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?? Data Quality Crisis: 50% of Organizations Are Struggling to Get It Right The results from IELA's recent Enterprise Data Transformation market study are in, and they revealed a startling statistic - 50% of those organizations surveyed rated their organization as either “poor” or “fair” in data cleansing and quality management. AI-readiness depends on clean, reliable and actionable data. So, what gives? ?? Why Is Data Quality So Challenging? 1?? Fragmented Data Sources: Organizations are often operating with siloed systems that make it difficult to consolidate and clean data effectively; 2?? Legacy Systems: Outdated technologies struggle to support modern data governance and cleansing needs; 3?? Lack of Standards: Absent consistent protocols for data validation, inaccuracies, and inconsistencies are proliferating; and 4?? Resource Constraints: Data cleansing is proving to be labor-intensive, requiring both technical tools and skilled professionals (nowadays in short supply) ?? Why It’s Critical to Get Data Quality Right Poor data quality leads to: ? Flawed AI Models: "Dirty" data directly impacts the accuracy and reliability of AI systems, leading (quite possibly) to bad decisions; ? Operational Inefficiencies: Time and resources are wasted trying to clean data on-the-fly; ? Customer Dissatisfaction: Inaccurate data can erode trust and compromise customer relationships; and ?Missed Opportunities: Without reliable data, organizations struggle to spot trends, optimize operations, and seize opportunities ?? The Implications of Inaction Organizations that fail to prioritize data quality risk falling behind. AI-readiness, predictive analytics, and operational excellence are all built on a foundation of clean data. As AI adoption accelerates, companies that don’t address these issues NOW could very well find themselves unable to compete. ??? How to close the gap... 1?? Invest in Data Governance: Establish strong frameworks to ensure consistency and accountability; 2?? Leverage AI for Data Cleansing: Use AI tools to automate the detection and resolution of data inconsistencies; 3?? Create "Golden Records": Develop a single source of truth for key data points to eliminate duplication and errors; and 4?? Upskill Teams: Equip your workforce with the skills needed to manage and cleanse data effectively. ?? Want to Learn More? Dive into the full IELA Enterprise Data Transformation market study for actionable insights on modernizing your data infrastructure and improving data quality. ?? https://lnkd.in/gxFgT3gd #DataQuality #DataGovernance #DataAnalytics #DataCleansing #DataValidation #PredictiveAnalytics #OperationalExcellence

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?

Data Catalog First, Data Quality Second: Here’s Why Data quality and data intelligence platforms (data catalogs) are essential for organizations to become AI-ready. Data quality and observability have always been crucial for people to trust their data to make critical business decisions. Now, the stakes have been raised with the promise of AI to help drive the automation of business processes and decision-making. Data quality is the degree to which data meets a company’s accuracy, validity, completeness, and consistency expectations. Data quality tasks ensure data is fit for operational activities, analytics, and decision-making in a manner that increases trust. Data observability monitors high-level data aspects like freshness, volume changes, abnormal values (anomalies), structure changes, and quality. A data catalog is a metadata repository of information sources across the enterprise, including data sets, business intelligence reports, visualizations, and conversations. It facilitates a deep understanding of data origin, context, and lineage that helps business and technical users find and understand data more quickly. Data catalogs increasingly address a broad range of data intelligence solutions, including self-service analytics, data governance with privacy, and cloud modernization. Data catalogs provide employees with an understanding of where their data resides, how to communicate policy information, and how to use data appropriately. This way, data catalogs offer rich insights into various data quality characteristics. Data quality enables people in organizations to make decisions based on trusted data to improve the company's ability to thrive and be successful. While the impulse to address data quality first is understandable, implementing a data catalog should take precedence. A data catalog establishes the foundation for a more effective data quality initiative by fostering a thorough understanding and organization of data assets. From enhancing data literacy to ensuring quality for all business processes, including AI, a data catalog sets the stage for a culture of superior data quality. To learn more about how Alaiot can help you accelerate your successful data quality implementation. Click the link below:

If data is really the new oil, with enough intrinsic value to power the transformation and need for agility to allow organizations to evolve, then why is this resource neglected and left in a state of such low quality? Data governance and management is a messy undertaking. Starting is hard, particularly when it is still seen largely as a cost center and driven by IT (if at all). While I'm not overly fond of the analogy, it is still reasonably pervasive. Clive Humby (who coined the analogy) says... "Like oil, data is valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity. so, must data be broken down, analyzed for it to have value." Just as having a glossy front end UI won't provide a positive experience if the back-end is antiquated, a wonderfully designed BI dashboard won't provide useful insights if the source of truth is a swamp of data. Worse, it presents a real and present risk to an organization both in terms of security as well as fueling business hallucinations. AI and RAG based models tapping the data swamp is another accelerant to this risk. So if data programs (strategy/governance/management) are necessary, perhaps the first problem to tackle is 'where does it live?' and by association, who makes the investment and turns the cost center into a profit center? If no one wants to own it and sees the value in doing so, failure is a sure bet. The following article written by Robert S. Seiner (author of Non-Invasive Data Governance - NIDG) is a short, but thought prompting read that offers a proper comparative analysis of the pros and cons of where a DG program should reside in an organization. https://lnkd.in/gJMSAc3h

The Importance of Data Cleaning: Elevating Your Data Quality In the realm of data science, data is often referred to as the new oil. However, like crude oil, data needs refining to be truly valuable. This is where data cleaning, an essential step in the data analysis process, comes into play. ?? What is Data Cleaning? Data cleaning involves identifying and correcting (or removing) errors and inconsistencies in data to improve its quality. This process ensures that your data is accurate, complete, and reliable, which is crucial for making informed business decisions. ??Why is Data Cleaning Crucial? 1. Accuracy and Reliability Imagine trying to navigate using a GPS with outdated or incorrect maps. The chances of reaching your destination are slim. Similarly, making business decisions based on faulty data can lead to costly mistakes. Clean data ensures your analyses and insights are based on accurate information. 2. Enhanced Decision-Making Data cleaning enables organizations to make better decisions. Clean data provides a solid foundation for analytics, leading to insights that are not only reliable but also actionable. 3. Cost Efficiency Dealing with bad data can be expensive. According to IBM, the annual cost of poor-quality data in the U.S. alone is $3.1 trillion (according to reports from 2023). Investing time in data cleaning can save substantial costs associated with data errors, rework, and bad decisions. 4. Increased Productivity Clean data reduces the time spent on rectifying errors and allows teams to focus on more strategic tasks. This boosts overall productivity and efficiency within the organization. ??Interactive Tips for Effective Data Cleaning ??Regular Audits: Schedule regular data audits to identify and correct errors promptly. This keeps your data in top shape. ??Automated Tools: Leverage automated data cleaning tools to streamline the process. Tools like OpenRefine, Trifacta, and Talend can save time and enhance accuracy. ??Standardize Processes: Establish standard data entry and maintenance procedures to minimize inconsistencies. ??Training: Educate your team about the importance of data quality and best practices for data management. ??Real-Life Example: Boosting Business Intelligence Consider a retail company that struggled with inconsistent customer data, leading to flawed marketing strategies. After implementing a comprehensive data cleaning strategy, they saw a 30% increase in campaign effectiveness and a significant boost in customer engagement. Clean data empowered their marketing team to target the right customers with the right messages. Don't you investing in data cleaning is investing in your company’s future. Make sure your data is a true asset and not a liability. Tell us how has data cleaning transformed your data strategy? Share your experiences and tips in the comments below so we can grow together. #IIDST #DataScience #DataQuality #DataCleaning #TechEducation

Data transformations can be HARD - but with proper attention and planning, data transformation can be invigorating! Have you ever felt overwhelmed by the thought of reorganizing your data? Losing access to files or interrupting daily tasks can be intimidating, but fear often holds us back from unlocking automation and innovation. In my experience, small businesses often manage their information in a single stack—one drive of information (or a OneDrive of information!) with everything sorted into a dozen or so top-level folders. But when you dig deeper, each folder often expands into chaos: historical information mixed with current data, version duplicates upon duplicates, and data bloat galore. This setup hinders efficiency and complicates data security and management practices. Think of it like cleaning out a utility closet full of old project parts and tools. To make progress, you have to unpack the contents and separate everything into manageable piles. Without proper containers—boxes, bins, or folders—you can’t sort the items in a meaningful way. Without knowing what the ‘junk’ is, you won’t know how many containers, or what sizes, are needed. Without a plan, your clean-out can dissolve into random piles with no function or order. Like loose screws in the bottom of a tool bag, the chaos shall spear hands searching in the dark. To avoid the pitfalls of the data chaos, a few steps should be taken to prepare for the transformation: - Audit the data: Understand the entire scope of what the data is, what it does, and who owns it. - Pre-clean the data: Do you really need every version, every file, or every year’s worth of data? Likely not. Archive, reduce, and rename files and folders for clarity. - Identify or develop attributes: Like labels on boxes, attributes are crucial for organization. Define these for your data. - Map it: Map it in detail. Draw a physical picture of what your data looks like to understand how its structured, flows, and demonstrate the boundaries or even security boundaries. Remember, the details of transformation depend on the destination, and the quality depends on the effort you put into preparation and consideration of potential. Take your data and morph it into something that improves workflows and data handling. Analyze current processes for opportunities, and build transformative features into your baseline data. Dream big. Develop innovation. Be brave and transform! What’s your biggest challenge when it comes to data transformation? I’d love to hear your thoughts!

An easy framework to identify and measure your data culture efforts With the rise of GenAI, the focus on data and its cultural adoption will become increasingly important. But "data culture" is a broad and often fuzzy term, making it difficult to capture and visualize. Every company inherently has a data culture because data is continuously created and used. The real question isn't whether we have a data culture, but how well data is generated, valued, interpreted, and used. Data culture can be seen as a subset of organizational culture. It consists of shared values, behaviors, and norms within an organization specifically concerning data. These values, behaviors, and norms are shaped by soft and hard factors within an organization. One effective framework is the McKinsey 7S Framework. This model differentiates between soft and hard factors, showing that culture is influenced not only by values and attitudes but also by the design of organizational structures. These soft factors consider the SKILLS of the workforce, the STYLE of the leadership approach, the STAFF, and the SHARED VALUES. On the other hand, hard factors concentrate on STRATEGY and its execution, the STRUCTURE of the organization, and SYSTEMS to create the value chain of the company. With this in mind, we shouldn't overcomplicate data culture. Data culture is part of the company culture, so we can also adopt existing cultural models to shape our understanding of data culture. Let's translate and map McKinsey's 7S Framework to Data Culture: → Data Strategy: defines the long-term strategic direction of the data value chain to improve the defensive and offensive capabilities in the data journey. → Data Governance: describes the necessary structures (e.g., processes, policies, tools) and roles that are required for the successful handling of data in the company. → Data Collaboration: seen as an ongoing process that broadens access to data and empowers collaboration between employees to find, access, self-analyze, and share data without additional support. → Data Leadership: describes the approach of leaders in an organization in fostering a data culture, guiding and motivating the staff to create and utilize data for and in strategic and operational decisions. → Data Literacy: describes the capability how to read, interpret, use, and communicate data effectively and the measures to improve data-related competencies. → Data Empowerment: ensures that incentive structures, clear communication channels, required tools, and authorities are defined and implemented. → Data Principles: are core values that determine an organization's approach to data governance, data management, data usage, and data exchange. We can use this framework to measure the current organizational understanding of data culture. By defining a questionnaire for each key pillar we can evaluate and map the results into a heatmap, that gives us clarity on how to improve our cultural efforts.

??????????????????? ?? ????????-???????????? ??????????????: ???????????? ???????????????????? ?????? ???????????????? ?? Creating a data-driven culture is a transformative journey, but it comes with its own set of challenges. Based on my first-hand experience, here are some common issues data teams face and how to overcome them: ?? ???????????? ???????????????????? 1. Data Silos: One of the biggest hurdles is dealing with data silos. Different departments often store data in isolated systems, making it hard to get a unified view. Breaking down these silos requires a concerted effort to integrate data across the organisation. 2. Lack of Data Literacy: Not everyone is comfortable with data. I've seen teams struggle because they lack the skills to interpret and use data effectively. Investing in training and promoting data literacy is essential to empower everyone to make data-driven decisions. 3. Resistance to Change: Cultural shifts are never easy. There’s often resistance from teams accustomed to traditional decision-making methods. It’s crucial to communicate the benefits of a data-driven approach clearly and involve key stakeholders early in the process. 4. Data Quality Issues: Inconsistent and inaccurate data can undermine trust in data-driven insights. Ensuring high data quality through proper governance and validation processes is vital for reliable decision-making. 5. Tool Overload: With so many data tools available, it’s easy to fall into the trap of adopting too many, leading to confusion and inefficiency. Selecting the right tools that align with your needs and ensuring proper training can help avoid this pitfall. ?? ???????????????????? ?????? ???????????????????? 1. Foster Collaboration: Encourage departments to share data and insights. Creating cross-functional teams can help bridge gaps and foster a more integrated approach to data. 2. Promote Data Literacy: Regular training sessions and workshops can help boost data literacy across the organization. Make sure everyone understands how to access, interpret, and use data effectively. 3. Lead by Example: Leaders need to champion the use of data in decision-making. By demonstrating the value of data-driven decisions, leaders can inspire their teams to follow suit. 4. Ensure Data Quality: Implement robust data governance practices to maintain data quality. Regular audits and validation processes can help ensure that the data used for decision-making is accurate and reliable. 5. Streamline Tools and Processes: ?Choose tools that integrate well with your existing systems and meet your specific needs. Providing comprehensive training on these tools can help teams use them more effectively. Building a data-driven culture is a continuous process that requires dedication and effort. By addressing these common challenges head-on, we can create an environment where data is a trusted and valuable asset for everyone. #DataDrivenCulture #TechLeadership #DataLiteracy #DataGovernance

?? ???????? ?????????????? ????. ???????? ?????????????????????????? ???? ???????? ??????????: ????????'?? ?????? ????????????????????? ?? Ensuring trust in your data is non-negotiable and if you don't trust your data you can't be a Data Driven organization. These concepts often come up in conversations—Data Quality, Data Trust?and Data Observability. While they may sound similar, they play distinct roles in modern data management. Let’s break it down: ???????? ?????????????? ?? At its core, data quality refers to the accuracy, completeness, consistency, and reliability of data. It’s about ???????????? ???????? ?????? ???????? ???? ?????? ?????? ??????????????—clean, organized, and error-free. Basically this is an exercise of identifying, managing critical data attributes and ?????????? ???????????? through ???????? ?????????? ??????????????????????. IMO this is not scalable. ???????? ?????????????????????????? ?? Data observability, on the other hand, takes a broader, more automated approach. Instead of focusing on a few data attributes or some known issues, it monitors all data objects and attributes across your entire pipeline. Observability is more about learning from the data automatically and detecting anomalies and unusual patterns automatically and also provide an end to end?visibility across pipelines and systems. IMO Data Observability is what modern organization need. Most data issues don’t appear as clear-cut errors and often go unnoticed until they cause a failure downstream. This is where data observability shines, detecting anomalies in real-time, long before they lead to costly problems. Some examples data issues a good Data observability platform should?flag and alert. -- Date field that typically gets the current date has future/past dated transactions -- Decimal field that typically gets values between $1 and $100 starts getting values in Millions -- Varchar field that normally gets numeric values starts getting Text values -- Varchar field that was null for the past x number of runs starts getting values -- Varchar field called description is storing sensitive information like SSN, Credit Card, Bank Acct Info etc ???? ?????????????? ?? Data Quality = Tackling known issues through specific rules to ensure key data is accurate. Data Observability = Broad, automated monitoring that detects anomalies upstream in real-time, preventing issues from affecting downstream processes and data consumers. By combining both, organizations can ensure clean, reliable data while maintaining a healthy, operational pipeline, empowering better data-driven decisions! ?? #DataQuality #DataObservability #DataOps #DataManagement #Analytics #DataDriven #DataContracts

?? Data Strategy: Why it Matters and How to Build One ?? As the Director of Innovation, I'm constantly exploring ways to help thought leaders and C-level executives navigate the ever-evolving business landscape. Today, I want to share an insightful article titled "Data Strategy: Why it Matters and How to Build One" by Josh Howard and Amit Kara. ?? In today's data-driven world, organizations must ensure that their data management practices align with their business strategies. That's where a data strategy comes into play. It's a comprehensive plan that outlines how an organization collects, manages, governs, utilizes, and derives value from its data. ??? Having a data strategy is crucial as it enables organizations to make data-driven decisions, improve agility, and collaborate effectively. It also ensures data governance, privacy, and organizational control. A solid data strategy facilitates adoption, helps plan for changes, and drives growth. ?? The benefits of having a data strategy are immense. It empowers informed decision-making, increases efficiency and cost savings, fosters a data-focused culture, reduces risk, and enhances the customer experience. Moreover, a data strategy plays a crucial role in achieving analytical and AI maturity, enabling organizations to extract insights and innovate. ?? Building a data strategy involves putting together a team, defining objectives, evaluating the current situation, creating a roadmap, establishing clear policies, investing in new technology, educating and building a data-first culture, and monitoring and reassessing regularly. It's a journey that requires commitment and collaboration. ?? However, implementing a data strategy comes with its own set of challenges, such as limited data literacy and the need for buy-in from the organization. It's important to choose the right type of data strategy that balances reliability, security, compliance, and innovation. ?? The article also includes a fascinating case study of Thomas, a global talent assessment provider, that transformed its consultancy operation using a data strategy. This case study highlights the benefits of data democratization and the use of a data lake as a single source of truth. ?? In conclusion, having a data strategy is essential in today's data-driven business environment. It empowers organizations to unlock the power of data, make informed decisions, and drive growth and innovation. So, let's embrace the data revolution and build a future where data strategy is at the heart of every successful organization! ?? #DataStrategy #DataDrivenDecisions #Innovation #BusinessGrowth