Empathy

I am in love with the human spirit. Despite the frustrations humans give at times, I feel the connection we all have.

I feel the passion of the teacher who although isn't getting paid their true worth, show up every day and love on their students.

The blue collar man who shows up faithfully to provide for his family.

The wealthy man whose heart is filled with charity and wanting to make a difference.

The athlete who is brimming with confidence and driven by sheer will to win.

I understand why God loves us so much.

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Somewhere in a data warehouse, a customer record sits incomplete. A postcode field contains only the first half of its expected value. An email address lacks its domain. A timestamp references a date that never existed. These fragments of broken data might seem trivial in isolation, but multiply them across millions of records and the consequences become staggering. According to Gartner research, poor data quality costs organisations an average of $12.9 million annually, whilst MIT Sloan Management Review research with Cork University Business School found that companies lose 15 to 25 percent of revenue each year due to data quality failures.

The challenge facing modern enterprises is not merely detecting these imperfections but deciding what to do about them. Should a machine learning algorithm guess at the missing values? Should a rule-based system fill gaps using statistical averages? Or should a human being review each problematic record individually? The answer, as it turns out, depends entirely on what you are trying to protect and what you can afford to lose.

The Anatomy of Broken Content

Before examining solutions, it is worth understanding what breaks and why. Content can fail in countless ways: fields left empty during data entry, format inconsistencies introduced during system migrations, encoding errors from international character sets, truncation from legacy database constraints, and corruption from network transmission failures. Each failure mode demands a different repair strategy.

The taxonomy of data quality dimensions provides a useful framework. Researchers have identified core metrics including accuracy, completeness, consistency, timeliness, validity, availability, and uniqueness. A missing value represents a completeness failure. A postcode that does not match its corresponding city represents a consistency failure. A price expressed in pounds where euros were expected represents a validity failure. Each dimension requires different detection logic and repair approaches.

The scale of these problems is often underestimated. A systematic survey of software tools dedicated to data quality identified 667 distinct platforms, reflecting the enormity of the challenge organisations face. Traditional approaches relied on manually generated criteria to identify issues, a process that was both time-consuming and resource-intensive. Newer systems leverage machine learning to automate rule creation and error identification, producing more consistent and accurate outputs.

Modern data quality tools have evolved to address these varied failure modes systematically. Platforms such as Great Expectations, Monte Carlo, Anomalo, and dbt have emerged as industry standards for automated detection. Great Expectations, an open-source Python library, allows teams to define validation rules and run them continuously across data pipelines. The platform supports schema validation to ensure data conforms to specified structures, value range validation to confirm data falls within expected bounds, and row count validation to verify record completeness. This declarative approach to data quality has gained significant traction, with the tool now integrating seamlessly with Apache Airflow, Apache Spark, dbt, and cloud platforms including Snowflake and BigQuery.

Monte Carlo has taken a different approach, pioneering what the industry calls data observability. The platform uses unsupervised machine learning to detect anomalies across structured, semi-structured, and unstructured data without requiring manual configuration. According to Gartner estimates, by 2026, 50 percent of enterprises implementing distributed data architectures will adopt data observability tools, up from less than 20 percent in 2024. This projection reflects a fundamental shift in how organisations think about data quality: from reactive firefighting to proactive monitoring. The company, having raised $200 million in Series E funding at a $3.5 billion valuation, counts organisations including JetBlue and Nasdaq among its enterprise customers.

The Three Pillars of Automated Repair

Once malformed content is detected, organisations face a crucial decision: how should it be repaired? Three distinct approaches have emerged, each with different risk profiles, resource requirements, and accuracy characteristics.

Heuristic Imputation: The Statistical Foundation

The oldest and most straightforward approach to data repair relies on statistical heuristics. When a value is missing, replace it with the mean, median, or mode of similar records. When a format is inconsistent, apply a transformation rule. When a constraint is violated, substitute a default value. These methods are computationally cheap, easy to understand, and broadly applicable.

Mean imputation, for instance, calculates the average of all observed values for a given field and uses that figure to fill gaps. If customer ages range from 18 to 65 with an average of 42, every missing age field receives the value 42. This approach maintains the overall mean of the dataset but introduces artificial clustering around that central value, distorting the true distribution of the data. Analysts working with mean-imputed data may draw incorrect conclusions about population variance and make flawed predictions as a result.

Regression imputation offers a more sophisticated alternative. Rather than using a single value, regression models predict missing values based on relationships with other variables. A missing salary figure might be estimated from job title, years of experience, and geographic location. This preserves some of the natural variation in the data but assumes linear relationships that may not hold in practice. When non-linear relationships exist between variables, linear regression-based imputation performs poorly, creating systematic errors that propagate through subsequent analyses.

Donor-based imputation, used extensively by statistical agencies including Statistics Canada, the U.S. Bureau of Labor Statistics, and the U.S. Census Bureau, takes values from similar observed records and applies them to incomplete ones. For each recipient with a missing value, a donor is identified based on similarity across background characteristics. This approach preserves distributional properties more effectively than mean imputation but requires careful matching criteria to avoid introducing bias.

The fundamental limitation of all heuristic methods is their reliance on assumptions. Mean imputation assumes values cluster around a central tendency. Regression imputation assumes predictable relationships between variables. Donor imputation assumes that similar records should have similar values. When these assumptions fail, the repairs introduce systematic errors that compound through downstream analyses.

Machine Learning Inference: The Algorithmic Frontier

Machine learning approaches to data repair represent a significant evolution from statistical heuristics. Rather than applying fixed rules, ML algorithms learn patterns from the data itself and use those patterns to generate contextually appropriate repairs.

K-nearest neighbours (KNN) imputation exemplifies this approach. The algorithm identifies records most similar to the incomplete one across multiple dimensions, then uses values from those neighbours to fill gaps. Research published in BMC Medical Informatics found that KNN algorithms demonstrated the overall best performance as assessed by mean squared error, with results independent from the mechanism of randomness and applicable to both Missing at Random (MAR) and Missing Completely at Random (MCAR) data. Due to its simplicity, comprehensibility, and relatively high accuracy, the KNN approach has been successfully deployed in real data processing applications at major statistical agencies.

However, the research revealed an important trade-off. While KNN with higher k values (more neighbours) reduced imputation errors, it also distorted the underlying data structure. The use of three neighbours in conjunction with feature selection appeared to provide the best balance between imputation accuracy and preservation of data relationships. This finding underscores a critical principle: repair methods must be evaluated not only on how accurately they fill gaps but on how well they preserve the analytical value of the dataset. Research on longitudinal prenatal data found that using five nearest neighbours with appropriate temporal segmentation provided imputed values with the least error, with no difference between actual and predicted values for 64 percent of deleted segments.

MissForest, an iterative imputation method based on random forests, has emerged as a particularly powerful technique for complex datasets. By averaging predictions across many decision trees, the algorithm handles mixed data types and captures non-linear relationships that defeat simpler methods. Original evaluations showed missForest reducing imputation error by more than 50 percent compared to competing approaches, particularly in datasets with complex interactions. The algorithm uses built-in out-of-bag error estimates to assess imputation accuracy without requiring separate test sets, enabling continuous quality monitoring during the imputation process.

Yet missForest is not without limitations. Research published in BMC Medical Research Methodology found that while the algorithm achieved high predictive accuracy for individual missing values, it could produce severely biased regression coefficient estimates when imputed variables were used in subsequent statistical analyses. The algorithm's tendency to predict toward variable means introduced systematic distortions that accumulated through downstream modelling. This finding led researchers to conclude that random forest-based imputation should not be indiscriminately used as a universal solution; correct analysis requires careful assessment of the missing data mechanism and the interrelationships between variables.

Multiple Imputation by Chained Equations (MICE), sometimes called fully conditional specification, represents another sophisticated ML-based approach. Rather than generating a single imputed dataset, MICE creates multiple versions, each with different plausible values for missing entries. This technique accounts for statistical uncertainty in the imputations and has emerged as a standard method in statistical research. The MICE algorithm, first appearing in 2000 as an S-PLUS library and subsequently as an R package in 2001, can impute mixes of continuous, binary, unordered categorical, and ordered categorical data whilst maintaining consistency through passive imputation. The approach preserves variable distributions and relationships between variables more effectively than univariate imputation methods, though it requires significant computational resources and expertise to implement correctly. Generally, ten cycles are performed during imputation, though research continues on identifying optimal iteration counts under different conditions.

The general consensus from comparative research is that ML-based methods preserve data distribution better than simple imputations, whilst hybrid techniques combining multiple approaches yield the most robust results. Optimisation-based imputation methods have demonstrated average reductions in mean absolute error of 8.3 percent against the best cross-validated benchmark methods across diverse datasets. Studies have shown that the choice of imputation method directly influences how machine learning models interpret and rank features; proper feature importance analysis ensures models rely on meaningful predictors rather than artefacts of data preprocessing.

Human Review: The Accuracy Anchor

Despite advances in automation, human review remains essential for certain categories of data repair. The reason is straightforward: humans can detect subtle, realistic-sounding failure cases that automated systems routinely miss. A machine learning model might confidently predict a plausible but incorrect value. A human reviewer can recognise contextual signals that indicate the prediction is wrong. Humans can distinguish between technically correct responses and actually helpful responses, a distinction that proves critical when measuring user satisfaction, retention, or trust.

Field studies have demonstrated that human-in-the-loop approaches can maintain accuracy levels of 87 percent whilst reducing annotation costs by 62 percent and time requirements by a factor of three. The key is strategic allocation of human effort. Automated systems handle routine cases whilst human experts focus on ambiguous, complex, or high-stakes situations. One effective approach combines multiple prompts or multiple language models and calculates the entropy of predictions to determine whether automated annotation is reliable enough or requires human review.

Research on automated program repair in software engineering has illuminated the trust dynamics at play. Studies found that whether code repairs were produced by humans or automated systems significantly influenced trust perceptions and intentions. The research also discovered that test suite provenance, whether tests were written by humans or automatically generated, had a significant effect on patch quality, with developer-written tests typically producing higher-quality repairs. This finding extends to data repair: organisations may be more comfortable deploying automated repairs for low-risk fields whilst insisting on human review for critical business data.

Combined human-machine systems have demonstrated superior performance in domains where errors carry serious consequences. Medical research has shown that collaborative approaches outperform both human-only and ML-only systems in tasks such as identifying breast cancer from medical imaging. The principle translates directly to data quality: neither humans nor machines should work alone.

The optimal hybrid approach involves iterative annotation. Human annotators initially label a subset of problematic records, the automated system learns from these corrections and makes predictions on new records, human annotators review and correct errors, and the cycle repeats. Uncertainty sampling focuses human attention on cases where the automated system has low confidence, maximising the value of human expertise whilst minimising tedious review of straightforward cases. This approach allows organisations to manage costs while maintaining efficiency by strategically allocating human involvement.

Matching Methods to Risk Profiles

The choice between heuristic, ML-based, and human-mediated repair depends critically on the risk profile of the data being repaired. Three factors dominate the decision.

Consequence of Errors: What happens if a repair is wrong? For marketing analytics, an incorrectly imputed customer preference might result in a slightly suboptimal campaign. For financial reporting, an incorrectly imputed transaction amount could trigger regulatory violations. For medical research, an incorrectly imputed lab value could lead to dangerous treatment decisions. The higher the stakes, the stronger the case for human review.

Volume and Velocity: How much data requires repair, and how quickly must it be processed? Human review scales poorly. A team of analysts might handle hundreds of records per day; automated systems can process millions. Real-time pipelines using technologies such as Apache Kafka and Apache Spark Streaming demand automated approaches simply because human review cannot keep pace. These architectures handle millions of messages per second with built-in fault tolerance and horizontal scalability.

Structural Complexity: How complicated are the relationships between variables? Simple datasets with independent fields can be repaired effectively using basic heuristics. Complex datasets with intricate interdependencies between variables require sophisticated ML approaches that can model those relationships. Research consistently shows that missForest and similar algorithms excel when complex interactions and non-linear relations are present.

A practical framework emerges from these considerations. Low-risk, high-volume data with simple structure benefits from heuristic imputation: fast, cheap, good enough. Medium-risk data with moderate complexity warrants ML-based approaches: better accuracy, acceptable computational cost. High-risk data, regardless of volume or complexity, requires human review: slower and more expensive, but essential for protecting critical business processes.

Enterprise Toolchains in Practice

The theoretical frameworks for data repair translate into concrete toolchains that enterprises deploy across their data infrastructure. Understanding these implementations reveals how organisations balance competing demands for speed, accuracy, and cost.

Detection Layer: Modern toolchains begin with continuous monitoring. Great Expectations provides declarative validation rules that run against data as it flows through pipelines. Teams define expectations such as column values should be unique, values should fall within specified ranges, or row counts should match expected totals. The platform generates validation reports and can halt pipeline execution when critical checks fail. Data profiling capabilities generate detailed summaries including statistical measures, distributions, and patterns that can be compared over time to identify changes indicating potential issues.

dbt (data build tool) has emerged as a complementary technology, with over 60,000 teams worldwide relying on it for data transformation and testing. The platform includes built-in tests for common quality checks: unique values, non-null constraints, accepted value ranges, and referential integrity between tables. About 40 percent of dbt projects run tests each week, reflecting the integration of quality checking into routine data operations. The tool has been recognised as both Snowflake Data Cloud Partner of the Year and Databricks Customer Impact Partner of the Year, reflecting its growing enterprise importance.

Monte Carlo and Anomalo represent the observability layer, using machine learning to detect anomalies that rule-based systems miss. These platforms monitor for distribution drift, schema changes, volume anomalies, and freshness violations. When anomalies are detected, automated alerts trigger investigation workflows. Executive-level dashboards present key metrics including incident frequency, mean time to resolution, platform adoption rates, and overall system uptime with automated updates.

Repair Layer: Once issues are detected, repair workflows engage. ETL platforms such as Oracle Data Integrator and Talend provide error handling within transformation layers. Invalid records can be redirected to quarantine areas for later analysis, ensuring problematic data does not contaminate target systems whilst maintaining complete data lineage. When completeness failures occur, graduated responses match severity to business impact: minor gaps generate warnings for investigation, whilst critical missing data that would corrupt financial reporting halts pipeline processing entirely.

AI-powered platforms have begun automating repair decisions. These systems detect and correct incomplete, inconsistent, and incorrect records in real time, reducing manual effort by up to 50 percent according to vendor estimates. The most sophisticated implementations combine rule-based repairs for well-understood issues with ML-based imputation for complex cases and human escalation for high-risk or ambiguous situations.

Orchestration Layer: Apache Airflow, Prefect, and similar workflow orchestration tools coordinate the components. A typical pipeline might ingest data from source systems, run validation checks, route records to appropriate repair workflows based on error types and risk levels, apply automated corrections where confidence is high, queue uncertain cases for human review, and deliver cleansed data to target systems.

Schema registries, particularly in Kafka-based architectures, enforce data contracts at the infrastructure level. Features include schema compatibility checking, versioning support, and safe evolution of data structures over time. This proactive approach prevents many quality issues before they occur, ensuring data compatibility across distributed systems.

Measuring Business Impact

Deploying sophisticated toolchains is only valuable if organisations can demonstrate meaningful business outcomes. The measurement challenge is substantial: unlike traditional IT projects with clear cost-benefit calculations, data quality initiatives produce diffuse benefits that are difficult to attribute. Research has highlighted organisational and managerial challenges in realising value from analytics, including cultural resistance, poor data quality, and the absence of clear goals.

Discovery Improvements

One of the most tangible benefits of improved data quality is enhanced data discovery. When data is complete, consistent, and well-documented, analysts can find relevant datasets more quickly and trust what they find. Organisations implementing data governance programmes have reported researchers locating relevant datasets 60 percent faster, with report errors reduced by 35 percent and exploratory analysis time cut by 45 percent.

Data discoverability metrics assess how easily users can find specific datasets within data platforms. Poor discoverability, such as a user struggling to locate sales data for a particular region, indicates underlying quality and metadata problems. Improvements in these metrics directly translate to productivity gains as analysts spend less time searching and more time analysing.

The measurement framework should track throughput (how quickly users find data) and quality (accuracy and completeness of search results). Time metrics focus on the speed of accessing data and deriving insights. Relevancy metrics evaluate whether data is fit for its intended purpose. Additional metrics include the number of data sources identified, the percentage of sensitive data classified, the frequency and accuracy of discovery scans, and the time taken to remediate privacy issues.

Analytics Fidelity

Poor data quality undermines the reliability of analytical outputs. When models are trained on incomplete or inconsistent data, their predictions become unreliable. When dashboards display metrics derived from flawed inputs, business decisions suffer. Gartner reports that only nine percent of organisations rate themselves at the highest analytics maturity level, with 87 percent demonstrating low business intelligence maturity.

Research from BARC found that more than 40 percent of companies do not trust the outputs of their AI and ML models, whilst more than 45 percent cite data quality as the top obstacle to AI success. These statistics highlight the direct connection between data quality and analytical value. Global spending on big data analytics is projected to reach $230.6 billion by 2025, with spending on analytics, AI, and big data platforms expected to surpass $300 billion by 2030. This investment amplifies the importance of ensuring that underlying data quality supports reliable outcomes.

Measuring analytics fidelity requires tracking model performance over time. Are prediction errors increasing? Are dashboard metrics drifting unexpectedly? Are analytical conclusions being contradicted by operational reality? These signals indicate data quality degradation that toolchains should detect and repair.

Data observability platforms provide executive-level dashboards presenting key metrics including incident frequency, mean time to resolution, platform adoption rates, and overall system uptime. These operational metrics enable continuous improvement by letting organisations track trends over time, spot degradation early, and measure the impact of improvements.

Return on Investment

The financial case for data quality investment is compelling but requires careful construction. Gartner research indicates poor data quality costs organisations an average of $12.9 to $15 million annually. IBM research published in Harvard Business Review estimated poor data quality cost the U.S. economy $3.1 trillion per year. McKinsey Global Institute found that poor-quality data leads to 20 percent decreases in productivity and 30 percent increases in costs. Additionally, 20 to 30 percent of enterprise revenue is lost due to data inefficiencies.

Against these costs, the returns from data quality toolchains can be substantial. Data observability implementations have demonstrated ROI percentages ranging from 25 to 87.5 percent. Cost savings for addressing issues such as duplicate new user orders and improving fraud detection can reach $100,000 per issue annually, with potential savings from enhancing analytics dashboard accuracy reaching $150,000 per year.

One organisation documented over $2.3 million in cost savings and productivity improvements directly attributable to their governance initiative within six months. Companies with mature data governance and quality programmes experience 45 percent lower data breach costs, according to IBM's Cost of a Data Breach Report, which found average breach costs reached $4.88 million in 2024.

The ROI calculation should incorporate several components. Direct savings from reduced error correction effort (data teams spend 50 percent of their time on remediation according to Ataccama research) represent the most visible benefit. Revenue protection from improved decision-making addresses the 15 to 25 percent revenue loss that MIT research associates with poor quality. Risk reduction from fewer compliance violations and security breaches provides insurance value. Opportunity realisation from enabled analytics and AI initiatives captures upside potential. Companies with data governance programmes report 15 to 20 percent higher operational efficiency according to McKinsey research.

A holistic ROI formula considers value created, impact of quality issues, and total investment. Data downtime, when data is unavailable or inaccurate, directly impacts initiative value. Including downtime in ROI calculations reveals hidden costs and encourages investment in quality improvement.

The Emerging Landscape

Several trends are reshaping how organisations approach content repair and quality measurement.

AI-Native Quality Tools: The integration of artificial intelligence into data quality platforms is accelerating. Unsupervised machine learning detects anomalies without manual configuration. Natural language interfaces allow business users to query data quality without technical expertise. Generative AI is beginning to suggest repair strategies and explain anomalies in business terms. The Stack Overflow 2024 Developer Survey shows 76 percent of developers using or planning to use AI tools in their workflows, including data engineering tasks.

According to Gartner, by 2028, 33 percent of enterprise applications will include agentic AI, up from less than 1 percent in 2024. This shift will transform data quality from a technical discipline into an embedded capability of data infrastructure.

Proactive Quality Engineering: Great Expectations represents an advanced approach to quality management, moving governance from reactive, post-error correction to proactive systems of assertions, continuous validation, and instant feedback. The practice of analytics engineering, as articulated by dbt Labs, believes data quality testing should be integrated throughout the transformation process, not bolted on at the end.

This philosophy is gaining traction. Data teams increasingly test raw data upon warehouse arrival, validate transformations as business logic is applied, and verify quality before production deployment. Quality becomes a continuous concern rather than a periodic audit.

Consolidated Platforms: The market is consolidating around integrated platforms. The announced merger between dbt Labs and Fivetran signals a trend toward end-to-end solutions that handle extraction, transformation, and quality assurance within unified environments. IBM has been recognised as a Leader in Gartner Magic Quadrants for Augmented Data Quality Solutions, Data Integration Tools, and Data and Analytics Governance Platforms for 17 consecutive years, reflecting the value of comprehensive capabilities.

Trust as Competitive Advantage: Consumer trust research shows 75 percent of consumers would not purchase from organisations they do not trust with their data, according to Cisco's 2024 Data Privacy Benchmark Study. This finding elevates data quality from an operational concern to a strategic imperative. Organisations that demonstrate data stewardship through quality and governance programmes build trust that translates to market advantage.

The Human Element

Despite technological sophistication, the human element remains central to effective data repair. Competitive advantage increasingly depends on data quality rather than raw computational power. Organisations with superior training data and more effective human feedback loops will build more capable AI systems than competitors relying solely on automated approaches.

The most successful implementations strategically allocate human involvement, using AI to handle routine cases whilst preserving human input for complex, ambiguous, or high-stakes situations. Uncertainty sampling allows automated systems to identify cases where they lack confidence, prioritising these for human review and focusing expert attention where it adds most value.

Building effective human review processes requires attention to workflow design, expertise cultivation, and feedback mechanisms. Reviewers need context about why records were flagged, access to source systems for investigation, and clear criteria for making repair decisions. Their corrections should feed back into automated systems, continuously improving algorithmic performance.

Strategic Implementation Guidance

The question of how to handle incomplete or malformed content has no universal answer. Heuristic imputation offers speed and simplicity but introduces systematic distortions. Machine learning inference provides contextual accuracy but requires computational resources and careful validation. Human review delivers reliability but cannot scale. The optimal strategy combines all three, matched to the risk profile and operational requirements of each data domain.

Measurement remains challenging but essential. Discovery improvements, analytics fidelity, and financial returns provide the metrics needed to justify investment and guide continuous improvement. Organisations that treat data quality as a strategic capability rather than a technical chore will increasingly outcompete those that do not. Higher-quality data reduces rework, improves decision-making, and protects investment by tying outcomes to reliable information.

The toolchains are maturing rapidly. From validation frameworks to observability platforms to AI-powered repair engines, enterprises now have access to sophisticated capabilities that were unavailable five years ago. The organisations that deploy these tools effectively, with clear strategies for matching repair methods to risk profiles and robust frameworks for measuring business impact, will extract maximum value from their data assets.

In a world where artificial intelligence is transforming every industry, data quality determines AI quality. The patterns and toolchains for detecting and repairing content are not merely operational necessities but strategic differentiators. Getting them right is no longer optional.

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References and Sources

1. Gartner. “Data Quality: Why It Matters and How to Achieve It.” Gartner Research. https://www.gartner.com/en/data-analytics/topics/data-quality

2. MIT Sloan Management Review with Cork University Business School. Research on revenue loss from poor data quality.

3. Great Expectations. “Have Confidence in Your Data, No Matter What.” https://greatexpectations.io/

4. Monte Carlo. “Data + AI Observability Platform.” https://www.montecarlodata.com/

5. Atlan. “Automated Data Quality: Fix Bad Data & Get AI-Ready in 2025.” https://atlan.com/automated-data-quality/

6. Nature Communications Medicine. “The Impact of Imputation Quality on Machine Learning Classifiers for Datasets with Missing Values.” https://www.nature.com/articles/s43856-023-00356-z

7. BMC Medical Informatics and Decision Making. “Nearest Neighbor Imputation Algorithms: A Critical Evaluation.” https://link.springer.com/article/10.1186/s12911-016-0318-z

8. Oxford Academic Bioinformatics. “MissForest: Non-parametric Missing Value Imputation for Mixed-type Data.” https://academic.oup.com/bioinformatics/article/28/1/112/219101

9. BMC Medical Research Methodology. “Accuracy of Random-forest-based Imputation of Missing Data in the Presence of Non-normality, Non-linearity, and Interaction.” https://link.springer.com/article/10.1186/s12874-020-01080-1

10. PMC. “Multiple Imputation by Chained Equations: What Is It and How Does It Work?” https://pmc.ncbi.nlm.nih.gov/articles/PMC3074241/

11. Appen. “Human-in-the-Loop Improves AI Data Quality.” https://www.appen.com/blog/human-in-the-loop-approach-ai-data-quality

12. dbt Labs. “Deliver Trusted Data with dbt.” https://www.getdbt.com/

13. Integrate.io. “Data Quality Improvement Stats from ETL: 50+ Key Facts Every Data Leader Should Know in 2025.” https://www.integrate.io/blog/data-quality-improvement-stats-from-etl/

14. IBM. “IBM Named a Leader in the 2024 Gartner Magic Quadrant for Augmented Data Quality Solutions.” https://www.ibm.com/blog/announcement/gartner-magic-quadrant-data-quality/

15. Alation. “Data Quality Metrics: How to Measure Data Accurately.” https://www.alation.com/blog/data-quality-metrics/

16. Sifflet Data. “Considering the ROI of Data Observability Initiatives.” https://www.siffletdata.com/blog/considering-the-roi-of-data-observability-initiatives

17. Data Meaning. “The ROI of Data Governance: Measuring the Impact on Analytics.” https://datameaning.com/2025/04/07/the-roi-of-data-governance-measuring-the-impact-on-analytics/

18. BARC. “Observability for AI Innovation Study.” Research on AI/ML model trust and data quality obstacles.

19. Cisco. “2024 Data Privacy Benchmark Study.” Research on consumer trust and data handling.

20. IBM. “Cost of a Data Breach Report 2024.” Research on breach costs and governance programme impact.

21. AWS. “Real-time Stream Processing Using Apache Spark Streaming and Apache Kafka on AWS.” https://aws.amazon.com/blogs/big-data/real-time-stream-processing-using-apache-spark-streaming-and-apache-kafka-on-aws/

22. Journal of Applied Statistics. “A Novel Ranked K-nearest Neighbors Algorithm for Missing Data Imputation.” https://www.tandfonline.com/doi/full/10.1080/02664763.2024.2414357

23. Contrary Research. “Monte Carlo Company Profile.” https://research.contrary.com/company/monte-carlo

24. PMC. “A Survey of Data Quality Measurement and Monitoring Tools.” https://pmc.ncbi.nlm.nih.gov/articles/PMC9009315/

25. ResearchGate. “High-Quality Automated Program Repair.” Research on trust perceptions in automated vs human code repair.

26. Stack Overflow. “2024 Developer Survey.” Research on AI tool adoption in development workflows.

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

In Summary: * Happy Birthday to me. Today I am 77 years old. And I've had a good day. Thanks to all who sent me Happy Birthday wishes. It's been a quiet Saturday in the Roscoe-verse. I followed two Big Ten Conference Basketball Games this afternoon: Indiana losing to Iowa, and Purdue beating USC. Last night's sleep was a short one, so I plan to turn in early tonight, hoping for a better, longer Saturday into Sunday sleep.

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Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 08:15 – 1 peanut butter sandwich * 10:05 – 1 cheese sandwich * 12:45 – snacking on saltine crackers * 16:15 – home made vegetable soup, fried fish, white rice

Activities, Chores, etc.: * 06:00 – bank accounts activity monitored * 06:20 – read, pray, follow news reports from various sources, surf the socials, nap * 11:00 – watching Saturday morning cartoons * 12:00 – tuned to The Flagship Station for IU Sports for pregame coverage and then for the radio call of the NCAA men's college basketball game between the Iowa Hawkeyes and the Indiana Hoosiers. * 17:00 – now tuned into another Big Ten Conference men's basketball game, Purdue Boilermakers at USC Trojans * 18:00 – had a nice “Happy Birthday” video chat with my daughter. * 18:30 – back to the basketball game * 19:30 – listening to relaxing music

Chess: * 15:10 – moved in all pending CC games

In a relatively short time TTRPGs have evolved and blossomed from a dungeon and wilderness procedural coin-nicking game to, well, almost anything really. So I was thinking: what would happen if, rather than evolving, every TTRPG was just a variation of OD&D? I’ve conjured some answers from my sick mind for three of the biggies.

Vampire: The Masquerade

Storyteller system? Blech. This wouldn’t be about the melodramatics that goes with being a hot vampire. No, this is a game of territorial warfare, with the city as a megadungeon. Various clans would be represented as classes: Brujah for fighter, Tremere for wizard, and maybe Nosferatu as a thief type thing.

Rather than torches burning down you’d have blood points. Gotta keep feeding otherwise you save vs frenzy. But feeding on the gen pop increases the chance of being discovered. Save vs masquerade.

It’s blood for xp and influence instead of gold. The more influence, the more territory you can get.

Cyberpunk Red

Like Vampire Night City is a megadungeon or hexcrawl. This is gritty corpo espionage, baby! Rather than wandering monsters you’d have patrol alerts based on how you’re approaching the heist. Going in guns blazing is upping those patrol chances considerably.

Obviously the netrunner would be a wizard type, with programs instead of spells. The solo would be a fighter and techie the thief.

Torches would be swapped for battery power and signal range, the latter to keep the techie in the dungeon crawl.

No minigame for netrunning. Just a save vs brain getting fried up. And rather than rolling abilities success is based on your upgrades and tech.

Call of Cthulhu

So now we have an investigation crawl where sanity is impacted by the place you’re investigating and the wandering monsters. Roll vs sanity of reduce it like HP.

Classes are soldier (fighter), occultist (wizard), and investigator (thief). Only an occultist can read from magic tomes without losing sanity, or perhaps losing less.

Rather than looking for clues and solving a mystery this would be more about finding ancient secrets and getting them to a safe place (e.g. Miskatonic). The more mythos relics gained the more xp.

Torches would simply be flashlights and eldritch terrors would be like ten times as strong as a red dragon.

This has to be one of the strangest developments I've noticed in online communication recently—and yes, sadly, the real world, as if there were any difference.

At some point, it apparently became fashionable to slap the label narcissist on anyone who has behaved badly, as well as many people who haven't. Someone's ex is a narcissist. That one's boss is a narcissist. Everyone's parents are narcissists. What in the world is inspiring people to talk like this? Narcissistic Personality Disorder (NPD) does exist, yes, and there may be some very conspicuous examples of it in public life, although I'm not qualified to diagnose anyone. Still, it's a minority disorder. The Cleveland Clinic reports that NPD affects around 0.5% to 5% of Americans. Clearly, most people who behave badly do not qualify for a diagnosis. Moreover, mental illnesses like anxiety and depression are far more common.

Yes, sometimes people treat others badly because they are narcissists, but others are unkind due to their depression, anxiety, obsessive-compulsive disorder, bipolar disorder, borderline personality disorder, addiction, or one of the dozens of other psychological afflictions that cause so much pain. In most cases—not all, but most—I'm sure those suffering with these ailments endure much more agony than the people around them. Of course, that's assuming the target of the “narcissist” label is even clinically unwell. Maybe your boss just wants to further their own goals at the exclusion of yours. That's not narcissism. That's not mental illness. That's just corporate life. (I would argue that any manager pursuing their own goals at the exclusion of yours is a bad manager, but that doesn't make them a narcissist.)

Language evolves, and I suppose people can use the term narcissist to mean brute, if they choose. The dictionary wasn't handed down from the heavens, unchangeable. I just worry that being so sloppy with terminology unfairly demonizes the vast majority of mental illnesses that inspire unusual behavior for other reasons. I also think it can suggest a degree of intent that simply doesn't exist. Maybe that person at the convention shouted at you because they struggle with anger or because they never learned how disagree respectfully, not because they want to feel superior to you.

I do wonder—and this is pretty speculative—whether some people are so cavalier with the term narcissist because they want to deflect attention away from their own narcissism. I'm not talking about clinical narcissism, the type that seriously harms oneself and others, but rather more ordinary narcissism, the kind that leads one to believe that anyone actually cares about their status updates. I think it's plausible that social media does foster some amount of casual, everyday narcissism. Could it be that people throw the term around because they're uncomfortable facing their own shrouded narcissism?

Instead of throwing labels around, maybe we should spend more time looking in the mirror—in a healthy way. I will try to do the same.

#Life #SocialMedia #Tech

There are moments in every generation when a culture must decide whether it will protect what is fragile or reshape it to fit the anxieties of the moment. Children always stand at the center of those decisions. Not because they are weak, but because they are unfinished. Not because they lack worth, but because their worth is so great that it demands patience, care, and restraint. Faith has always understood this, even when society forgets it. Long before modern debates, Scripture treated childhood not as an identity to be declared, but as a sacred season to be guarded.

One of the quiet tragedies of modern life is how quickly we rush to define what has not yet had time to develop. We live in a world that struggles with waiting. We want answers now. Labels now. Certainty now. But faith does not operate on the timeline of anxiety. Faith moves at the pace of formation. It understands that some things cannot be hurried without harm. Children are among those things.

From a faith-based perspective, identity is not something imposed early; it is something revealed gradually. The idea that a child must settle deep questions of identity before they have even learned how to carry responsibility misunderstands both childhood and human development. Scripture never treats growth as a problem to be solved. It treats growth as a process to be trusted.

When we say there is no such thing as a “trans child,” what we are saying—when spoken carefully, lovingly, and responsibly—is not a denial of human experience or emotional struggle. It is a rejection of the idea that children must be permanently defined during a season that is, by its very nature, temporary. Childhood is fluid. It is exploratory. It is marked by imagination, imitation, emotional intensity, and incomplete understanding. That is not a flaw in children. It is the very condition that makes childhood what it is.

Faith recognizes that children live in borrowed language. They repeat what they hear. They try on ideas the way they try on clothes—seeing what fits, what feels comfortable, what draws attention, and what brings reassurance. This has always been true. Long before modern terminology existed, children still explored roles, behaviors, and expressions as part of learning who they are in relation to the world. Faith has never treated this exploration as a declaration of destiny.

Scripture consistently frames children as those who must be guided, protected, and taught—not tasked with resolving questions that even adults struggle to answer. “Train up a child” assumes that a child is not yet trained. “Teach them when they are young” assumes they are still learning. “Let the little children come to me” assumes they are welcomed without conditions, explanations, or labels.

Even Jesus, in His humanity, was not described as fully revealed in childhood. The Gospels tell us He grew. He increased in wisdom. He matured. Growth was not something to correct; it was something to honor. If growth was part of Christ’s human experience, then growth must be allowed space in the lives of children without being rushed or redefined.

One of the great confusions of our time is mistaking compassion for immediacy. True compassion does not rush to permanent conclusions based on temporary states. It does not panic at uncertainty. It does not treat discomfort as an emergency that must be resolved through irreversible decisions. Compassion sits with confusion. Compassion listens without demanding answers. Compassion understands that presence often heals more deeply than solutions.

Children who express confusion, discomfort, or difference are not announcing who they will be for the rest of their lives. They are communicating something internal that they do not yet have the language or perspective to understand. They are asking questions, not delivering verdicts. They are searching for safety, not certainty. Faith responds to that search with stability, not labels.

The modern impulse to define children early often comes from adult fear rather than child need. Adults fear getting it wrong. They fear not affirming enough. They fear causing harm by hesitation. But faith teaches us that fear-driven decisions rarely produce wisdom. Scripture repeatedly reminds us that fear clouds judgment, while patience clarifies it.

There is a difference between acknowledging a child’s feelings and allowing those feelings to define their identity. Faith honors feelings without surrendering to them. Feelings matter. They reveal inner experiences. But they are not rulers. They change. They evolve. They mature as understanding grows. Adults learn this over decades. Children are only beginning to learn it.

To place adult-level identity conclusions onto a child is not empowerment. It is a transfer of responsibility they are not equipped to carry. It asks them to make sense of questions that require life experience, emotional regulation, and cognitive maturity. Faith recognizes this as an unfair burden, no matter how well-intentioned it may be.

Jesus spoke with extraordinary seriousness about how adults treat children. His warnings were not abstract. They were direct. He understood that adults possess power over children—not just physical power, but interpretive power. Adults shape how children understand themselves. That power must be exercised with humility, restraint, and reverence.

Faith does not deny that some children experience deep distress, confusion, or discomfort. It does not minimize suffering. But it refuses to treat suffering as proof that a child’s identity must be redefined. Faith sees suffering as a signal for care, not conversion. It sees distress as a call for support, not categorization.

One of the most damaging messages a child can receive is that uncertainty is dangerous and must be resolved immediately. Faith teaches the opposite. It teaches that uncertainty is part of learning. That questions are not failures. That confusion is not condemnation. That time is a gift, not a threat.

Children do not need to be told who they are before they understand what it means to be human. They need love that does not flinch. They need adults who are calm enough to wait. They need guardians who are secure enough not to project their own fears onto developing minds.

Faith insists that the body is not an accident. It insists that creation has meaning even when understanding is incomplete. It insists that development is not something to override, but something to steward. Children are not raw material to be shaped by cultural trends. They are lives entrusted to care.

There is wisdom in letting children grow without pressure to self-diagnose, self-label, or self-define beyond their capacity. Faith does not fear that patience will erase truth. It trusts that truth emerges more clearly when it is not forced.

This is not about denying anyone’s humanity. It is about protecting childhood itself. It is about refusing to collapse a sacred season of growth into a battleground of adult ideologies. It is about remembering that children deserve more than answers—they deserve safety.

Faith does not say to a child, “You must decide who you are now.” Faith says, “You are allowed to grow.” Faith does not say, “This feeling defines you forever.” Faith says, “This feeling matters, and we will walk with you through it.” Faith does not say, “Your confusion means something is wrong.” Faith says, “Your confusion means you are human.”

The most loving thing an adult can offer a child is not certainty, but steadiness. Not labels, but presence. Not pressure, but protection. Faith has always known this, even when culture struggles to remember it.

Children deserve the gift of time. Time to mature. Time to learn. Time to understand their bodies, their emotions, their beliefs, and their place in the world without being rushed into conclusions they cannot yet evaluate.

God is not threatened by time. Love is not endangered by patience. Truth does not disappear when it is allowed to unfold.

And when we remember that, we stop arguing about children and start caring for them. We stop defining them and start protecting them. We stop demanding answers and start offering love.

That is not fear. That is not rejection. That is faith honoring the sacred process of becoming human.

Faith has always understood something modern culture struggles to hold at the same time: love and limits are not enemies. They are partners. Love without limits becomes indulgence. Limits without love become cruelty. Wisdom lives where both are present.

When we apply this to children, the clarity becomes even sharper. Children need love that is unwavering and limits that are protective. They need adults who are strong enough to say, “You don’t have to figure this out right now,” and gentle enough to say, “I’m not going anywhere while you grow.”

One of the quiet dangers of our age is how often adults confuse affirmation with agreement. Affirmation says, “You matter.” Agreement says, “You are correct.” Faith does not require adults to agree with every conclusion a child reaches in order to affirm their worth. In fact, responsible love often says, “I hear you,” without saying, “This must define you.”

Children are not miniature adults. They do not possess the neurological development, emotional regulation, or long-term perspective required to make permanent decisions about identity. This is not an insult. It is a biological and spiritual reality. Faith respects reality rather than pretending it can be overcome through willpower or ideology.

Throughout Scripture, maturity is treated as something that develops through time, experience, instruction, and testing. Wisdom is not assumed; it is acquired. Discernment is not automatic; it is learned. Stability is not innate; it is formed. To expect children to resolve identity questions that adults debate endlessly is not empowering—it is unreasonable.

Faith also recognizes the profound influence adults have over children. Words spoken by authority figures do not land neutrally. They shape self-perception. They frame inner narratives. They linger long after conversations end. This is why Scripture warns teachers so strongly. This is why Jesus spoke so fiercely about causing little ones to stumble. Adults do not merely respond to children; they shape the pathways children walk.

When adults rush to define children, they often do so without realizing they are collapsing a wide future into a narrow present. They take a moment of uncertainty and turn it into a lifelong story. Faith urges restraint precisely because the stakes are so high.

There is also a spiritual humility required here—an acknowledgment that adults do not fully understand the inner world of a child simply because a child expresses distress. Pain does not always mean the same thing. Discomfort does not point to one singular solution. Faith teaches us to ask, to listen, to explore, and to wait.

Children experience discomfort for countless reasons. Social pressure. Trauma. Anxiety. Sensory sensitivity. Fear of rejection. Desire for belonging. Struggles with expectations. These experiences deserve care, not compression into a single explanatory framework. Faith refuses to reduce the complexity of a human life into a slogan.

The idea that childhood discomfort must be resolved through identity redefinition often reveals more about adult impatience than child need. Faith teaches us that some struggles are meant to be walked through, not bypassed. Growth is often uncomfortable. Maturity is rarely painless. But discomfort is not evidence that something has gone wrong; sometimes it is evidence that development is happening.

There is a profound difference between helping a child cope with distress and teaching a child that their distress means their body or identity is fundamentally misaligned. Faith is cautious about messages that teach children to distrust their own embodied existence before they have even had time to understand it.

The body, in faith, is not an obstacle to be overcome. It is a gift to be understood. Scripture consistently treats embodiment as meaningful, purposeful, and worthy of care. Children deserve time to develop a relationship with their bodies that is grounded in respect rather than suspicion.

This does not mean ignoring a child’s pain. It means responding to pain without redefining the child. It means offering support without imposing narratives. It means helping children build resilience rather than teaching them that discomfort requires escape.

Faith also teaches that identity is not self-created in isolation. It is formed in relationship—with God, with family, with community. Children discover who they are through belonging, not through self-analysis. They learn stability by being surrounded by stable adults.

When adults project ideological certainty onto children, they often rob them of this relational grounding. The child becomes responsible for navigating abstract concepts they cannot yet contextualize. Faith insists that adults bear the weight of discernment so children do not have to.

One of the most loving things faith offers children is the assurance that they are not behind. They are not failing. They are not broken because they are unsure. Uncertainty is not a diagnosis. It is a stage.

The pressure to define identity early often carries an unspoken threat: if you don’t decide now, you will miss your chance. Faith rejects this lie. Faith teaches that God is not constrained by timelines of panic. Truth does not expire. Love does not evaporate with patience.

Children need to hear that they are allowed to change their minds. That exploration does not require conclusions. That they are not obligated to explain themselves in adult language. That they do not owe the world a definition before they are ready.

This is especially important in a culture that increasingly treats children as symbols rather than individuals. When children become representatives of causes, they lose the freedom to simply be children. Faith pushes back against this with quiet insistence: a child is not an argument. A child is a life.

Faith also calls adults to examine their own motivations. Are we responding out of fear or wisdom? Out of urgency or care? Out of ideology or love? Children feel the difference even when they cannot articulate it.

The faithful response to childhood confusion is not distance, dismissal, or diagnosis. It is closeness, listening, and steadiness. It is adults who are strong enough to say, “You are safe here,” without demanding resolution.

Perhaps the most radical act of faith in this moment is to trust that God can work through time. That development is not an emergency. That patience is not neglect. That waiting is not abandonment.

Children deserve adults who believe this deeply enough to live it.

When faith speaks into this conversation at its best, it does not shout. It does not condemn. It does not reduce complex lives to talking points. It speaks with gravity and gentleness. It says, “We will protect childhood because childhood is sacred.”

There is no such thing as a “trans child” because children are not finished. They are not final. They are not fixed. They are becoming.

And becoming requires time.

Time to grow. Time to learn. Time to feel. Time to understand.

Faith gives children that time—not because it is afraid of truth, but because it trusts it.

The greatest gift we can offer children in a confused world is not certainty, but constancy. Not answers, but assurance. Not labels, but love.

And sometimes the most faithful words an adult can speak to a child are the simplest ones:

You are loved. You are safe. You are not late. You are allowed to grow.

God is patient. Love is patient. And you have time.

Truth.

God bless you.

Bye bye.

Watch Douglas Vandergraph’s inspiring faith-based videos on YouTube.

Support the ministry by buying Douglas a coffee.

Your friend, Douglas Vandergraph

#faith #children #truthwithcompassion #wisdom #parenting #identity #hope #patience #love

Our Father Who art in heaven Hallowed be Thy name Thy Kingdom come Thy will be done on Earth as it is in heaven Give us this day our daily Bread And forgive us our trespasses As we forgive those who trespass against us And lead us not into temptation But deliver us from evil

Amen

Jesus is Lord! Come Lord Jesus!

Come Lord Jesus! Christ is Lord!

Blue Comet

And rains of the overcoat Sleighing safely- in Nuuk a place of burning refuge Days upon water on ice Feelings for venture Sequenced to night And the stars on offer Light our track Eyes locking- to an overbound comet The pattern and path Dreaming parallel Inspiredly- homing our range Feelings of mercy On the young, frail ground A pair of tiny whiskers Noted for style and senses at night Batches in order for lessons of peer Handheld with bells and mobiles And a crane and a comet

Could this be the one? Our new home? Murderous into They were here and left nothing But other people

Tidings and things The little bee That collected our signatures For the airlock Fortunes could be

The Americans are gone

Things with wings And clouds of twinflower and rain With no septic fear There is snow And we hurried still As before When we were new But not yet Danes And proud of the distance And we filled our stomachs With the fruits of our neighbour Selling beans and ochre and kale In return for no thing

The sustenance America brought Was nothing like the urge To send them packing

And the Danes won- as before Hiding hope just in case And we named them a fjord Our best Man and his day For the beautiful news We are new And renewed A new sense of home.

Yes, I was randomly scrolling through YouTube and watched this video. Yes, I should have done something productive instead, but I was watching this video. Simon explained in this video that you have to turn negative into positive, like what we need to tell our kids, too. Instead of “don’t eat on the couch,” say “eat on the table.” Or “don’t look for obstacles” – “look for the path.”

It is so simple. And I feel bad that I had that ah moment so late in my life. What a waste of time. :( BUT, better now than never.

Then I realized what he explained in this video; I was already doing unconsciously. I had this aha moment: for years, I always told myself that I had no time to do something. I have responsibilities, a wife, kids, a house, clients, and everything. There is no time in it to do, for example, side projects.

So, what I was doing subconsciously was, instead of saying, “I have no time,” I was looking for time. Like in the video as an example, the skier. Skiers look for the path and not the obstacles. And that’s it. I was looking for time slots where I could do something. And in the past year, I found a lot of them. 😎

It’s the same with taking small steps. At least you are taking steps. How big they are doesn’t matter.

---

86 of #100DaysToOffload
#log
Thoughts?

I've been using Linux almost exclusively as my operating system of choice for my personal computer for the last 6 years, and I couldn't be happier. I wanted to share a little about how even a layperson can use Linux for their basic computing needs, and to present options for anyone tired of using Windows and its ever-deteriorating operating system.

Windows is truly terrible. Remember when your computer didn't shove ads in your face? Windows ensnares you in their horrible Office ecosystem and the tentacles of Copilot now touch every bit of their operating system. I for one just want my computer to do what I tell it to do without trying to up-sell me or devour my every move to train Copilot. I want to be able to use my computer several years without being forced to upgrade through planned obsolescence.

If you've been using Windows for a long time and want out, I hope you give Linux a try. If you want to get started but find yourself overwhelmed by the process of installing Linux, find that one nerd friend or family member and ask them for help! Many of us Linux users would love if those in our circle joined us on the light-side and are eager to help get you started.

In this post, I'll talk briefly about what Linux is and the various distributions, or “flavours”. I'll then go into some customization you can do with Linux.

Linux Distributions

If you are not familiar with Linux, you may be wondering what a Linux distribution is. Essentially, Linux comes in a bunch of different flavours, and each flavour has its own pros and cons. Debian, for instance, is considered a very stable distribution, and is the basis for a number of other distributions. Two other popular distributions are Ubuntu and Arch. Ubuntu, like Debian, is considered a more stable distribution and is used by beginners and advanced users alike. Arch, on the other end, is considered more “cutting edge”, however, it requires more tinkering and isn't considered ideal for most new users. Another interesting Linux distribution is elementaryOS, which focuses on providing users with an experience closer to what they are used to with Apple, while still being Linux.

My Linux distribution of choice is Debian because I don't want to think too hard about the nitty-gritty of my operating system, and I'm okay with older, stable versions of certain software.

Desktop Environments

When you're deciding which Linux Distribution you want to run, you can also choose which desktop environment you'd like to use. A desktop environment is like the user interface, and unlike with Apple or Windows, you can choose from a variety of environments. Some Linux distributions, such as elementaryOS and Linux Mint, have their own desktop environments. From my experience, the two most popular desktop environments are GNOME and KDE. I always recommend taking some time digging through the settings of your newly installed desktop environment and customizing it, finding what works for you.

I use KDE and find it very intuitive with more than enough customization options for me.

Downloading Applications

After you've chosen the distribution and desktop environment, all that's left is to start installing the software you need to start using your newly Linux-ed computer! Most desktop environments will have a “software center” where you can look for applications to install. Software can also be downloaded from other sources when required.

If you want to get up and running, you are going to need an Office Suite and a browser. If you're looking for alternatives to Microsoft's Office Suite, see my previous post on Office Suite Alternatives and try LibreOffice. As for a browser, I recommend Firefox or Vivaldi as alternatives to Google Chrome.

And there you have it! Don't let Linux's reputation as the complex, scary operating system stop you from exploring alternatives to the ever-deteriorating Microsoft and Apple operating system experiences. Linux is as user-friendly as its ever been, and you can always ask for help getting started. All these Linux distributions have forums with folks who are more than happy to help answer any questions you may have.

There's a whole world outside the walled gardens ready for you to explore.

Wanted to state the following at the inception of my anonymous blog W1tn3ss:

• I am Pro-Life
• I am a fiscal conservative
• I believe in small government
• I believe in Personal Responsibility
• I believe in the right to bear arms
• I believe in individuals doing what they want so long as it doesn’t bother me

#abortion #life #ideology

Hawkeyes vs Hoosiers

This afternoon I'll be listening to The Flagship Station for IU Sports for pregame coverage and then for the radio call of the NCAA men's college basketball game between the Iowa Hawkeyes and the Indiana Hoosiers.

GO HOOSIERS!

Zout en de interventie van De Meterman

Da da da du liebst mich, ich lieb dich....nicht De deurdeun klingelde in de entree kas van huize Zout en zo nu en dan Peper. Zout sprong vol energie van de bank en rende naar de voor eigenlijk achterdeur, maar door toedoen van de huurmaatschappij zit daar voor eeuwig en altijd zolang de huur zal bestaan de achterdeur bel. Zout slingerde de deur open en sprak blij “Hallo Vreemdeling, van waar zijt gij gekomen en waarmede kan ik u van dienst zijn!!!”

De persoon buiten deinsde terug, niet gewend aan dergelijk vertoon van enthousiasme zeker niet in deze schuwe redelijk vers van de pers showciale huurwijk, Hij herpakte zich door zijn jasje recht te trekken en vervolgens in de houding te gaan staan en gaf daarop acht. “Gegroet burger, mijn naam is Haas, Haas je Repje-Niet, ik kom namens de huidige staat in opdracht van Het bureau Vossenhol en Dergelijke punt kom langs voor een noodzakelijke interventie tussen u en mij hier aan de voordeur. “Dit is de achterdeur Haas, het voordeel is slechts schijn maar laat u daardoor niet van de wijs brengen. Wat fijn dat u hier en nu tussen beiden komt, de plek inneemt tussen mij en de rest van ons, leuk.” zei Zout zoals altijd monter en eerlijk.

De deur aan deurloper ging voort met zijn boeiende relaas rondom zijn verschijnen aldaar “Ik zal u eerst mijn folder tonen met daarop een QR code die u moet scannen zodat u kunt zien dat ik geen crimineel ben die u van alles wil ontfutselen maar kom ter goeder trouw, goedgelovig en vol goede bedoelingen, dat ik legaal in dienst ben als Sociaal Controleur van een erkende salaris betaler. Heeft u voor dit doel, voor mij en het bedrijf, voor deze nobele IT daad de mobiele telefoon nabij? Dus niet de klassieke telefoon, graag, ik ken u oudjes altijd in de weer met moeilijkheden maken rondom moderne fratsen en oude gewoonten, dat moderne ding voor moderne vormen van communicatie, ja.” “Wel zeker heb ik die ergens in de buurt liggen en wel daar bij de computer waar hij altijd ligt omdat mijn telefoon maar voor een deel telefonisch is ingesteld maar feitelijk alleen dienst doet als modum, nodig voor het begeleiden van wild stromende data, dammen en of de usb sluispoorten open zetten zodat ik hier thuis nooit zomaar op een dag zonder beeld en geluid zit, Dit wonder der techniek ontstaan uit en en voor verveling omzetten in, nou ja, in geld eigenlijk, alleen hier niet, op een positie elders ver ver van mij. Ik haal de modum erbij, een moment geduld aub.”
… Zout scande de QR code, hij kon er niet onder uit, zo sprak de man aan de poort en dus doe je het, het kon ook niet anders dan oké zijn, anders druk je niet eerst zo'n folder, doe je die moeite niet. De QR code was een pad naar vele keurmerken, iso isolement isolatie normen, afkomst en milieu, neutraliteit, het verklaarde de man voor hem als echt belangrijk, zich staande houdend aan de juiste kant van de zakenwereld, hij voldeed aan de norm, alle normen die je wel en niet zou stellen aan mensen die altijd ergens tussen komen. Het werd zodoende ook duidelijk dat Haas een incasserende meter opnemer voorstelde, Zijn rol op aard was het opnemen van meetbare standen en voor die moeite moest Zout bij het opnemen van de meter 5 Smægmåånse Døllår betalen via een betrouwbare app aan deze persoon, zijn stand overmaken om zodoende te voldoen aan de schatplichten, Prima dacht Zout, dat is goed geregeld. Mooi dat het zo kan.

“U weet inmiddels dat ik hiervoor u sta als een officieel door de overhead goed gekeurde sociale controleur, en ik kom hier u sociale meterstand opnemen, het bewijs van goed gedrag voor elke burger die iedere dag met enorm veel plezier aan alle plichten voldoet en met even veel luim de lasten draagt. U weet waarschijnlijk nog niet waar de sociale meter zit die ik moet opnemen?!” zei Haas zeker van zijn zaak. Hij was inmiddels helemaal gemarineerd in het spul waarmee een mens zijn rol op aard uit kan voeren, opdienen aan de ander, het vertrouwen er in. Zout moest erkennen dat Haas in dezen gelijk had. Hij wist inderdaad niks van noch over de sociale meter in elk huis, Dit was de eerste keer dat hij te horen kreeg dat een dergelijk apparaat is ontwikkeld, in ieder huis zit, en kan worden afgelezen. Hij zei dan ook “Neen, inderdaad, het spijt me dat ik u wat dat betreft niet verder kan helpen. Wilt u anders gebruik maken van mijn toilet zodat u toch iets kan doen in plaats van meten?”

“Nee hoor, niet nodig ik weet als een van de weinige ambtenaren in dienst van het BV Smægmå waar de sociale meter in elk huis zit!” De controleur blaakte van trots bij het overhevelen van deze boodschap aan Zout zijn oren en dus hersenen. Zout stond perplex, als aan de vloer genageld bij het horen van dit bericht maar deed toch spontaan een stap opzij om de controleur volledig ter wille te staan en de geranium jungle te betreden, dit woonperk vol verwilderde gerania die langzamerhand het huis in bezit namen, In dit huis werd Zout bekeken door de Geraniums in plaats van andersom, aan beide kanten van het raam. De meteropnemer keek verschrikt naar de ontstane opening, verduidelijkte deze angst reactie met verhelderend informatie met betrekking tot de meter locatie. Uwer meter is mobiel!

Haas zei “U hoeft het alleen te pakken zoals u normaal doet bij een soortgelijke meter opname, het is namelijk u persoonsgebonden bank paspoort in combinatie met u oerpersoonsbewijs, het teken voor ons dat u ook echt bestaat en niet slechts een fantasietje bent ontstaan uit onze toeslagen en salarisstroken, daarmee wordt u sociale karakter wettelijk bewezen tijdens de overdracht van de vijf Døllår, de meterstand basis input, onze meta meter verbonden aan de applicatie doet dan de rest, wij meten dan daarmee het geheel der levenstransacties over x aantal jaren op diverse unieke locaties en aan de hand daarvan wordt dan de uiteindelijke sociale meterstand bepaald en krijgt u van onze aanslag afdeling een zeer hoge aanslag die u binnen een bepaald termijn moet betalen of anders zwaait er wat, de sirenes van de staat en het personeel dat altijd op sirenes afkomt, er aan wil verdienen of het zeker wil dienen, de sirene afhankelijken.”

“Oh, aaah, wat dom van mij dat ik u toestemming wou geven om in mijn gehuurde meterkast te turen voor de toestanden die ik ook al zelf moet betalen, en flink ook, zo'n geranium kas heeft veel peut nodig, maar ik trek meteen mijn buidel wijd open voor zo'n enorm goed meet doel, u zult zien dat ik aan die sociale waarden voldoe, nooit een dag niet consumeer en zelfs hier en daar wat meer produceer dan stront en pis alleen, ik net als u en mijn lezer(s) een volwaardig burger ben en deze deze torenhoge schuld absoluut verdien, O dat bedrag te betalen aan u en u grote opperhoofd, de grote graaier aan top van de sociale ladder, wat zal ik intens genieten van elk moment waarop ik het keer op keer afbetaal nadat ik hopelijk u enorme aanspraak op mijn hele leven van nu tot aan de eindigheid en beyond mag ontvangen! Dank u, dank u” Zout knielde voor de zekerheid neer voor de voeten van de sociale meterman en snel daarna betaalde hij zijn maatschappij lidmaatschap door hier aan de achterste voordeur zijn sociale meter via een app te laten opnemen. Het verlopen oerpaspoort werd niet met veel vreugd ontvangen maar Haas zei dat Zout hemzelve later toch altijd voor alle kosten van het niet en het wel nakomen van alle mogelijk gemaakte verplichtingen opdraaide, hoe dan ook, waar dan ook, Zo is het systeem van dergelijke afspraken nou eenmaal ingericht door de bron ervan, de grote graaier opa's opa en zo voorts. Zout zei ten slotte om aan dit deur bezoek een einde te breien “Mea Culpa” en sloot af met “Mooi dat het zo kan, fijn dat ik u volledig moet vertrouwen en dat ik elke dag de lasten mag dragen voor u fabelachtig bestaan, o edelmoedig edelman” Hij boog voor de zekerheid nogmaals voor de meterman, een korte hoofdse kniebuiging, en sloeg daarna de achterdeur voor zijn neus dicht want de geraniums mochten door zo'n moedwillige interventie natuurlijk niet verkouden worden.

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