Data Dynamics的动态

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!

DATA STRATEGY: Unlocking Value by 'TURNING DATA into WISDOM’ - A Foundational MODEL for a Data MONETIZATION STRATEGY… I recently devised a Data Strategy, comprising a Data Governance Program, Data Product Development Framework, a Business Capability Model (for determining the Organizational Gap in dealing with the Data Practices involved); and an Advanced Data Monetization Model (attached). Let’s delve a bit into the most relevant components: Start off by recognizing that your data is a strategic asset. It’s not just about collecting and storing it; it’s about extracting value from it. One powerful avenue for realizing this value is through Data Monetization. In this post, I’ll explore how your organization can leverage #DataProducts and a robust data strategy to drive business growth and gain a competitive edge. DATA STRATEGY through the #FourLenses, an approach developed by Bernard Marr (Bernardmarr.com) that truly simplifies the otherwise complex undertaking of aligning the Business and data Strategy while bridging to execution in a fast-track mode. DATA PRODUCTS - FUELING INNOVATION Data products are the building blocks of data monetization. These are curated, cataloged, cleansed, well-structured and modeled datasets designed to meet specific business domain or market needs. By packaging data into valuable products, organizations can empower data consumers with actionable insights. Heck! Data Product ????? A premium Data Product is Google Maps. You can’t barely fathom the depth of knowledge and technology enabling the knitty-gritty to allow you to seamlessly go from A to B. DATA PRODUCTS DEVELOPMENT LIFE CYCLE To kickstart your data monetization journey, adopt a structured approach—the Data Product Development Cycle(fully explained in the framework). This methodology ensures that data products are launched and continuously refined, validated, and aligned with business objectives. It encompasses stages such as Business Requirements, Design, Development, Testing, Certification and Deployment. DATA GOVERNANCE PROGRAM Effective data monetization requires a robust governance model. The framework includes a layered lean ‘Data Governance Model’, underpinned by a ‘Business Capabilities Model’. The Business Capability Model purpose is to Identify organizational gaps in the ‘Data-related’ practices. TURNING DATA INTO WISDOM - An ADVANCED MONETIZATION MODEL Leveraging advanced analytics, Static or Operational Machine Learning models, closing the Internal Capabilities gaps, Data monetization isn’t just about selling datasets; it’s about turning data into wisdom. Imagine integrating Data Science, advanced AI models, and real-time Pipeline Management. But Monetization also has a ‘market purview’, wherein organizations harness their data acumen and seasoned Data Management capabilities to build data-driven value-added service offerings for their customers, partners or open market. just picture a world without Google Maps nowadays!??????

How Most Organizations Get Data Strategy Wrong?—?and How to Fix It https://ift.tt/uJtk1yT Wave by CLEANPNG. DEMYSTIFY DATA?STRATEGY How Most Organizations Get Data & AI Strategy Wrong?—?and How to Fix?It Redefining Data Strategy to Drive Competitive Advantage with Data, Analytics and?AI Unlocking business value from data, analytics, and Artificial Intelligence is a hot topic. Yet, most organizations struggle to monetize existing data or to create new data that advances their thinking. I assert that in most cases, this struggle stems from a lack of effective business strategy?design. This is the third installment in a series demystifying data strategy— something that organizations seem to need to become data-driven and remain competitive in today’s digital world. This document serves as a playbook for data strategy, offering a deep and structured exploration by addressing misconceptions, clarifying definitions, presenting actionable frameworks, and illustrating concepts through a detailed?example. I am Jens, a business-minded data expert with nearly two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms. tl;dr A data strategy is not a standalone supplement to business strategy, nor is it a plan for building data capabilities. It is rather the strategy of a data function, providing services to the internal data customers. A data strategy does not define how an organization creates value with data, analytics and AI. This is the job of the business strategy with data-related strategic choices being an integral?part. Misconceptions about data strategy, combined with weak business strategy design, prevent organizations from leveraging data effectively. These issues hinder their ability to gain a competitive advantage. The Playing to Win framework helps organizations with both: embedding data innovation into their business strategy and designing effective data strategies. TABLE OF?CONTENTS 1 THE FUZZ ABOUT DATA STRATEGY 1.1 Data Can Create Benefits Throughout the Organization 1.2 Organizations Struggle to Become Data-Driven 1.3 The Data Strategy Promise 2 DATA STRATEGY MISCONCEPTIONS 2.1 Misconception #1: A Plan to Build Data and AI Capabilities 2.2 Misconception #2: Approaches and Guidelines How Data is Managed 2.3 Misconception #3: A Supplement to Business Strategy 2.4 Quo Vadis? 3 STRATEGY 3.1 The Playing to Win Strategy Framework 3.2 What Strategy Actually Is 3.3 Plan vs. Strategy: The Typical Misunderstanding 3.4 The Strategic Choice Structuring Process 3.5 What Strategies an Organization Needs 3.6 Reinforcing Rods 3.7 What Strategies an Organization Doesn’t Need 3.8 Strategy Design Teams 3.9 Summarizing Strategy Fundamentals 4 DATA STRATEGY DEFINITION & DESIGN 4.1 Defining Data Strategy 4.2 Data Strategy Choice Cascade 4.3 Data Strategy Design Process 4.4 Step I: Identify...

?? 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

I was scrolling through federal opportunities in sam.gov (because I’m a masochist) and I keep coming across the term data management used in ways that well ... stink. Everyone describes the system that “manages” the data that they want and well…keeps the data over there where it won’t hurt anyone and looks organized. That’s not what data management is! Oh and you also don’t want data management if you’re talking about a system that does ETL, measures the accuracy or algorithm performance, or deploys AI Agents….definitely not. ?? The Misuse of the term "Data Management" is Holding Us Back ?? For years, in Defense we’ve been using the term data management to describe everything from data storage, to governance, to compliance, to model development, and to even just organizing data in spreadsheets. But here’s the problem — data management doesn’t capture the true potential of what can be achieved with data when it’s done right. And more importantly, it often downplays the real value that comes from effectively using data to drive organizational decisions, innovation, and growth. This is an important conversation when we’re talking about integrating Artificial Intelligence solutions. Quality data leads to quality AI solutions. When we say data management, we tend to frame it as a purely behind-the-scenes task. It's seen as a burden, a necessary evil that just needs to be handled. But in reality, data is one of the most valuable assets we have! It's not just about “managing” it, it's about unlocking its potential to create insights, drive innovation, and fuel decision-making at every level of an organization. Think about it — data is a catalyst. When treated with the right mindset and tools, the right data can transform the entire battlespace. It’s not just a resource to be “managed” but an opportunity to be leveraged for strategic advantage. So, I’ll be blunt: it’s time to stop using “data management” as a catch-all phrase that undermines the power of data. Let’s start talking about data operations, data strategy, and data-driven transformation — terms that actually reflect the value and impact that well-governed, well-analyzed, and well-leveraged data can bring to the table. When we reframe the conversation around empowering teams to make smarter, more informed decisions, we open the door to real innovation. This is what modern data practices are all about — turning data into action, not just a resource to be maintained. Maybe use the free data operations manual we created called ciuTshi. Link is in the first comment. Let’s change the narrative. Let’s stop managing data and start unlocking its potential. ?? #DataManagement #DefenseTech #AI #DataValue #sKG Dan Folliard Melissa Johnson Lisa Sanders David Radell Heather Anderson, MBA Chad Raaymakers Nicole Nemmers Eugene Kuznetsov Waseem Naqvi Michael Stewart Jon Lazar Christopher Knapp Michael McDermott Tony Crotwell Kristofer Doucette, CFE Robert “Ramsey” Oliver Stu Bradin David Spirk

?? **Unlocking the Power of Data: Crafting an Effective Data Strategy** ?? In today's data-driven world, having a robust data strategy is essential for any organization's success. But what exactly is a data strategy? It's a comprehensive plan that outlines how a business will collect, manage, analyze, and utilize data to achieve its goals. Here are the **key components** of a data strategy: 1. **Data Governance:** Establishing policies for data quality, security, and privacy. 2. **Data Management:** Organizing and maintaining data infrastructure. 3. **Data Analytics:** Generating insights through BI, machine learning, and AI. 4. **Data Architecture:** Designing the technical framework for data processes. 5. **Data Integration:** Ensuring seamless data flow across systems. 6. **Data Culture:** Promoting data literacy and data-driven decision-making. **Examples of Data Strategies in Action:** - **Customer-Centric Data Strategy:** An e-commerce company uses customer data to personalize recommendations and marketing efforts, boosting engagement and sales. - **Operational Efficiency Data Strategy:** A manufacturing firm uses IoT sensor data to predict maintenance needs and prevent downtime, improving productivity. - **Innovative Product Development Data Strategy:** A tech company analyzes market data to identify trends and inform new product development. **Types of Data Strategies:** 1. **Offensive Data Strategy:** Focused on growth and competitive advantage. Ideal for marketing optimization, sales growth, and product innovation. 2. **Defensive Data Strategy:** Focused on managing risks and ensuring compliance. Essential for data security, regulatory compliance, and risk management. 3. **Operational Data Strategy:** Focused on enhancing internal processes and efficiency. Perfect for process automation, resource optimization, and performance tracking. **When to Use Each Type of Strategy:** - **Offensive:** When aiming to drive revenue growth, enter new markets, or innovate. - **Defensive:** When focusing on protecting against risks and ensuring compliance. - **Operational:** When seeking to improve internal operations and achieve cost efficiencies. By choosing the right data strategy—offensive, defensive, or operational—organizations can unlock significant value, improve decision-making, and achieve sustainable growth. ?? How is your organization leveraging data? Share your thoughts and experiences! ?? #DataStrategy #DataAnalytics #BusinessGrowth #DataGovernance #Innovation #OperationalEfficiency #DataDriven --- Feel free to adjust the tone and details to better fit your personal style or organizational voice.

?? Unleashing the Power of Big Data Analytics ?? As the Director of Innovation, I am constantly amazed by the transformative role that Big Data Analytics plays in reshaping decision-making processes and driving innovation in the realm of modern business intelligence. Today, I want to share with you the incredible potential of Big Data Analytics and how it can navigate strategic complexities and unlock new possibilities for your organization. Big Data Analytics is all about addressing the Three V's - Volume, Velocity, and Variety. It provides the tools and techniques to process and make sense of the massive volume of data generated on a daily basis, analyze and act on data in real-time, and handle diverse data types. [3] [2] [1] By leveraging advanced analytics techniques like machine learning, data mining, and predictive modeling, organizations can extract meaningful insights from data and make data-driven decisions. Imagine being able to gain a deeper understanding of your business processes, customer behavior, and market trends. With Big Data Analytics, you can optimize operational efficiency, personalize customer experiences, identify growth opportunities, mitigate risks, and stay ahead of the competition. It's like having a crystal ball that helps you make smarter, more informed decisions. But it's not all sunshine and rainbows. There are challenges and considerations in the field of data analytics that we must address. Ethical considerations and data privacy, cybersecurity challenges and compliance, scalability and data quality, and the interpretability of analytics models are just a few of the hurdles we need to overcome. By upholding ethical standards, ensuring data security, maintaining data quality, and enhancing the interpretability of analytics models, we can fully harness the power of Big Data Analytics. So, how can you unlock the potential of Big Data Analytics in your organization? Start by defining clear objectives and investing in the right talent and technology. Foster a data-driven culture that promotes collaboration across departments. Embrace data literacy and adopt an iterative approach to data analysis. By following these best practices, you can leverage Big Data Analytics to drive insights, make better decisions, and gain a competitive edge. I would love to hear your thoughts on Big Data Analytics and how it has transformed decision-making in your organization. Share your experiences and let's continue the conversation! #BigDataAnalytics #DataDrivenDecisions #Innovation #DigitalTransformation References: [1] The Impact of Big Data on Business Strategy and Decision-Making: https://lnkd.in/d-i35twC [2] Data Analytics Simulation: Strategic Decision Making: https://lnkd.in/dnyB-JST [3] Making Sense of Big Data: The Impact of Analytics on Decision-Making: https://lnkd.in/dwWxD8zj

Unlocking the Value of Data - Data Monetization Recently, I have been asked “How do you get value out of data?” or in more now-a-days terms How do you monetize data? Well….. all you need to do, is to bring your data to the bank and cash it simple isn’t it !.?? Let’s get serious,. Shall we!? In today's digital age, data is often recognized as the new oil (Which I really hate!). But what does it really mean to monetize data, and how can organizations harness its full potential? ? What is Data Monetization!? Data monetization is the process of turning data into revenue. It involves leveraging data to create tangible economic benefits. This can be achieved through direct sales, creating new products or services, improving efficiency, or enhancing customer experiences. There are various ways to achieve data monetization - Direct Monetization: Selling raw data or insights to third parties. Think market research firms, advertisers, or other businesses looking for valuable insights. Indirect Monetization: Using data to improve products, services, or processes. For instance, personalizing customer experiences or optimizing operations to cut costs. Alright …alright… alright…. How can organization benefit from this?.... just keep reading ! ? Benefits for Organizations - Revenue Growth: New income streams from selling data or data-driven products. - Competitive Edge: Better insights lead to more informed decisions, giving companies a strategic advantage. - Customer Satisfaction: Personalized experiences enhance customer loyalty and satisfaction. ? Annndddd….there is a framework !! …. Keep reading ???? Frameworks for Data Monetization To effectively monetize data, organizations can follow structured frameworks: - Data Collection: Ensure high-quality, relevant data collection across all touchpoints. - Data Management: Implement robust data management practices to organize and maintain data integrity…. This is what I do ???? - Analytics and Insights: Utilize advanced analytics to extract meaningful insights from data. - Monetization and Implement Strategy: Develop clear strategies for how data can be turned into revenue, ? So, is it really this simple….. no, of course not, there are challenges …Keep reading ?? ? Transforming Challenges into Opportunities Embracing data monetization is not without its challenges. Privacy concerns, data quality issues, and technological hurdles are real. However, these challenges can be turned into opportunities with the right mindset and approach. Investing in robust data governance, adopting cutting-edge technologies, and fostering a data-driven culture are key steps toward success. Conclusion Data monetization is more than just a buzzword—it's a powerful strategy that can transform how businesses operate and grow. By unlocking the hidden value in data, organizations can create new revenue streams, drive innovation, and stay ahead in the competitive landscape. #DataMonetization #DigitalTransformation #DataDriven

Is bad Data Quality crashing in and ruining your best laid plans and projects? Do this one thing right now. Are you starting to feel like the world is going insane? Time out, scope creep. I know you're awesome, but that right there is mission impossible. Let's look in the mirror and deal with the insane reflection looking right back at ya. What's that? You don't think you're insane? That's what they all say, but stay with me, I'm not saying you're actually "INSANE". It's your IT crowd approach to building solutions that keep taking longer than planned, keep costing more than budgetted, and keep falling short on the business value, benefits, and outcomes you promised.? Gartner continues to tow the failed data party line, reporting that nearly 80% of IT projects still continue to stumble across the finish line, failing to deliver the ROI that was promised and needed. Data continutes to be the number one issue that survey results point to, that is most to blame for cost overruns and underwhelming results. AI hasn't magically changed anything (go figure), with 30% of AI powered projects getting the rip cord pulled and being cancelled mid-stream due to data quality not being sufficient to support what is needed. Pure play Data Analytics and Data Science projects have an even worse record, with less than 13% delivering the analytical insights and value promised. Those are almost 10:1 odds going the wrong way. In 2020 I delivered a ThinkLinkers presentation that talked about why Data teams need to adopt the Covey mindset of always "Starting With The End In Mind", and making sure the Business Benefit, Outcome, and Value, however the business defines it, is clearly understood. ? While she was at SAP, Maria C Villar produced a series of YouTube videos on the topic of Data Strategy that allso stressed the critical importance of an Outside->In approach, that ?grounds everything that is discussed, designed, prioritized, and built going forward, on delivering that single quantifiable, measurable outcome. This is a core tenet of #DataFirst, whether it’s a Data Governance, Data Quality, or Data Migration project, or even just a single DataPipeline, DataSet, or Report being developed. There is zero value in anything you are doing unless you can tie a direct line to the business beneft being delivered, and the connective tissue between those two end points must cross over one or more business process lanes. This guarantees you are focused on the data that matters. It also ensures what you are delivering is built to last, because the data that is critical to success is not only supporting the business processes needed, it’s also kept "mean and clean" by those BAU business processes too. Einstein said the definition of insanty was doing the same thing and expecting different results. Isn't it time to do things different and change the conversation by doing #DataFirst? #DataMatters #PeopleMatterMost Syniti Capgemini