Skip links

Enhancing Data Observability: Unveiling Excellence

Jump To Section

In the fast-paced world of business, data has emerged as a cornerstone for informed decision-making and strategic planning. To harness the full potential of data, organizations are increasingly turning towards the concept of Data Observability. This transformative practice empowers companies to not only comprehend the status of their data and data systems but also reap a multitude of business benefits. In this article, we’ll delve into the world of Data Observability and explore the numerous advantages it offers to modern businesses.

Understanding Data Observability

Data Observability can be likened to a powerful telescope for a company’s data landscape, offering a panoramic view of data pipelines, structures, and flows. It’s the quintessential capability for organizations to have a real-time grasp of the data’s health, quality, and movement within their ecosystem. With strong data observability practices in place, businesses are equipped to develop robust tools and processes, which allow them to detect data bottlenecks, prevent downtimes, and ensure consistency in their data-driven operations.

The Business Problem: Navigating Data Challenges

Before delving into the benefits of Data Observability, let’s first understand the challenges that businesses typically face:

Locating Appropriate Data Sets: The sheer volume of data available can make it challenging to identify and access the right datasets for analysis and decision-making.

Ensuring Data Reliability: Trustworthy insights depend on data accuracy. Ensuring that the data is reliable and up-to-date is crucial for making informed business choices.

Managing Data Dynamics: The ever-changing nature of data – in terms of volume, structure, and content – poses a considerable challenge for maintaining consistent data quality.

Impact of Changing Data: When the data feeding into models and algorithms changes, outcomes and predictions can also shift. Managing this variability is crucial for maintaining reliable results.

Visibility Gap: Executing complex processes like models, jobs, and SQL queries without proper visibility can lead to inefficiencies and operational blind spots.

Operational Performance: High-performance data operations are essential for productivity. Challenges in this area can hinder efficient business processes.

Cost and Budget Concerns: Unforeseen cost overruns, poor spend forecasting, and budget tracking can disrupt financial planning.

Implementing Data Observability 

Data observability can be implemented as a “rule” inside our data pipeline.
For instance, if the “freshness check” rule is triggered, then the “terminator” will wake up and halt the execution of the data pipeline, thus protecting the data and enhancing the quality and trust, which is essential for the concept of a single source of truth (SSOT).

To tackle these challenges head-on, businesses are integrating a range of tools and technologies under the umbrella of Data Observability. Some key components of this implementation include:

Insights through ML (Amazon sage maker/Azure ml/gcp vertex ai): This platform facilitates the training and deployment of machine learning models, ensuring that data-driven predictions and decisions are rooted in accurate insights.

ETL Automation (AWS Glue/ azure data factory/ GCP data flow): Designed for ETL (Extract, Transform, Load) processes, AWS Glue automates data preparation and movement, ensuring data quality and consistency.

Data Storage (AWS redshift/Azure synapse/GCP big query): A combination of storage solutions like Amazon S3, PostgreSQL, and Redshift offer scalable and secure data storage capabilities.

Output Formats (aws quicksight/Microsoft power bi/google data studio): Data can be stored in formats like CSV or Parquet, etc., optimizing storage efficiency and data processing speed.

Data Analysis Tools (csv, json, avro, parquet, orc):
Amazon Athena and Amazon QuickSight are powerful tools for querying and visualizing data, enabling efficient analysis and data-driven decision-making.


Realizing Outcomes: Use Cases from Pharma Industry

The implementation of Data Observability yields several valuable outcomes and use cases:

1. Freshness: Organizations can ensure that the data they are working with is up-to-date, reducing the risk of outdated insights driving decisions.

E.g.: Clinical trial data needs to be latest in all systems to comply with regulatory bodies reporting requirements and principal investigators will make medical decisions based on that data

Specialization and registration of every HCP needs to be up to date to make sure product sale is not impacted and to comply with non-marketing regulation off-label use

Competitor data should be latest to make better marketing decisions (Data to Infographic)

        Clinical Trail Freshness Dashboard

HCP Specialization Freshness Dashboard

Clinical Trial PhaseCountryUpdated DateCurrent Date
CountryUpdated DateCurrent Date

Phase 1India08-Sep11-Sep

Phase 1Switzerland11-Sep11-Sep

Phase 1US10-Sep11-Sep

Phase 1UK09-Sep11-Sep

Phase 1Russia08-Sep11-Sep

Phase 1Pakistan11-Sep11-Sep

Phase 1Indonesia09-Sep11-Sep

Phase 1China10-Sep11-Sep

Phase 2India11-Sep11-Sep

Phase 2Switzerland09-Sep11-Sep

Phase 2US10-Sep11-SepCompetitor Data

Phase 2UK11-Sep11-Sep
CompetitorUpdated DateCurrent Date

Phase 2Russia09-Sep11-Sep
Competitor 111-Sep11-Sep

Phase 2Pakistan10-Sep11-Sep
Competitor 211-Sep11-Sep

Phase 2Indonesia11-Sep11-Sep
Competitor 310-Sep11-Sep

Phase 2China11-Sep11-Sep
Competitor 409-Sep11-Sep

Phase 3India09-Sep11-Sep
Competitor 508-Sep11-Sep

Phase 3Switzerland10-Sep11-Sep
Competitor 611-Sep11-Sep

Phase 3US08-Sep11-Sep
Competitor 709-Sep11-Sep

Phase 3UK09-Sep11-Sep
Competitor 810-Sep11-Sep

Phase 3Russia10-Sep11-Sep
Competitor 908-Sep11-Sep

Phase 3Pakistan08-Sep11-Sep

Phase 3Indonesia09-Sep11-Sep

Phase 3China08-Sep11-Sep

Phase 4India08-Sep11-Sep

Phase 4Switzerland08-Sep11-Sep

Phase 4US11-Sep11-Sep

Phase 4UK09-Sep11-Sep

Phase 4Russia10-Sep11-Sep

Phase 4Pakistan11-Sep11-Sep

Phase 4Indonesia10-Sep11-Sep

Phase 4China09-Sep11-Sep

Reference Sample for Infographic:


2. Distribution: Data movement across various stages can be monitored, ensuring a smooth flow of information without bottlenecks.

E.g.: Clinical trial moves from one stage to another starting from recruitment to baseline condition study to treatment (Treatment provided and results)

New products/competitors coming into the market, products going off patent, recall/stoppage will have an impact on data distribution and will be captured/reported to business team accordingly.

When a product is approved to be used for multiple indications it is captured and reported.
(Data to Infographic)

Clinical Trial Distribution Dashboard

Product Distribution Dashboard

CountryStageNo of CasesAs-Of
CountryCategoryNo Of ProductsAs-Of
IndiaNew Product3211-Sep
SwitzerlandNew Product2811-Sep
USNew Product1411-Sep
UKNew Product2411-Sep
RussiaNew Product1911-Sep
PakistanNew Product1611-Sep
IndonesiaNew Product2211-Sep
ChinaNew Product3011-Sep
IndiaBaseline Condition Study89811-Sep
IndiaNew Competitor Product6811-Sep
SwitzerlandBaseline Condition Study45611-Sep
SwitzerlandNew Competitor Product5411-Sep
USBaseline Condition Study76811-Sep
USNew Competitor Product7811-Sep
UKBaseline Condition Study42011-Sep
UKNew Competitor Product2311-Sep
RussiaBaseline Condition Study98411-Sep
RussiaNew Competitor Product5611-Sep
PakistanBaseline Condition Study64211-Sep
PakistanNew Competitor Product4311-Sep
IndonesiaBaseline Condition Study50811-Sep
IndonesiaNew Competitor Product3211-Sep
ChinaBaseline Condition Study99211-Sep
ChinaNew Competitor Product4511-Sep
IndiaTreatment Started31211-Sep
IndiaOff Patent1211-Sep
SwitzerlandTreatment Started26411-Sep
SwitzerlandOff Patent1411-Sep
USTreatment Started21211-Sep
USOff Patent1111-Sep
UKTreatment Started28911-Sep
UKOff Patent811-Sep
RussiaTreatment Started22911-Sep
RussiaOff Patent1411-Sep
PakistanTreatment Started28011-Sep
PakistanOff Patent511-Sep
IndonesiaTreatment Started26611-Sep
IndonesiaOff Patent1011-Sep
ChinaTreatment Started20811-Sep
ChinaOff Patent911-Sep
IndiaTreatment Ended19811-Sep
SwitzerlandTreatment Ended16411-Sep
USTreatment Ended15211-Sep
UKTreatment Ended10811-Sep
RussiaTreatment Ended15511-Sep
PakistanTreatment Ended14311-Sep
IndonesiaTreatment Ended17611-Sep
ChinaTreatment Ended12411-Sep
IndiaResults Shared9811-Sep
IndiaMultiple indication use1211-Sep
SwitzerlandResults Shared8611-Sep
SwitzerlandMultiple indication use1911-Sep
USResults Shared10011-Sep
USMultiple indication use1611-Sep
UKResults Shared7511-Sep
UKMultiple indication use811-Sep
RussiaResults Shared3611-Sep
RussiaMultiple indication use1311-Sep
PakistanResults Shared8611-Sep
PakistanMultiple indication use511-Sep
IndonesiaResults Shared6511-Sep
IndonesiaMultiple indication use1111-Sep
ChinaResults Shared7711-Sep
ChinaMultiple indication use1711-Sep

3. Volume: Monitoring data volume helps in scaling resources effectively, accommodating fluctuations in data influx.

E.g.: High volume of clinical trial data gets captured when there is higher patient recruitment due to sudden rise in adverse events or when a new site gets activated, and patients start getting recruited in it.

Sales data volume increase gets captured when the reach gets expanded post approval from concerned authority (a new state or a country)

Increase in HCP data gets captured once our products get approval to enter new geography or the drug is approved for multiple indication
(Data to Infographic)

Clinical Trial Volume Dashboard











Sales Increase Dashboard











HCP Volume Dashboard











4. Schema: Ensuring consistency in data structures prevents conflicts and errors when processing information.

E.g.: When the Structure of vendor data is incorrect it is captured and reported before it is sent to the SDTM system for data study/analysis.

When a combination of old and new product codes for the same product gets sent, it gets captured and a Mapping of all these source product codes to one master product is enabled.

5. Lineage: Tracking the lineage of data allows businesses to understand where data comes from and how it’s transformed, aiding in data quality assurance.

E.g.: The traversal of Clinical trial – patient data from EDC (electronic data capture) to SDTM (study data tabulation model) to study report can be captured and reported.

The traversal of HCP data (First & Last Name, Specialization, Phone Number, etc.) received from multiple sources (Raw to Clean to Curated layer via MDM Match & Merge rule checks and arriving at a golden record) can be captured and reported.

Outcome (Infographic)

Lower incidence of regulatory observation by 15% – clinical trial

Improved compliance to regulation – 5% – clinical trial

Better decision ability and improved performance – 10% – commercial

Harnessing the Business Benefits: 

Implementing robust Data Observability practices offers a range of business benefits that foster growth and efficiency:

Improves Data Accuracy: By having real-time insights into data health, organizations can identify and rectify inaccuracies before they affect decisions.

Timely Data Delivery: With observability, data downtimes are minimized, ensuring that insights are available when needed, promoting timely and informed decisions.

Early Issue Detection: Observability allows businesses to spot potential data concerns in their infancy, preventing them from escalating into more significant problems.

Prevents Data Downtime: By identifying bottlenecks and performance issues in data pipelines, observability minimizes disruptions, leading to uninterrupted operations.

Cost Optimization: Through constant monitoring, businesses can optimize resources effectively, preventing unnecessary costs and improving budget forecasting.

Conclusion: Illuminating the Path Forward

Data observability not only provides health insights (in the form of predictive analytics and advanced analytics) but helps in collecting metadata for periodically updating the data catalogs. Capturing end-to-end data lineage is the key to understanding your single source of truth.

In conclusion, Data Observability emerges as a critical enabler for businesses seeking to thrive in a data-centric landscape. By addressing data challenges at their core, it empowers organizations to make accurate decisions, foster efficiency, and propel growth in an increasingly competitive environment. It’s not just about observing data; it’s about observing success.



Latest Reads


Suggested Reading

Ready to Unlock Yours Enterprise's Full Potential?

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes