Analytics, Business Intelligence, Data Cleaning, Data Development, Data Governance, Data Integration, Data Modeling, Data Quality Assurance, Data Security, Data Strategy, Data Visualization

Self-Service Analytics: Empowering Users While Maintaining Trust and Control

Self-service analytics has become a cornerstone of modern data strategies. As organizations generate more data and business users demand faster insights, relying solely on centralized analytics teams creates bottlenecks. Self-service analytics shifts part of the analytical workload closer to the business—while still requiring strong foundations in data quality, governance, and enablement.

This article is based on a detailed presentation I did at a HIUG conference a few years ago.

What Is Self-Service Analytics?

Self-service analytics refers to the ability for business users—such as analysts, managers, and operational teams—to access, explore, analyze, and visualize data on their own, without requiring constant involvement from IT or centralized data teams.

Instead of submitting requests and waiting days or weeks for reports, users can:

  • Explore curated datasets
  • Build their own dashboards and reports
  • Answer ad-hoc questions in real time
  • Make data-driven decisions within their daily workflows

Self-service does not mean unmanaged or uncontrolled analytics. Successful self-service environments combine user autonomy with governed, trusted data and clear usage standards.

Why Implement or Provide Self-Service Analytics?

Organizations adopt self-service analytics to address speed, scalability, and empowerment challenges.

Key Benefits

  • Faster Decision-Making
    Users can answer questions immediately instead of waiting in a reporting queue.
  • Reduced Bottlenecks for Data Teams
    Central teams spend less time producing basic reports and more time on high-value work such as modeling, optimization, and advanced analytics.
  • Greater Business Engagement with Data
    When users interact directly with data, data literacy improves and analytics becomes part of everyday decision-making.
  • Scalability
    A small analytics team cannot serve hundreds or thousands of users manually. Self-service scales insight generation across the organization.
  • Better Alignment with Business Context
    Business users understand their domain best and can explore data with that context in mind, uncovering insights that might otherwise be missed.

Why Not Implement Self-Service Analytics? (Challenges & Risks)

While powerful, self-service analytics introduces real risks if implemented poorly.

Common Challenges

  • Data Inconsistency & Conflicting Metrics
    Without shared definitions, different users may calculate the same KPI differently, eroding trust.
  • “Spreadsheet Chaos” at Scale
    Self-service without governance can recreate the same problems seen with uncontrolled Excel usage—just in dashboards.
  • Overloaded or Misleading Visuals
    Users may build reports that look impressive but lead to incorrect conclusions due to poor data modeling or statistical misunderstandings.
  • Security & Privacy Risks
    Improper access controls can expose sensitive or regulated data.
  • Low Adoption or Misuse
    Without training and support, users may feel overwhelmed or misuse tools, resulting in poor outcomes.
  • Shadow IT
    If official self-service tools are too restrictive or confusing, users may turn to unsanctioned tools and data sources.

What an Environment Looks Like Without Self-Service Analytics

In organizations without self-service analytics, patterns tend to repeat:

  • Business users submit report requests via tickets or emails
  • Long backlogs form for even simple questions
  • Analytics teams become report factories
  • Insights arrive too late to influence decisions
  • Users create their own disconnected spreadsheets and extracts
  • Trust in data erodes due to multiple versions of the truth

Decision-making becomes reactive, slow, and often based on partial or outdated information.

How Things Change With Self-Service Analytics

When implemented well, self-service analytics fundamentally changes how an organization works with data.

  • Users explore trusted datasets independently
  • Analytics teams focus on enablement, modeling, and governance
  • Insights are discovered earlier in the decision cycle
  • Collaboration improves through shared dashboards and metrics
  • Data becomes part of daily conversations, not just monthly reports

The organization shifts from report consumption to insight exploration. Well, that’s the goal.

How to Implement Self-Service Analytics Successfully

Self-service analytics is as much an operating model as it is a technology choice. The list below outlines important aspects that must be considered, decided on, and implemented when planning the implementation of self-service analytics.

1. Data Foundation

  • Curated, well-modeled datasets (often star schemas or semantic models)
  • Clear metric definitions and business logic
  • Certified or “gold” datasets for common use cases
  • Data freshness aligned with business needs

A strong semantic layer is critical—users should not have to interpret raw tables.

2. Processes

  • Defined workflows for dataset creation and certification
  • Clear ownership for data products and metrics
  • Feedback loops for users to request improvements or flag issues
  • Change management processes for metric updates

3. Security

  • Role-based access control (RBAC)
  • Row-level and column-level security where needed
  • Separation between sensitive and general-purpose datasets
  • Audit logging and monitoring of usage

Security must be embedded, not bolted on.

4. Users & Roles

Successful self-service environments recognize different user personas:

  • Consumers: View and interact with dashboards
  • Explorers: Build their own reports from curated data
  • Power Users: Create shared datasets and advanced models
  • Data Teams: Govern, enable, and support the ecosystem

Not everyone needs the same level of access or capability.

5. Training & Enablement

  • Tool-specific training (e.g., how to build reports correctly)
  • Data literacy education (interpreting metrics, avoiding bias)
  • Best practices for visualization and storytelling
  • Office hours, communities of practice, and internal champions

Training is ongoing—not a one-time event.

6. Documentation

  • Metric definitions and business glossaries
  • Dataset descriptions and usage guidelines
  • Known limitations and caveats
  • Examples of certified reports and dashboards

Good documentation builds trust and reduces rework.

7. Data Governance

Self-service requires guardrails, not gates.

Key governance elements include:

  • Data ownership and stewardship
  • Certification and endorsement processes
  • Naming conventions and standards
  • Quality checks and validation
  • Policies for personal vs shared content

Governance should enable speed while protecting consistency and trust.

8. Technology & Tools

Modern self-service analytics typically includes:

Data Platforms

  • Cloud data warehouses or lakehouses
  • Centralized semantic models

Data Visualization & BI Tools

  • Interactive dashboards and ad-hoc analysis
  • Low-code or no-code report creation
  • Sharing and collaboration features

Supporting Capabilities

  • Metadata management
  • Cataloging and discovery
  • Usage monitoring and adoption analytics

The key is selecting tools that balance ease of use with enterprise-grade governance.

Conclusion

Self-service analytics is not about giving everyone raw data and hoping for the best. It is about empowering users with trusted, governed, and well-designed data experiences.

Organizations that succeed treat self-service analytics as a partnership between data teams and the business—combining strong foundations, thoughtful governance, and continuous enablement. When done right, self-service analytics accelerates decision-making, scales insight creation, and embeds data into the fabric of everyday work.

Thanks for reading!

AI Governance, Data Governance, Data Quality Assurance

Glossary – 100 “Data Governance” Terms

Below is a glossary that includes 100 “Data Governance” terms and phrases, along with their definitions and examples, in alphabetical order. Enjoy!

TermDefinition & Example
Access ControlRestricting data access. Example: Role-based permissions.
Audit TrailRecord of data access and changes. Example: Who updated records.
Business GlossaryStandardized business terms. Example: Definition of “Revenue”.
Business MetadataBusiness context of data. Example: KPI definitions.
Change ManagementManaging governance adoption. Example: New policy rollout.
Compliance AuditFormal governance assessment. Example: External audit.
Consent ManagementTracking user permissions. Example: Marketing opt-ins.
ControlMechanism to reduce risk. Example: Access approval workflows.
Control FrameworkStructured control set. Example: SOX controls.
Data AccountabilityClear responsibility for data outcomes. Example: Named data owners.
Data Accountability ModelFramework assigning responsibility. Example: Owner–steward mapping.
Data AccuracyCorrectness of data values. Example: Valid email addresses.
Data ArchivingMoving inactive data to long-term storage. Example: Historical logs.
Data BreachUnauthorized data exposure. Example: Leaked customer records.
Data CatalogCentralized inventory of data assets. Example: Enterprise data catalog tool.
Data CertificationMarking trusted datasets. Example: “Certified” badge.
Data ClassificationCategorizing data by sensitivity. Example: Public vs confidential.
Data CompletenessPresence of required data. Example: No missing customer IDs.
Data ComplianceAdherence to internal policies. Example: Quarterly audits.
Data ConsistencyUniform data representation. Example: Same currency everywhere.
Data ContractAgreement on data structure and SLAs. Example: Producer-consumer contract.
Data CustodianTechnical role managing data infrastructure. Example: Database administrator.
Data DictionaryRepository of field definitions. Example: Column descriptions.
Data DisposalSecure deletion of data. Example: End-of-life purging.
Data DomainLogical grouping of data. Example: Finance data domain.
Data EthicsResponsible use of data. Example: Avoiding discriminatory models.
Data GovernanceFramework of policies, roles, and processes for managing data. Example: Enterprise data governance program.
Data Governance CharterFormal governance mandate. Example: Executive-approved charter.
Data Governance CouncilOversight group for governance decisions. Example: Cross-functional committee.
Data Governance MaturityLevel of governance capability. Example: Ad hoc vs optimized.
Data Governance PlatformIntegrated governance tooling. Example: Enterprise governance suite.
Data Governance RoadmapPlanned governance initiatives. Example: 3-year roadmap.
Data HarmonizationAligning data definitions. Example: Unified metrics.
Data IntegrationCombining data from multiple sources. Example: CRM + ERP merge.
Data IntegrityTrustworthiness across lifecycle. Example: Referential integrity.
Data Issue ManagementTracking and resolving data issues. Example: Data quality tickets.
Data LifecycleStages from creation to disposal. Example: Create → archive → delete.
Data LineageTracking data from source to consumption. Example: Source → dashboard mapping.
Data LiteracyAbility to understand and use data. Example: Training programs.
Data MaskingObscuring sensitive data. Example: Masked credit card numbers.
Data MeshDomain-oriented governance approach. Example: Decentralized ownership.
Data MonitoringContinuous oversight of data. Example: Schema change alerts.
Data ObservabilityMonitoring data health. Example: Freshness alerts.
Data OwnerAccountable role for a dataset. Example: VP of Sales owns sales data.
Data Ownership MatrixMapping data to owners. Example: RACI chart.
Data Ownership ModelAssignment of accountability. Example: Business-owned data.
Data Ownership TransferChanging ownership responsibility. Example: Org restructuring.
Data PolicyHigh-level rules for data handling. Example: Data retention policy.
Data PrivacyProper handling of personal data. Example: GDPR compliance.
Data ProductGoverned, consumable dataset. Example: Curated sales table.
Data ProfilingAssessing data characteristics. Example: Null percentage analysis.
Data QualityAccuracy, completeness, and reliability of data. Example: No duplicate customer IDs.
Data Quality RuleCondition data must meet. Example: Order date cannot be null.
Data RetentionRules for how long data is kept. Example: 7-year retention policy.
Data Review ProcessPeriodic governance review. Example: Policy refresh.
Data RiskPotential harm from data misuse. Example: Regulatory fines.
Data SecuritySafeguarding data from unauthorized access. Example: Encryption at rest.
Data Sharing AgreementRules for sharing data. Example: Partner data exchange.
Data StandardAgreed-upon data definition or format. Example: ISO country codes.
Data StewardshipOperational responsibility for data quality and usage. Example: Business steward for customer data.
Data TimelinessData availability when needed. Example: Daily refresh SLA.
Data TraceabilityAbility to trace data changes. Example: Transformation history.
Data TransparencyVisibility into data usage and meaning. Example: Open definitions.
Data TrustConfidence in data reliability. Example: Executive reporting.
Data Usage PolicyRules for data consumption. Example: Analytics-only usage.
Data ValidationChecking data against rules. Example: Type and range checks.
EncryptionEncoding data for protection. Example: AES encryption.
Enterprise Data GovernanceOrganization-wide governance approach. Example: Company-wide standards.
Exception ManagementHandling rule violations. Example: Approved data overrides.
Federated GovernanceShared governance model. Example: Domain-level ownership.
Golden RecordSingle trusted version of an entity. Example: Unified customer profile.
Governance FrameworkStructured governance approach. Example: DAMA-DMBOK.
Governance MetricsMeasurements of governance success. Example: Issue resolution time.
Impact AnalysisAssessing effects of data changes. Example: Column removal impact.
Incident ResponseHandling data security incidents. Example: Breach mitigation plan.
KPI (Governance KPI)Metric for governance effectiveness. Example: Data quality score.
Least PrivilegeMinimum access needed principle. Example: Read-only analyst access.
Master DataCore business entities. Example: Customers, products.
MetadataInformation describing data. Example: Column definitions.
Metadata ManagementManaging metadata lifecycle. Example: Automated harvesting.
Operating ControlsDay-to-day governance controls. Example: Access reviews.
Operating ModelHow governance roles interact. Example: Centralized governance.
Operational MetadataData about data processing. Example: Load timestamps.
Personally Identifiable Information (PII)Data identifying individuals. Example: Social Security number.
Policy EnforcementEnsuring policies are followed. Example: Automated checks.
Policy ExceptionApproved deviation from policy. Example: Temporary access grant.
Policy LifecycleCreation, approval, review of policies. Example: Annual updates.
Protected Health Information (PHI)Health-related personal data. Example: Medical records.
Reference ArchitectureStandard governance architecture. Example: Approved tooling stack.
Reference DataControlled value sets. Example: Country lists.
Regulatory ComplianceMeeting legal data requirements. Example: GDPR, CCPA.
Risk AssessmentEvaluating governance risks. Example: Privacy risk scoring.
Risk ManagementIdentifying and mitigating data risks. Example: Privacy risk assessment.
Sensitive DataData requiring protection. Example: Financial records.
SLA (Service Level Agreement)Data delivery expectations. Example: Refresh by 8 AM.
Stakeholder EngagementInvolving business users. Example: Governance workshops.
Stewardship ModelStructure of stewardship roles. Example: Business and technical stewards.
Technical MetadataSystem-level data information. Example: Data types and schemas.
TokenizationReplacing sensitive data with tokens. Example: Payment systems.
Tooling EcosystemSet of governance tools. Example: Catalog + lineage tools.
Data Careers, Data Engineering, Data Integration

What Exactly Does a Data Engineer Do?

A Data Engineer is responsible for building and maintaining the systems that allow data to be collected, stored, transformed, and delivered reliably for analytics and downstream use cases. While Data Analysts focus on insights and decision-making, Data Engineers focus on making data available, trustworthy, and scalable.

In many organizations, nothing in analytics works well without strong data engineering underneath it.

The Core Purpose of a Data Engineer

At its core, the role of a Data Engineer is to:

  • Design and build data pipelines
  • Ensure data is reliable, timely, and accessible
  • Create the foundation that enables analytics, reporting, and data science

Data Engineers make sure that when someone asks a question of the data, the data is actually there—and correct.

Typical Responsibilities of a Data Engineer

While the exact responsibilities vary by company size and maturity, most Data Engineers spend time across the following areas.

Ingesting Data from Source Systems

Data Engineers build processes to ingest data from:

  • Operational databases
  • SaaS applications
  • APIs and event streams
  • Files and external data sources

This ingestion can be batch-based, streaming, or a mix of both, depending on the business needs.

Building and Maintaining Data Pipelines

Once data is ingested, Data Engineers:

  • Transform raw data into usable formats
  • Handle schema changes and data drift
  • Manage dependencies and scheduling
  • Monitor pipelines for failures and performance issues

Pipelines must be repeatable, resilient, and observable.

Managing Data Storage and Platforms

Data Engineers design and maintain:

  • Data warehouses and lakehouses
  • Data lakes and object storage
  • Partitioning, indexing, and performance strategies

They balance cost, performance, scalability, and ease of use while aligning with organizational standards.

Ensuring Data Quality and Reliability

A key responsibility is ensuring data can be trusted. This includes:

  • Validating data completeness and accuracy
  • Detecting anomalies or missing data
  • Implementing data quality checks and alerts
  • Supporting SLAs for data freshness

Reliable data is not accidental—it is engineered.

Enabling Analytics and Downstream Use Cases

Data Engineers work closely with:

  • Data Analysts and BI developers
  • Analytics engineers
  • Data scientists and ML engineers

They ensure datasets are structured in a way that supports efficient querying, consistent metrics, and self-service analytics.

Common Tools Used by Data Engineers

The exact toolset varies, but Data Engineers often work with:

  • Databases & Warehouses (e.g., cloud data platforms)
  • ETL / ELT Tools and orchestration frameworks
  • SQL for transformations and validation
  • Programming Languages such as Python, Java, or Scala
  • Streaming Technologies for real-time data
  • Infrastructure & Cloud Platforms
  • Monitoring and Observability Tools

Tooling matters, but design decisions matter more.

What a Data Engineer Is Not

Understanding role boundaries helps teams work effectively.

A Data Engineer is typically not:

  • A report or dashboard builder
  • A business stakeholder defining KPIs
  • A data scientist focused on modeling and experimentation
  • A system administrator managing only infrastructure

That said, in smaller teams, Data Engineers may wear multiple hats.

What the Role Looks Like Day-to-Day

A typical day for a Data Engineer might include:

  • Investigating a failed pipeline or delayed data load
  • Updating transformations to accommodate schema changes
  • Optimizing a slow query or job
  • Reviewing data quality alerts
  • Coordinating with analysts on new data needs
  • Deploying pipeline updates

Much of the work is preventative—ensuring problems don’t happen later.

How the Role Evolves Over Time

As organizations mature, the Data Engineer role evolves:

  • From manual ETL → automated, scalable pipelines
  • From siloed systems → centralized platforms
  • From reactive fixes → proactive reliability engineering
  • From data movement → data platform architecture

Senior Data Engineers often influence platform strategy, standards, and long-term technical direction.

Why Data Engineers Are So Important

Data Engineers are critical because:

  • They prevent analytics from becoming fragile or inconsistent
  • They enable speed without sacrificing trust
  • They scale data usage across the organization
  • They reduce technical debt and operational risk

Without strong data engineering, analytics becomes slow, unreliable, and difficult to scale.

Final Thoughts

A Data Engineer’s job is not just moving data from one place to another. It is about designing systems that make data dependable, usable, and sustainable.

When Data Engineers do their job well, everyone downstream—from analysts to executives—can focus on asking better questions instead of questioning the data itself.

Good luck on your data journey!

Data Education & Training, Data Science

Glossary – 100 “Data Science” Terms

Below is a glossary that includes 100 “Data Science” terms and phrases, along with their definitions and examples, in alphabetical order. Enjoy!

TermDefinition & Example
A/B TestingComparing two variants. Example: Website layout test.
AccuracyOverall correct predictions rate. Example: 90% accuracy.
Actionable InsightInsight leading to action. Example: Improve onboarding.
AlgorithmProcedure used to train models. Example: Decision trees.
Alternative HypothesisAssumption opposing the null hypothesis. Example: Group A performs better than B.
AUCArea under ROC curve. Example: Model ranking metric.
Bayesian InferenceUpdating probabilities with new evidence. Example: Prior and posterior beliefs.
Bias-Variance TradeoffBalance between simplicity and flexibility. Example: Model tuning.
BootstrappingResampling technique for estimation. Example: Estimating confidence intervals.
Business ProblemDecision-focused question. Example: Why churn increased.
CausationOne variable directly affects another. Example: Price drop causes sales increase.
ClassificationPredicting categories. Example: Spam detection.
ClusteringGrouping similar observations. Example: Market segmentation.
Computer VisionInterpreting images and video. Example: Image classification.
Confidence IntervalRange likely containing the true value. Example: 95% CI for average revenue.
Confusion MatrixTable evaluating classification results. Example: True positives vs false positives.
CorrelationStrength of relationship between variables. Example: Ad spend vs revenue.
Cross-ValidationRepeated training/testing splits. Example: k-fold CV.
Data DriftChange in input data distribution. Example: New demographics.
Data ImputationReplacing missing values. Example: Median imputation.
Data LeakageTraining model with future information. Example: Using post-event data.
Data ScienceInterdisciplinary field combining statistics, programming, and domain knowledge to extract insights from data. Example: Predicting customer churn.
Data StorytellingCommunicating insights effectively. Example: Executive dashboards.
DatasetA structured collection of data for analysis. Example: Customer transactions table.
Deep LearningMulti-layer neural networks. Example: Speech recognition.
Descriptive StatisticsSummary statistics of data. Example: Mean, median.
Dimensionality ReductionReducing number of features. Example: PCA.
Effect SizeMagnitude of difference or relationship. Example: Lift in conversion rate.
Ensemble LearningCombining multiple models. Example: Boosting techniques.
Ethics in Data ScienceResponsible use of data and models. Example: Avoiding biased predictions.
ExperimentationTesting hypotheses with data. Example: A/B testing.
Explainable AI (XAI)Techniques to explain predictions. Example: SHAP values.
Exploratory Data Analysis (EDA)Initial data investigation using statistics and visuals. Example: Distribution plots.
F1 ScoreBalance of precision and recall. Example: Imbalanced datasets.
FeatureAn input variable used in modeling. Example: Customer age.
Feature EngineeringCreating new features from raw data. Example: Tenure calculated from signup date.
ForecastingPredicting future values. Example: Demand forecasting.
GeneralizationModel performance on unseen data. Example: Stable test accuracy.
Hazard FunctionInstantaneous event rate. Example: Churn risk over time.
Holdout SetData reserved for final evaluation. Example: Final test dataset.
HyperparameterPre-set model configuration. Example: Learning rate.
HypothesisA testable assumption about data. Example: Discounts increase conversion rates.
Hypothesis TestingStatistical method to evaluate assumptions. Example: t-test for average sales.
InsightMeaningful analytical finding. Example: High churn among new users.
LabelKnown output used in supervised learning. Example: Fraud or not fraud.
LikelihoodProbability of data given parameters. Example: Used in Bayesian models.
Loss FunctionMeasures prediction error. Example: Mean squared error.
MeanArithmetic average. Example: Average sales value.
MedianMiddle value of ordered data. Example: Median income.
Missing ValuesAbsent data points. Example: Null customer age.
ModeMost frequent value. Example: Most common category.
ModelMathematical representation learned from data. Example: Logistic regression.
Model DriftPerformance degradation over time. Example: Changing customer behavior.
Model InterpretabilityUnderstanding model decisions. Example: Feature importance.
Monte Carlo SimulationRandom sampling to model uncertainty. Example: Risk modeling.
Natural Language Processing (NLP)Analyzing human language. Example: Sentiment analysis.
Neural NetworkModel inspired by the human brain. Example: Image recognition.
Null HypothesisDefault assumption of no effect. Example: No difference between two groups.
OptimizationProcess of minimizing loss. Example: Gradient descent.
OutlierValue significantly different from others. Example: Unusually large purchase.
OverfittingModel memorizes training data. Example: Poor test performance.
PipelineEnd-to-end data science workflow. Example: Ingest → train → deploy.
PopulationEntire group of interest. Example: All customers.
Posterior ProbabilityUpdated belief after observing data. Example: Updated churn likelihood.
PrecisionCorrect positive prediction rate. Example: Fraud detection precision.
Principal Component Analysis (PCA)Linear dimensionality reduction technique. Example: Visualizing high-dimensional data.
Prior ProbabilityInitial belief before observing data. Example: Baseline churn rate.
p-valueProbability of observing results under the null hypothesis. Example: p < 0.05 indicates significance.
RecallAbility to identify all positives. Example: Medical diagnosis.
RegressionPredicting numeric values. Example: Sales forecasting.
Reinforcement LearningLearning via rewards and penalties. Example: Game-playing AI.
ReproducibilityAbility to recreate results. Example: Fixed random seeds.
ROC CurveClassifier performance visualization. Example: Threshold comparison.
SamplingSelecting subset of data. Example: Survey sample.
Sampling BiasNon-representative sampling. Example: Surveying only active users.
SeasonalityRepeating time-based patterns. Example: Holiday sales.
Semi-Structured DataData with flexible structure. Example: JSON files.
StackingEnsemble method using meta-models. Example: Combining classifiers.
Standard DeviationAverage distance from the mean. Example: Price volatility.
StationarityStable statistical properties over time. Example: Mean doesn’t change.
Statistical PowerProbability of detecting a true effect. Example: Larger sample sizes increase power.
Statistical SignificanceEvidence results are unlikely due to chance. Example: Rejecting the null hypothesis.
Structured DataData with a fixed schema. Example: SQL tables.
Supervised LearningLearning with labeled data. Example: Credit risk prediction.
Survival AnalysisModeling time-to-event data. Example: Customer churn timing.
Target VariableThe outcome a model predicts. Example: Loan default indicator.
Test DataData used to evaluate model performance. Example: Held-out validation set.
Text MiningExtracting insights from text. Example: Topic modeling.
Time SeriesData indexed by time. Example: Daily stock prices.
TokenizationSplitting text into units. Example: Words or subwords.
Training DataData used to train a model. Example: Historical transactions.
Transfer LearningReusing pretrained models. Example: Image models for medical scans.
TrendLong-term direction in data. Example: Growing user base.
UnderfittingModel too simple to capture patterns. Example: High bias.
Unstructured DataData without predefined structure. Example: Text, images.
Unsupervised LearningLearning without labels. Example: Customer clustering.
Uplift ModelingMeasuring treatment impact. Example: Marketing campaign effectiveness.
Validation SetData used for tuning models. Example: Hyperparameter selection.
VarianceMeasure of data spread. Example: Sales variability.
Word EmbeddingsNumerical text representations. Example: Word2Vec.
Data Careers, Data Science

What Exactly Does a Data Scientist Do?

A Data Scientist focuses on using statistical analysis, experimentation, and machine learning to understand complex problems and make predictions about what is likely to happen next. While Data Analysts often explain what has already happened, and Data Engineers build the systems that deliver data, Data Scientists explore patterns, probabilities, and future outcomes.

At their best, Data Scientists help organizations move from descriptive insights to predictive and prescriptive decision-making.

The Core Purpose of a Data Scientist

At its core, the role of a Data Scientist is to:

  • Explore complex and ambiguous problems using data
  • Build models that explain or predict outcomes
  • Quantify uncertainty and risk
  • Inform decisions with probabilistic insights

Data Scientists are not just model builders—they are problem solvers who apply scientific thinking to business questions.

Typical Responsibilities of a Data Scientist

While responsibilities vary by organization and maturity, most Data Scientists work across the following areas.

Framing the Problem and Defining Success

Data Scientists work with stakeholders to:

  • Clarify the business objective
  • Determine whether a data science approach is appropriate
  • Define measurable success criteria
  • Identify constraints and assumptions

A key skill is knowing when not to use machine learning.

Exploring and Understanding Data

Before modeling begins, Data Scientists:

  • Perform exploratory data analysis (EDA)
  • Investigate distributions, correlations, and outliers
  • Identify data gaps and biases
  • Assess data quality and suitability for modeling

This phase often determines whether a project succeeds or fails.

Feature Engineering and Data Preparation

Transforming raw data into meaningful inputs is a major part of the job:

  • Creating features that capture real-world behavior
  • Encoding categorical variables
  • Handling missing or noisy data
  • Scaling and normalizing data where needed

Good features often matter more than complex models.

Building and Evaluating Models

Data Scientists develop and test models such as:

  • Regression and classification models
  • Time-series forecasting models
  • Clustering and segmentation techniques
  • Anomaly detection systems

They evaluate models using appropriate metrics and validation techniques, balancing accuracy with interpretability and robustness.

Communicating Results and Recommendations

A critical responsibility is explaining:

  • What the model does and does not do
  • How confident the predictions are
  • What trade-offs exist
  • How results should be used in decision-making

A model that cannot be understood or trusted will rarely be adopted.

Common Tools Used by Data Scientists

While toolsets vary, Data Scientists commonly use:

  • Programming Languages such as Python or R
  • Statistical & ML Libraries (e.g., scikit-learn, TensorFlow, PyTorch)
  • SQL for data access and exploration
  • Notebooks for experimentation and analysis
  • Visualization Libraries for data exploration
  • Version Control for reproducibility

The emphasis is on experimentation, iteration, and learning.

What a Data Scientist Is Not

Clarifying misconceptions is important.

A Data Scientist is typically not:

  • A report or dashboard developer
  • A data engineer focused on pipelines and infrastructure
  • An AI product that automatically solves business problems
  • A decision-maker replacing human judgment

In practice, Data Scientists collaborate closely with analysts, engineers, and business leaders.

What the Role Looks Like Day-to-Day

A typical day for a Data Scientist may include:

  • Exploring a new dataset or feature
  • Testing model assumptions
  • Running experiments and comparing results
  • Reviewing model performance
  • Discussing findings with stakeholders
  • Iterating based on feedback or new data

Much of the work is exploratory and non-linear.

How the Role Evolves Over Time

As organizations mature, the Data Scientist role often evolves:

  • From ad-hoc modeling → repeatable experimentation
  • From isolated analysis → productionized models
  • From accuracy-focused → impact-focused outcomes
  • From individual contributor → technical or domain expert

Senior Data Scientists often guide model strategy, ethics, and best practices.

Why Data Scientists Are So Important

Data Scientists add value by:

  • Quantifying uncertainty and risk
  • Anticipating future outcomes
  • Enabling proactive decision-making
  • Supporting innovation through experimentation

They help organizations move beyond hindsight and into foresight.

Final Thoughts

A Data Scientist’s job is not simply to build complex models—it is to apply scientific thinking to messy, real-world problems using data.

When Data Scientists succeed, their work informs smarter decisions, better products, and more resilient strategies—always in partnership with engineering, analytics, and the business.

Good luck on your data journey!

Analytics, Business Intelligence, Data Analysis, Data Careers

What Exactly Does a Data Analyst Do?

The role of a Data Analyst is often discussed, frequently hired for, and sometimes misunderstood. While job titles and responsibilities can vary by organization, the core purpose of a Data Analyst is consistent: to turn data into insight that supports better decisions.

Data Analysts sit at the intersection of business questions, data systems, and analytical thinking. They help organizations understand what is happening, why it is happening, and what actions should be taken as a result.

The Core Purpose of a Data Analyst

At its heart, a Data Analyst’s job is to:

  • Translate business questions into analytical problems
  • Explore and analyze data to uncover patterns and trends
  • Communicate findings in a way that drives understanding and action

Data Analysts do not simply produce reports—they provide context, interpretation, and clarity around data.

Typical Responsibilities of a Data Analyst

While responsibilities vary by industry and maturity level, most Data Analysts spend time across the following areas.

Understanding the Business Problem

A Data Analyst works closely with stakeholders to understand:

  • What decision needs to be made
  • What success looks like
  • Which metrics actually matter

This step is critical. Poorly defined questions lead to misleading analysis, no matter how good the data is.

Accessing, Cleaning, and Preparing Data

Before analysis can begin, data must be usable. This often includes:

  • Querying data from databases or data warehouses
  • Cleaning missing, duplicate, or inconsistent data
  • Joining multiple data sources
  • Validating data accuracy and completeness

A significant portion of a Data Analyst’s time is spent here, ensuring the analysis is built on reliable data.

Analyzing Data and Identifying Insights

Once data is prepared, the Data Analyst:

  • Performs exploratory data analysis (EDA)
  • Identifies trends, patterns, and anomalies
  • Compares performance across time, segments, or dimensions
  • Calculates and interprets key metrics and KPIs

This is where analytical thinking matters most—knowing what to look for and what actually matters.

Creating Reports and Dashboards

Data Analysts often design dashboards and reports that:

  • Track performance against goals
  • Provide visibility into key metrics
  • Allow users to explore data interactively

Good dashboards focus on clarity and usability, not just visual appeal.

Communicating Findings

One of the most important (and sometimes underestimated) aspects of the role is communication. Data Analysts:

  • Explain results to non-technical audiences
  • Provide context and caveats
  • Recommend actions based on findings
  • Help stakeholders understand trade-offs and implications

An insight that isn’t understood or trusted is rarely acted upon.

Common Tools Used by Data Analysts

The specific tools vary, but many Data Analysts regularly work with:

  • SQL for querying and transforming data
  • Spreadsheets (e.g., Excel, Google Sheets) for quick analysis
  • BI & Visualization Tools (e.g., Power BI, Tableau, Looker)
  • Programming Languages (e.g., Python or R) for deeper analysis
  • Data Models & Semantic Layers for consistent metrics

A Data Analyst should know which tool is appropriate for a given task and should have good proficiency of the tools needed frequently.

What a Data Analyst Is Not

Understanding the boundaries of the role helps set realistic expectations.

A Data Analyst is typically not:

  • A data engineer responsible for building ingestion pipelines
  • A machine learning engineer deploying production models
  • A decision-maker replacing business judgment

However, Data Analysts often collaborate closely with these roles and may overlap in skills depending on team structure.

What the Role Looks Like Day-to-Day

On a practical level, a Data Analyst’s day might include:

  • Meeting with stakeholders to clarify requirements
  • Writing or refining SQL queries
  • Validating numbers in a dashboard
  • Investigating why a metric changed unexpectedly
  • Reviewing feedback on a report
  • Improving an existing dataset or model

The work is iterative—questions lead to answers, which often lead to better questions.

How the Role Evolves Over Time

As organizations mature, the Data Analyst role often evolves:

  • From ad-hoc reporting → standardized metrics
  • From reactive analysis → proactive insights
  • From static dashboards → self-service analytics enablement
  • From individual contributor → analytics lead or manager

Strong Data Analysts develop deep business understanding and become trusted advisors, not just report builders.

Why Data Analysts Are So Important

In an environment full of data, clarity is valuable. Data Analysts:

  • Reduce confusion by creating shared understanding
  • Help teams focus on what matters most
  • Enable faster, more confident decisions
  • Act as a bridge between data and the business

They ensure data is not just collected—but used effectively.

Final Thoughts

A Data Analyst’s job is not about charts, queries, or tools alone. It is about helping people make better decisions using data.

The best Data Analysts combine technical skills, analytical thinking, business context, and communication. When those come together, data stops being overwhelming and starts becoming actionable.

Thanks for reading and best wishes on your data journey!

Data Analysis, Data Cleaning, Data Conversions, Data Integration, Data Migration, Data Strategy

Data Conversions: Steps, Best Practices, and Considerations for Success

Introduction

Data conversions are critical undertakings in the world of IT and business, often required during system upgrades, migrations, mergers, or to meet new regulatory requirements. I have been involved in many data conversions over the years, and in this article, I am sharing information from that experience. This article provides a comprehensive guide to the stages, steps, and best practices for executing successful data conversions. This article was created from a detailed presentation I did some time back at a SQL Saturday event.

What Is Data Conversion and Why Is It Needed?

Data conversion involves transforming data from one format, system, or structure to another. Common scenarios include application upgrades, migrating to new systems, adapting to new business or regulatory requirements, and integrating data after mergers or acquisitions. For example, merging two customer databases into a new structure is a typical conversion challenge.

Stages of a Data Conversion Project

Let’s take a look at the stages of a data conversion project.

Stage 1: Big Picture, Analysis, and Feasibility

The first stage is about understanding the overall impact and feasibility of the conversion:

  • Understand the Big Picture: Identify what the conversion is about, which systems are involved, the reasons for conversion, and its importance. Assess the size, complexity, and impact on business and system processes, users, and external parties. Determine dependencies and whether the conversion can be done in phases.
  • Know Your Sources and Destinations: Profile the source data, understand its use, and identify key measurements for success. Compare source and destination systems, noting differences and existing data in the destination.
  • Feasibility – Proof of Concept: Test with the most critical or complex data to ensure the conversion will meet the new system’s needs before proceeding further.
  • Project Planning: Draft a high-level project plan and requirements document, estimate complexity and resources, assemble the team, and officially launch the project.

Stage 2: Impact, Mappings, and QA Planning

Once the conversion is likely, the focus shifts to detailed impact analysis and mapping:

  • Impact Analysis: Assess how business and system processes, reports, and users will be affected. Consider equipment and resource needs, and make a go/no-go decision.
  • Source/Destination Mapping & Data Gap Analysis: Profile the data, create detailed mappings, list included and excluded data, and address gaps where source or destination fields don’t align. Maintain legacy keys for backward compatibility.
  • QA/Verification Planning: Plan for thorough testing, comparing aggregates and detailed records between source and destination, and involve both IT and business teams in verification.

Stage 3: Project Execution, Development, and QA

With the project moving forward, detailed planning, development and validation, and user involvement become the priority:

  • Detailed Project Planning: Refine requirements, assign tasks, and ensure all parties are aligned. Communication is key.
  • Development: Set up environments, develop conversion scripts and programs, determine order of processing, build in logging, and ensure processes can be restarted if interrupted. Optimize for performance and parallel processing where possible.
  • Testing and Verification: Test repeatedly, verify data integrity and functionality, and involve all relevant teams. Business users should provide final sign-off.
  • Other Considerations: Train users, run old and new systems in parallel, set a firm cut-off for source updates, consider archiving, determine if any SLAs needed to be adjusted, and ensure compliance with regulations.

Stage 4: Execution and Post-Conversion Tasks

The final stage is about production execution and transition:

  • Schedule and Execute: Stick to the schedule, monitor progress, keep stakeholders informed, lock out users where necessary, and back up data before running conversion processes.
  • Post-Conversion: Run post-conversion scripts, allow limited access for verification, and where applicable, provide close monitoring and support as the new system goes live.

Best Practices and Lessons Learned

  • Involve All Stakeholders Early: Early engagement ensures smoother execution and better outcomes.
  • Analyze and Plan Thoroughly: A well-thought-out plan is the foundation of a successful conversion.
  • Develop Smartly and Test Vigorously: Build robust, traceable processes and test extensively.
  • Communicate Throughout: Keep all team members and stakeholders informed at every stage.
  • Pay Attention to Details: Watch out for tricky data types like DATETIME and time zones, and never underestimate the effort required.

Conclusion

Data conversions are complex, multi-stage projects that require careful planning, execution, and communication. By following the structured approach and best practices outlined above, organizations can minimize risks and ensure successful outcomes.

Thanks for reading!

AI, AI Strategy, Artificial Intelligence (AI), Data Governance, Data Science, Generative AI, Internet of Things (IoT), Large Language Models (LLMs), Machine Learning (ML), Natural Language Processing (NLP), Predictive Analytics

AI in Supply Chain Management: Transforming Logistics, Planning, and Execution
“AI in …” series

Artificial Intelligence (AI) is reshaping how supply chains operate across industries—making them smarter, more responsive, and more resilient. From demand forecasting to logistics optimization and predictive maintenance, AI helps companies navigate growing complexity and disruption in global supply networks.

What is AI in Supply Chain Management?

AI in Supply Chain Management (SCM) refers to using intelligent algorithms, machine learning, data analytics, and automation technologies to improve visibility, accuracy, and decision-making across supply chain functions. This includes planning, procurement, production, logistics, inventory, and customer fulfillment. AI processes massive and diverse datasets—historical sales, weather, social trends, sensor data, transportation feeds—to find patterns and make predictions that are faster and more accurate than traditional methods.

The current landscape sees widespread adoption from startups to global corporations. Leaders like Amazon, Walmart, Unilever, and PepsiCo all integrate AI across their supply chain operations to gain competitive edge and operational excellence.

How AI is Applied in Supply Chain Management

Here are some of the most impactful AI use cases in supply chain operations:

1. Predictive Demand Forecasting

AI models forecast demand by analyzing sales history, promotions, weather, and even social media trends. This helps reduce stockouts and excess inventory.

Examples:

  • Walmart uses machine learning to forecast store-level demand, reducing out-of-stock cases and optimizing orders.
  • Coca-Cola leverages real-time data for regional forecasting, improving production alignment with customer needs.

2. AI-Driven Inventory Optimization

AI recommends how much inventory to hold and where to place it, reducing carrying costs and minimizing waste.

Example: Fast-moving retail and e-commerce players use inventory tools that dynamically adjust stock levels based on demand and lead times.

3. Real-Time Logistics & Route Optimization

Machine learning and optimization algorithms analyze traffic, weather, vehicle capacity, and delivery windows to identify the most efficient routes.

Example: DHL improved delivery speed by about 15% and lowered fuel costs through AI-powered logistics planning.

News Insight: Walmart’s high-tech automated distribution centers use AI to optimize palletization, delivery routes, and inventory distribution—reducing waste and improving precision in grocery logistics.

4. Predictive Maintenance

AI monitors sensor data from equipment to predict failures before they occur, reducing downtime and repair costs.

5. Supplier Management and Risk Assessment

AI analyzes supplier performance, financial health, compliance, and external signals to score risks and recommend actions.

Example: Unilever uses AI platforms (like Scoutbee) to vet suppliers and proactively manage risk.

6. Warehouse Automation & Robotics

AI coordinates robotic systems and automation to speed picking, packing, and inventory movement—boosting throughput and accuracy.

Benefits of AI in Supply Chain Management

AI delivers measurable improvements in efficiency, accuracy, and responsiveness:

  • Improved Forecasting Accuracy – Reduces stockouts and overstock scenarios.
  • Lower Operational Costs – Through optimized routing, labor planning, and inventory.
  • Faster Decision-Making – Real-time analytics and automated recommendations.
  • Enhanced Resilience – Proactively anticipating disruptions like weather or supplier issues.
  • Better Customer Experience – Higher on-time delivery rates, dynamic fulfillment options.

Challenges to Adopting AI in Supply Chain Management

Implementing AI is not without obstacles:

  • Data Quality & Integration: AI is only as good as the data it consumes. Siloed or inconsistent data hampers performance.
  • Talent Gaps: Skilled data scientists and AI engineers are in high demand.
  • Change Management: Resistance from stakeholders slowing adoption of new workflows.
  • Cost and Complexity: Initial investment in technology and infrastructure can be high.

Tools, Technologies & AI Methods

Several platforms and technologies power AI in supply chains:

Major Platforms

  • IBM Watson Supply Chain & Sterling Suite: AI analytics, visibility, and risk modeling.
  • SAP Integrated Business Planning (IBP): Demand sensing and collaborative planning.
  • Oracle SCM Cloud: End-to-end planning, procurement, and analytics.
  • Microsoft Dynamics 365 SCM: IoT integration, machine learning, generative AI (Copilot).
  • Blue Yonder: Forecasting, replenishment, and logistics AI solutions.
  • Kinaxis RapidResponse: Real-time scenario planning with AI agents.
  • Llamasoft (Coupa): Digital twin design and optimization tools.

Core AI Technologies

  • Machine Learning & Predictive Analytics: Patterns and forecasts from historical and real-time data.
  • Natural Language Processing (NLP): Supplier profiling, contract analysis, and unstructured data insights.
  • Robotics & Computer Vision: Warehouse automation and quality inspection.
  • Generative AI & Agents: Emerging tools for planning assistance and decision support.
  • IoT Integration: Live tracking of equipment, shipments, and environmental conditions.

How Companies Should Implement AI in Supply Chain Management

To successfully adopt AI, companies should follow these steps:

1. Establish a Strong Data Foundation

  • Centralize data from ERP, WMS, TMS, CRM, IoT sensors, and external feeds.
  • Ensure clean, standardized, and time-aligned data for training reliable models.

2. Start With High-Value Use Cases

Focus on demand forecasting, inventory optimization, or risk prediction before broader automation.

3. Evaluate Tools & Build Skills

Select platforms aligned with your scale—whether enterprise tools like SAP IBP or modular solutions like Kinaxis. Invest in upskilling teams or partner with implementation specialists.

4. Pilot and Scale

Run short pilots to validate ROI before organization-wide rollout. Continuously monitor performance and refine models with updated data.

5. Maintain Human Oversight

AI should augment, not replace, human decision-making—especially for strategic planning and exceptions handling.

The Future of AI in Supply Chain Management

AI adoption will deepen with advances in generative AI, autonomous decision agents, digital twins, and real-time adaptive networks. Supply chains are expected to become:

  • More Autonomous: Systems that self-adjust plans based on changing conditions.
  • Transparent & Traceable: End-to-end visibility from raw materials to customers.
  • Sustainable: AI optimizing for carbon footprints and ethical sourcing.
  • Resilient: Predicting and adapting to disruptions from geopolitical or climate shocks.

Emerging startups like Treefera are even using AI with satellite and environmental data to enhance transparency in early supply chain stages.

Conclusion

AI is no longer a niche technology for supply chains—it’s a strategic necessity. Companies that harness AI thoughtfully can expect faster decision cycles, lower costs, smarter demand planning, and stronger resilience against disruption. By building a solid data foundation and aligning AI to business challenges, organizations can unlock transformational benefits and remain competitive in an increasingly dynamic global market.

Data Integration, Data Modeling, Power BI

How to turn off “Autodetect New Relationships” in Power BI (and why you may consider doing it)

Power BI includes a feature called Autodetect new relationships that automatically creates relationships between tables when new data is loaded into a model. While convenient for simple datasets, this setting can cause unexpected behavior in more advanced data models.

How to Turn Off Autodetect New Relationships

You can disable this feature directly from Power BI Desktop:

  1. Open Power BI Desktop
  2. Go to File → Options and settings → Options
  3. In the left pane, under CURRENT FILE, select Data Load
  4. Then in the page’s main area, under the Relationships section, uncheck:
    • Autodetect new relationships after data is loaded
  5. Click OK

Note that you may need to refresh your model for the change to fully take effect on newly loaded data.

Why You May Want to Disable This Feature

Turning off automatic relationship detection is considered a best practice for many professional Power BI models, especially as complexity increases.

Key reasons to disable it include:

  • Prevent unintended relationships
    This is the main reason. Power BI may create relationships you did not intend, based solely on matching column names or data types. Automatically generated relationships can introduce ambiguity and inactive relationships, leading to incorrect DAX results or performance issues.
  • Maintain full control of the data model, especially when the model needs to be carefully designed because of complexity or other reasons
    Manually creating relationships ensures they follow your star schema design and business logic. Complex models with role-playing dimensions, bridge tables, or composite models benefit from intentional, not automatic, relationships.
  • Improve model reliability and maintainability
    Explicit relationships make your model easier to understand, document, and troubleshoot.

When Autodetect Can Still Be Useful

Autodetect is a useful feature in some cases. For quick prototypes, small datasets, or ad-hoc analysis, automatic relationship detection can save time. However, once a model moves toward production or supports business-critical reporting, manual control is strongly recommended.

Thanks for reading!

Analytics, Business Intelligence, Data Education & Training, Microsoft Certification, PL-300, Power BI

Exam Prep Hub for PL-300: Microsoft Power BI Data Analyst

Welcome to the one-stop hub with information for preparing for the PL-300: Microsoft Power BI Data Analyst certification exam. Upon successful completion of the exam, you earn the Microsoft Certified: Power BI Data Analyst Associate certification.

This hub provides information directly here (topic-by-topic), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the PL-300 exam and making use of as many of the resources available as possible.

Skills tested at a glance (as specified in the official study guide)

  • Prepare the data (25–30%)
  • Model the data (25–30%)
  • Visualize and analyze the data (25–30%)
  • Manage and secure Power BI (15–20%)
Click on each hyperlinked topic below to go to the preparation content and practice questions for that topic. And there are also 2 practice exams provided below.

Prepare the data (25–30%)

Get or connect to data

Profile and clean the data

Transform and load the data

Model the data (25–30%)

Design and implement a data model

Create model calculations by using DAX

Optimize model performance

Visualize and analyze the data (25–30%)

Create reports

Enhance reports for usability and storytelling

Identify patterns and trends

Manage and secure Power BI (15–20%)

Create and manage workspaces and assets

Secure and govern Power BI items

Practice Exams

We have provided 2 practice exams (with answer keys) to help you prepare:

Important PL-300 Resources

To Do’s:

  • Schedule time to learn, study, perform labs, and do practice exams and questions
  • Schedule the exam based on when you think you will be ready; scheduling the exam gives you a target and drives you to keep working on it; but keep in mind that it can be rescheduled based on the rules of the provider.
  • Use the various resources above and below to learn
  • Take the free Microsoft Learn practice test, any other available practice tests, and do the practice questions in each section and the two practice tests available on this hub.

Good luck to you passing the PL-300: Microsoft Power BI Data Analyst certification exam and earning the Microsoft Certified: Power BI Data Analyst Associate certification!