific needs, eliminating bottlenecks and accelerating the analytical process. Supports Diverse Use Cases: The flexibility to handle various data formats empowers teams to explore new possibilities, from machine learning to text analytics. Empowered Decision-Making: By providing broader access to data and tools, the Raw, Curated, Analytics approach enables data-driven decision-making at all levels of the organization. Fosters Data Literacy: When data is more accessible, individuals across different departments become more comfortable working with it, fostering a data-driven culture. While the medallion model remains relevant for certain use cases, it's no longer sufficient in a world where data democratization is essential. The Raw, Curated, Analytics framework provides a more agile and accessible approach, empowering everyone in your organization to leverage data for better decision-making and innovation.

From Data Deluge to Data Democracy: How Self-Service Data Management (SSDM) Empowers Your Workforce Data: It's the lifeblood of modern business, promising deeper customer insights, optimized operations, and a significant edge over the competition. IDC predicts that the global datasphere will reach a staggering 175 zettabytes by 2025. Yet, a paradox grips businesses: despite this abundance, 64% of data initiatives fail to deliver expected value. Why? Because most data remains trapped in isolated silos, inaccessible to the very people who need it most – the business users. This is where Self-Service Data Management (SSDM) becomes a game-changer. It's not just about technology; it's a cultural shift empowering everyone in your organization – from C-suite to application & data users to become a citizen data scientist. The rise of the citizen data scientist is a major driver. Business users are hungry for data-driven insights to make informed decisions. SSDM provides user-friendly interfaces, democratizing data and fostering a culture of data ownership that don't require a PhD in tech. In today's fast-paced environment, waiting for IT to extract data creates bottlenecks. SSDM cuts through the red tape, enabling real-time access and analysis, leading to quicker decisions and faster response times. But the benefits extend far beyond speed.? SSDM fosters collaboration, turning data into a shared resource. Teams can work together to uncover hidden gems and solve problems more effectively. This collaborative spirit translates to a better customer experience.? Imagine a marketing team wielding data to personalize campaigns and a customer service team anticipating customer needs – all powered by SSDM. The impact is undeniable. A Forrester study revealed a 43% increase in employee productivity and a 30% reduction in data analysis times for organizations implementing SSDM solutions. McKinsey adds another compelling data point: data-driven organizations are 23 times more likely to outperform competitors on profitability measures. SSDM isn't a fad; it's a fundamental shift. By democratizing data, you unlock innovation and propel your organization towards sustainable growth. It's a cultural metamorphosis for the digital age. Ready to dive deeper? Explore our newest blog where we delve into the potential of SSDM and provide actionable insights on crafting and executing a winning strategy. Unleash the full power of your data assets and empower your workforce to become data rockstars. https://lnkd.in/da73VPbr #datamanagement #data #datademocracy #dataownership #datadeluge

The transition from data deluge to data democracy is crucial for businesses, and Self-Service Data Management (SSDM) plays a pivotal role in this transformation. SSDM empowers every level of an organization, enabling not just technological advancements but also fostering a cultural shift towards data ownership and collaboration. Excited to see how organizations leverage SSDM to drive innovation, improve decision-making, and enhance customer experiences!

Harness the Power of Data-Driven Decision Making ? Do your eyes glaze over when you hear about data? ?? Data may SEEM boring, but if you understand it, an entire world of digital transformation is at your fingertips. Data-driven decision-making isn't just a trend; it's a necessity. Data touches all we do and will grow in the future. Protecting data is critical. Using it to make decisions is a superpower. After decades of working in big data, data analytics, data management, and transforming business objectives with data, I've seen firsthand the impact data has on an organization's success or failure. Knowing how to manipulate data or search for information in enterprise-level warehouses isn’t enough. You must understand data transformation initiatives and data storytelling. ?? Leveraging data allows informed decisions vs. relying on gut feelings or assumptions, leading to more accurate and reliable outcomes. ?? In compliance-critical industries, data management plays a crucial role in meeting consent orders and regulatory requirements. Proper data handling ensures transparency and accountability. ?? Data isn't just numbers; it tells a story. Translating data into compelling narratives communicates insights effectively, influences stakeholders, and drives strategic initiatives. ?? Data transforms objectives with insights into customer behavior, market trends, and operational efficiencies, enabling companies to pivot and adapt with agility. Tips for data-driven decision-making: ? Invest in quality data management foundations for all data-driven initiatives and ensure your data is accurate, up-to-date, and easily accessible. ? Foster a data-driven culture, encouraging all levels to embrace data. Provide training and resources to understand and utilize data in daily roles. ? Use advanced analytics tools to uncover deeper insights. Predictive analytics, machine learning, and AI can provide a competitive edge by forecasting trends and identifying opportunities. ? Use data visualization and storytelling to make data accessible and engaging. Tailor communication to your audience to ensure it resonates. ? Ensure your data initiatives align with strategic goals, ensuring data-driven insights are actionable and relevant. ? But don’t stop there. The biggest concern I heard while managing a corporate-wide internship program was people were afraid to work with data because they weren't trained to handle it, interpret it, or understand it. ? If you want your company to be ahead in data transformation, start by providing training for all employees to use and understand data in their daily roles. Data-driven decision-making is more than a buzzword - it's a strategic way of operating. The benefits of harnessing the power of data are endless. Start simple but aim high in the data revolution to unlock its full potential. What experiences have you had with data impacting your decisions at work? Share in the comments. ??

?? Accelerating Growth: How the Chief Data Officer (CDO) Drives Company Success! ?? In today's data-driven world, the Chief Data Officer (CDO) isn't just a title—it's a catalyst for growth and innovation. Here's how the CDO plays a pivotal role in driving the company's success: 1. Data Strategy Architect: The CDO crafts a data strategy aligned with the company's vision and objectives. By identifying opportunities to leverage data assets for growth, the CDO charts the course for maximizing the value of data across the organization. 2. Cultural Catalyst: Transforming the company's culture into one that values and leverages data is key. The CDO champions data literacy and fosters a data-driven mindset at all levels, empowering employees to make informed decisions based on data-driven insights. 3. Innovation Driver: The CDO spearheads innovation initiatives fueled by data. Whether it's developing new products, optimizing processes, or enhancing customer experiences, data-driven innovation is at the heart of the CDO's agenda, driving competitive advantage and market differentiation. 4. Customer Centricity: Understanding customer needs and preferences is paramount for growth. The CDO leverages data analytics to gain deep insights into customer behavior, enabling personalized experiences, targeted marketing campaigns, and product innovation tailored to customer demands. 5. Operational Excellence: Data-driven decision-making isn't just for the boardroom—it permeates every aspect of the business. The CDO harnesses data analytics to optimize operations, streamline workflows, and drive efficiencies across departments, enhancing productivity and profitability. 6. Risk Mitigation: With great data comes great responsibility. The CDO ensures data governance and compliance measures are in place to mitigate risks associated with data privacy, security, and regulatory compliance, safeguarding the company's reputation and trustworthiness. 7. Partnership Builder: Collaboration is key to success in today's interconnected world. The CDO forges partnerships with internal stakeholders, external vendors, and industry peers to share data, insights, and best practices, driving collective growth and innovation. 8. Performance Measurement: The CDO establishes metrics and KPIs to track the impact of data initiatives on company growth. By measuring and analyzing data-driven outcomes, the CDO continuously refines strategies and investments to maximize ROI and drive sustained growth. In essence, the CDO isn't just a guardian of data—they're a growth enabler, driving transformation, innovation, and value creation across the organization. Ready to unlock the full potential of data-driven growth? Let's embark on this journey together! ???? #CDO #DataDrivenGrowth #InnovationLeadership

?? Exciting News! ?? As the Director of Innovation, I am thrilled to share with you an incredible article titled "Data Strategy 101" that provides a comprehensive overview of what data strategy is, its importance, best practices, challenges, and the concept of a data strategy roadmap. ?? Data strategy is like a master plan or blueprint that helps organizations confront business challenges and achieve predefined goals using data. It involves aligning technologies, processes, and people through their data roles and responsibilities. The article emphasizes the need for organizations to treat data as a business asset and transform their business ecosystem into a data-driven entity. ?? One of the key takeaways from the article is that many organizations struggle to align their data strategies with their organizational strategies. But fear not! The article highlights that impactful data strategies are emerging due to advancements in technology and the maturing of enterprise artificial intelligence. ?? The guiding principles of an enterprise data strategy include streamlining data acquisition processes, making data easily accessible and shareable, eliminating data silos, integrating disparate data, and developing consistent data usage and management goals. These principles are crucial for organizations looking to harness the power of data and drive innovation. ?? The article also introduces the concept of a data strategy roadmap, which provides a set of granular steps and action plans for achieving predefined business goals. This roadmap includes implementation plans, resources, schedules, costs, and deliverables. It emphasizes the importance of choosing tools and technologies based on precise requirements and assessing the current data systems, processes, and workforce for necessary changes. ??? In addition, the article discusses best practices for data management, such as establishing policies and procedures for consistent and efficient data usage across the organization. It also highlights the importance of data governance in ensuring data quality and security. ?? Now, I want to hear from you! Have you encountered any challenges aligning your data strategy with your organizational strategy? What steps have you taken to overcome them? Share your thoughts and experiences in the comments below! Let's learn from each other and foster a community of data-driven innovation. ?? #DataStrategy #DataDriven #Innovation #BusinessTransformation