Harnessing life sciences data requires moving beyond silos. Model real-world relationships between entities like genes, diseases, and drugs, enabling context-aware search, hypothesis generation, and precision medicine with the help of a knowledge graph powered by a data lake. Transform fragmented data into a unified, computable network.
Resilient and scalable
Smart BOTs are decentralized event-driven workflow engines, which can scale up based on workload
The core feature of BOTs is that they are asynchronous and able to run numerous tasks parallelly. Earlier, identifying a failed job and rerunning it was a nightmare for data ingestion and curation. Due to manual efforts involved in recurring the failed jobs, SLA breaches were quite frequent and tedious. However, BOTs are empowered to craft high-performance variants of themselves. They are the medium, mechanism, and platform for getting greater value from data analytics and augmented data preparation.
Over 80% of the world’s data is unstructured. Terabytes of data are being generated every minute & fast processing of this volume of data is becoming the need of the hour. More and more firms are now waking up to the reality of big data.
Modak’s unique and proprietary meta-programming approach ensures faster and effective implementation of governed data lakes. At each layer of the governed data lake, Modak ensures integrity and security concerns are handled effectively.
Data Governance helps in maintaining the integrity, availability, and security of data and information across business functions. Data Governance ensures high quality throughout the life cycle of the data. Consistent and trustworthy data ensures business analytics, AI models, and business value have the necessary checks and balances to provide consistency and accuracy.
Modak’s Governed Data Lake and metadata catalog solutions discover and secure the data which comply with the industry standards and best practices. Business, IT, and analytical users can easily evaluate the data quality and manage metadata. Ensuring users stay aligned with the terminologies and definitions. Data users can manage external data sources and provide unified and transformed data to external applications.
Data migration from on-premise data sources to the Cloud provides enterprises the opportunity to not only gain the benefits of Cloud operating costs and scalability, but if done correctly, changes the way Enterprises manage their data in the Cloud to increase value from analytics. Further, migration is not a one-time activity, for many reasons data from systems of record will remain on-premise, continuous data migration processes to the cloud to create “data fabrics” and “data lakes” are required.
At Modak we have proven data migration processes, software, and skills to help you in your journey to the cloud regardless of the data types, frequency, and volumes.
We integrate innovative processes, tools, and solutions to ensure that your data migration is carried out quickly and effectively.
We use our industrialized data migration factory to help you combine data migration with an effective archival strategy. This ensures that new systems are commissioned, and old systems are decommissioned more quickly.
We execute these data migrations using our Global Delivery Model approach where both, onsite and offsite teams collaborate to provide top value to the customers.
While each data migration method is exclusive and unique, we tend to perceive what has worked for others. This expertise will help in running your project smoothly. Modak provides you with the finest professionals possessing the precise skill sets required to complete the project successfully.
Modak’s Data Fingerprinting provides an index value that differentiates between other record values; this value is called as fuzzy value and the index is called as fuzzy index. These are called fingerprints of the data which are unique, these values are used to match similar leaves of a branch.
In this process, the comparison of column values is done across different tables and a hash code against the column is generated. Irrespective of what the column name is labeled across different tables, if the column shares the same data, then a score will be generated from 0 to 1 as to how much of data is matched and then the mapping of the data will be done and the data will be merged. This score will be generated using an algorithm.
For example, if there are different tables where the column is labeled as “col”, “column”, “col1”, but the data which is shared in the columns are the same, then the data is checked, a hash will be generated against that column, a score between 0 to 1 is generated and then the mapping of the data takes place by merging the columns.
Modak’s Managed Services addresses the complexities around Data Lake and enables efficient management of data. It prevents data from turning into a data swamp. Efficient data management and maintenance help in improving the performance of the Big Data environment to a great extent.
Modak’s Managed Services, bundled with proprietary real-time proactive monitoring tools, are integrated with native managers like Cloudera Manager for streamlined services.
We have a highly experienced, and certified DataOps team for Cloudera, capable of managing clusters with 500+ nodes containing Petabytes of data. This significantly eases our client’s journey with Hadoop Systems – whether it is Cloudera, Hortonworks, or MapR.
This has led to the use of processes that are well defined and tools that are best-in-class for effectively managing, maintaining, and monitoring big data platforms.
Data Ingestion is not just data acquisition, It’s about prepping the data for curation
Data Lakes require huge amounts of data to be processed, in some cases in Petabytes, requiring thousands of pipelines to be created. Traditional ETL-based tools are time-consuming and expensive to use. Modak’s unique & proprietary technology dramatically reduces the time, complexity, and risk to automatically generate data pipelines at scale, reducing the time to create a new pipeline from hours/days to less than a minute.
Modak uses a metaprogramming approach to generate the code for ingestion pipelines, using the metadata captured by Data Spiders.
Data Unification involves the process of ingesting, transforming, mapping, and deduplicating, and exporting the data from multiple data sources. Two software tools are commonly used by IT teams when dealing with transactional data sets to feed into data warehouses: ETL (Extract Transform and Load) software and MDM (Master Data Management) software.
The problem of unifying 3 different data standards with 10 records each doesn’t require a tool. Instead, the user can utilize a whiteboard and a pen to solve the issue. When it comes to five different data standards with 1 lakh rows, the traditional ETL approach can be used. But, if the problem is to solve tens or hundreds of separate data sources with 5000+ mapping rules, 3000+ variations in column names, and billions of records in each source, then the traditional ETL solution is not feasible.
Modak’s advanced capabilities in meta programming and fingerprinting techniques change the paradigm with machine learning techniques, which replace the traditional approach.
Through extensive automation, Modak leverages big data technologies and cloud infrastructure on a massive scale that ensures reduction in time, cost, and risk for large scale data lake projects.
Modak Nabu™ uses “NLP POS” recognition and named entity extractions to annotate the unstructured data.
The machine identifies and applies the last applied rules bypassing data classification and user review.
Nowadays, faster decisions are the need of the hour. It is difficult to make business decisions based on the data at hand. The representation of the data helps business individuals to understand the data and to make quick decisions. At Modak, we generate dashboards that are visually appealing and easy to understand. Business users can tweak the necessary data from the visuals to customize, according to their requirements.
Data Visualization is the graphical representation of data using charts, graphs, and maps. It helps in understanding the hidden patterns in data.
The process of reading and analyzing the data and coming up with business insights may take a lot of time in making business decisions. Instead, visualizing the data and generating business insights from the visuals makes it easier.
DataOps is a data enablement approach designed for rapid, reliable and repeatable delivery of ready-made data with fully operational analytics. Modak’s DataOps majorly consists of faster data enablement by using Modak’s approach of Data Preparation. Agile and smarter data engineering can handle large-scale data rapidly and efficiently is key to success.
As opposed to the traditional rigid schema model, where each use case must adapt to the ways of the model, DataOps provides self-service data analysis and data science solutions. Data consumers can analyze data and come up with new use cases for data-driven decisions.
The approach provides production-ready data and empowers consumers to become creative in effectively using the enterprise data without having to deal with complexities, such as finding data, quality, access, data integrity, difficulties with modern data management, and poorly-defined data.
DataOps aims to defeat data chaos by turning raw data into valuable and meaningful information. It brings the ability to infer relations among semantic objects across data silos and grants the capability to discover, analyze, and act upon data with ease.
Data consumers can use the robust search capabilities with the help of an extensive collection of metadata, data tagging, and data lineage driven by DataOps.
Modak’s Metaprogramming software is a technique that runs blocks of code on billions of rows and records at an instance. Modak’s Metaprogramming software is capable enough to read, generate, analyze or transform other programs, and even modify itself while running.
According to Gartner, more than 70% of big data projects have failed due to a large amount of time spent on data preparation and curation. Most businesses spend the maximum amount of time preparing data to generate insights using machine learning and automation. By the time the data reaches the visualization phase, either the data or the technology becomes outdated.
At Modak, our metaprogramming approach focuses mainly on the data preparation phase. The metaprogramming approach drastically accelerates data preparation and curation processes. Metadata is essential for data preparation in any big data platform. Metadata contains key information about the underlying data. Modak’s Nabu™ metaprogramming approach leverages metadata to ingest, curate, and unify data sets. Metaprogramming generates code through metadata, which Modak Nabu ™ captures from source and destination, and saves into technical, operational, and business metadata catalogs.
One of the benefits of metaprogramming is the increase in the productivity of developers once they get past the convention and configuration phases. In metaprogramming, metadata is used in data ingestion, cascading templates, and creating entities that are helpful for data visualization. Through the meta-programming approach, we follow a complete automated end-to-end process right from the source to ingestion and curation, so that users can utilize optimized data for their process.
From the need to deliver quality data quickly and continuously, emerges DataOps, an approach that promises agile data operations for analytics. Speed, agility, automation, and quality are what it aims to achieve the highest degree. With the exponential increase in data volumes at enterprises in recent years, there has been an ever-growing need to leverage data and streamline it faster for the decision-making process. For enterprises to adapt to this new normal and to become data-driven, the teams that consume and produce data must collaborate effectively and use data at each step of the process of making every business decision, regardless of whether the decision is big or small. To achieve robust and rapid insights, there should be a continuous and real-time delivery of data for analytics. A faster and agile approach for the delivery of analytics-ready data requires accelerated data pipelines that can ingest, test and deploy data rapidly and can handle huge volumes of data quickly and continuously. DataOps is a principles-based practice that aims to achieve faster delivery of reliable, self-service data. The approach needs continuous monitoring of inputs, outputs, and business logic. Speed, agility, metadata, automation, and self-service culture are some of the building blocks of DataOps.
Self-service data is a form of Business Intelligence (BI), in which line-of-business professionals are enabled and encouraged to perform queries and generate reports in close collaboration with the data analytics team. When business users are empowered to explore data and test their hypotheses without much IT help, the practice naturally internalizes data in the decision-making process. Business users can become innovative and propose new use cases for analytics. New analytics can be created quickly with the proposed use cases and businesses can see value in data analytics projects. This DataOps practice can quickly lead to incredible agility among data teams within organizations. So, this shared mindset is important. However, for all this to be practical, the underlying data engineering process should be robust and agile enough to provide analytics-ready data quickly and continuously to its data consumers.
Such fast and continuous delivery of quality data can be achieved with the support of metadata. Collecting extensive metadata is the key practice of DataOps. Maintaining consistency in metadata and capturing schema drifts is crucial. Metadata gives information about data. Once the collection process begins, it empowers data engineers to automate data processes and the implementation of thousands of test cases in data pipelines. Continuous automated testing will improve data quality, and thereby trust in data analytics. The collection of descriptive, administrative, and structural metadata would give us the essential information required to implement automation.
DataOps is not feasible without automation. Highly automated and augmented data pipelines will only deliver faster data enablement. As data pipelines grow in number and size, the organizations need to set some standards to govern data at various stages in the pipelines. Standardization and repeatability are the core components of automation. The organization that implements automation is more impregnable to schema drifts and changes in data.
Automated continuous testing is essential in building trust in data. Thousands of test cases can be generated automatically for data pipelines and can be used to test data continuously. The tests are simple and addictive. Whenever a change is made to data pipelines, test cases are created in DataOps. These tests are the early warning indicators of data quality issues. As the complexity of data pipelines rises, the interconnections in the data elements also become complex and the pipelines are prone to more errors. Automated continuous testing can help boost confidence in data. Further, statistical process controls to ensure continuous monitoring of the data pipelines by analyzing the output data. Any variations in data outputs can be identified, studied and appropriate action can be taken to resolve the issues. All these practices of DataOps, if applied to the fullest, can reduce cycle time drastically allowing the business users to dive deep into the data without any waiting time. It also encourages a collaborative working environment and promotes agility.
Self-Service and Collaboration
Metadata is the bedrock
Automation to the highest degree
Building trust in data
As data and AI move to the center of enterprise value creation, legacy systems aren’t just slowing data teams—they’re blocking AI at scale. Still relying on Hadoop? The clock is ticking on your data + AI potential. Migration to Databricks is the one imperative that enables your enterprise to operationalize AI, accelerate innovation, and unlock real-time intelligence.
For over a decade, Hadoop provided a viable framework for distributed storage and compute at scale. But for today’s AI-native organizations, its architecture has become a bottleneck. Rigid schema enforcement, batch-centric processing, tightly coupled storage-compute, and escalating ops overhead have made it increasingly infeasible to sustain innovation velocity.
Hadoop’s inherent limitations—manual tuning, poor elasticity, lack of built-in ML tooling, and costly maintenance cycles—are now amplified in environments where operational SLAs are measured in minutes, not hours. The delta between what business teams require and what Hadoop platforms can deliver has widened into a systemic misalignment between infrastructure and insight.
Across industry verticals, platform teams are migrating Hadoop workloads to Lakehouse architectures—specifically, the Databricks Lakehouse Platform—not just to cut cost but to re-architect for elasticity, interoperability, and AI scalability.
This blog outlines the hidden value your organization can capture by migrating to Databricks—switching from a legacy burden into a growth catalyst.
The most visible rationale for Hadoop migration—licensing costs—barely scratch the surface. The real costs are embedded across operations. The case for migrating to Databricks is driven by four core strategic considerations:
At its core, the Databricks Lakehouse is not a cloud-hosted replica of Hadoop—it is an architectural reset. The design principles are clear: This is not a lift-and-shift model. It’s a decoupled, unified data and ML architecture designed for governed collaboration and operational intelligence.
Exhibit 1: Hadoop to Databricks component map
As more companies migrated to modern cloud data and AI platforms, Hadoop providers have raised licensing costs to make up for their losses, which is only accelerating the migration. Organizations tend to focus on the comparative costs of licensing, and Hadoop’s subscription fees alone make a compelling case to migrate. The deeper truth is platform migrations are less about feature parity and more about securing the strategic foundation for long-term value creation. To get a true sense of what Hadoop is costing your organization, you have to step back. From a benchmark of 10 Databricks customers, it was found that licensing accounts for less than 15% of the total cost — it’s the tip of the iceberg. The other costs are made up of the following:
Databricks is priced based on consumption — you only pay for what you use. But Databricks is a more economical solution in other ways too:
From a platform engineering perspective, Databricks eliminates the redundant glue code, handoffs, and orchestration complexity typical of Hadoop-based stacks. Through a unified development experience across SQL, Python, Scala, and R—backed by interactive notebooks and version-controlled jobs—teams converge around a single interface. Key productivity enablers: This unified ecosystem drives 10x iteration speed for many data teams, especially in organizations migrating from custom Spark-on-YARN or Hive-on-HDFS pipelines.
With Databricks, customers are able to move beyond the limitations of Hadoop and finally address business-critical use cases. These organizations find that the value unlocked by a modern cloud-native data and AI platform far exceeds the cost of migration—driven by its ability to support more advanced use cases, at greater scale, and at significantly lower cost. Real-time fraud detection has shifted from reactive forensic analysis to continuous prevention, enabled by real-time telemetry from DNS and transactional logs. Databricks’ ability to process and score threats dynamically gives security teams the lead time to contain breaches before they escalate, reducing both financial loss and reputational risk. Customer churn and lifetime value modeling have also evolved. Rather than relying on monthly refreshes of static dashboards, organizations can now operationalize streaming inputs—usage patterns, support interactions, product telemetry—to proactively identify at-risk segments and optimize retention interventions. Marketing and finance functions gain a shared view of the customer that enables precision across both budget allocation and forecast planning. In ESG compliance and sustainability reporting, enterprises leverage Databricks to integrate real-time geospatial feeds with regulatory logic. This allows organizations to not only track their carbon and environmental footprint more effectively but to model alternative scenarios and improve operational sustainability strategies in-flight. For healthcare and life sciences, Databricks enables clinical outcome forecasting using multimodal data integration. Structured EHR data is joined with imaging diagnostics and genomic sequences, and processed in parallel using GPU acceleration. The result is faster risk stratification, more personalized treatment recommendations, and lower latency between clinical events and insight. In digital advertising and brand management, the platform’s ability to support petabyte-scale processing allows marketing teams to move beyond post-campaign reports. Real-time attribution, budget optimization, and audience segmentation now happen continuously, based on actual engagement streams across channels. The implication: greater ROI per campaign cycle and more agile go-to-market execution.
Hadoop workloads are rarely clean. Over time, they evolve into fragmented layers of ETL pipelines, interdependent Hive jobs, and fragile orchestration scripts. The result is deeply entangled systems with low observability, undocumented logic, and high change risk. For platform leaders, this creates a dilemma: how to migrate without replicating technical debt—or triggering regression in business-critical workflows. Modak brings execution certainty to Hadoop-to-Databricks migrations—combining automation, architectural rigor, and enterprise alignment. Through an MDP that enables enterprises to automate data ingestion, profiling and curation at petabyte scale—Nabu—and a KPI-aligned delivery model, Modak enables enterprises to execute large-scale Hadoop-to-Databricks migrations with architectural discipline, embedded governance, and reduced time-to-value. 2. Production-Ready Spark Pipelines—Generated, Not Rewritten 3. Embedded Cost Controls and Enterprise-Grade Observability
Exhibit 2: Value impact of direct migration
For infrastructure leaders, this transition is more than platform modernization—it’s the creation of a scalable, collaborative, and governed foundation for enterprise-wide data activation. The Lakehouse isn’t just a Hadoop successor. It’s the convergence point of performance, trust, and AI readiness. For every platform team weighed down by infrastructure complexity and unmet SLAs, the message is clear: Hadoop served its purpose. But in a cloud-native, AI-led landscape, it’s time to architect what comes next. Run a no-risk discovery engagement with Modak and receive a blueprint that quantifies technical feasibility and business value to unlock your AI advantage.
Hidden Costs of Staying on Hadoop
1. Infrastructure:
2. Cost of Ownership:
CAPEX vs. OPEX: Pay Only for What Is Used
3. Raising Productivity
4. Business-Impacting Use Cases
Architecting the Exit: Don’t Recreate Hadoop in the Cloud
Closing the Gap Between Ambition and Architecture
The digital transformation has led to a massive surge in both the quantity and diversity of available data. This represents an outstanding opportunity for different organizations for whom data has been an integral part of their service and product portfolio. However, since we rely on AI to make sense of big and complicated datasets, one important aspect of modern data management is getting renewed attention: Data Catalog. Firms with effective data catalog utilization witness awesome changes in the quality and speed of data analysis and in the interest and engagement of people who want to perform data analysis.
According to the Aberdeen’s research, firms deal with data environments that are developing in excess of 30% every year, some much better than that. Data catalog help data teams to locate, comprehend, and implement data more effectively by organizing data from different sources on a centralized platform
In this data-driven age, streamlined data management is not just an option- it’s a necessity. Efficient data cataloging and metadata management enable businesses increase operational efficacy, comply with strict regulations, and get actionable insights from their data.
Data cataloging is the systematic organization of data into a searchable repository, much like books in a library. This system allows businesses to efficiently locate, understand, and utilize their data assets.
“A data catalog creates and maintains an inventory of data assets through the discovery, description, and organization of distributed datasets. The data catalog provides context to enable data stewards, data/business analysts, data engineers, data scientists and other lines of business (LOB) data consumers to find and understand relevant datasets for the purpose of extracting business value. Modern machine-learning-augmented data catalogs automate various tedious tasks involved in data cataloging, including metadata discovery, ingestion, translation, enrichment and the creation of semantic relationships between metadata. These next-generation data catalogs can, therefore, propel enterprise metadata management projects by allowing business users to participate in understanding, enriching, and using metadata to inform and further their data and analytics initiatives.’’
– Gartner, Augmented Data Catalogs 2019. (Access for Gartner subscribers only)
Now we are clear about data catalogs- data management, searching, data inventory, and data evaluation- but all rely on the core potential to offer a collection of metadata.
Ideally, metadata is the data that offers proper information about other data. We can say that metadata is “data about data”. It has markers or labels that describe data, making it seamless to understand, identify, organize, and use. Metadata can be implemented with different data formats, utilizing images, documents, databases, videos, and more.
In addition to the significance of data cataloging and metadata, data quality plays an important role in data management. Data quality efforts can be improved greatly by properly cataloging your data. When metadata gives context and structure, it becomes easier to recognize redundancies, inconsistencies, or gaps in data, permitting businesses to enhance their data quality initiatives. Data cataloging and quality advancements ensure that firms not only understand their data better but also trust and implement it more efficiently by working hand-in-hand.
Metadata management involves handling data that describes other data, providing essential context about the data’s content and purpose. It acts like a quick reference guide, enabling users to understand the specifics of data without delving into the details.
Metadata acts as the cornerstone of data management strategy. It offers structure, context, and insightful meaning to raw data, helping systems and users to search, understand, and use information very efficiently. Previously, metadata was normally utilized to index and retrieve data in databases and file systems. However, with the advancement of machine learning and artificial intelligence, the role of metadata has emerged effectively.
One of the main challenges that enterprises face is maintaining the consistency and accuracy of metadata over time, specifically as data evolves. Traditionally, data stewards were responsible for managing and updating metadata manually, a procedure that was both prone to errors and labor-intensive. This results in inefficiencies, specifically in large-scale operations where data complexity is greater.
With the emergence of AI-driven cataloging strategies, these difficulties are being fixed more efficiently. ML algorithms can generate, extract, and enrich metadata automatically, reducing the manual work required to maintain data catalogs. This helps businesses to scale their data operations, ensure regular updates, and improve the quality of metadata management. AI helps automate processes like metadata classification, data tagging, and even the identification of relationships between datasets, minimizing the requirement of extensive manual intervention
Data cataloging is the process of creating an organized inventory of data assets within an enterprise. It encompasses documenting metadata including data resources, relationships, formats, and usage regulations, in a central repository. For data assets, data catalogs act as a single source of truth, offering users an inclusive view of available data and its related metadata.
Previously, utilizing data cataloging has been an issue for firms because of the complications of handling huge data spread across several systems. Manually keeping metadata updated by data stewards was mostly prone to human error and time-consuming. Furthermore, fragmented data made it challenging to achieve true interoperability, resulting in inefficiencies and incomplete insights.
However, now AI is revolutionizing how data cataloging is used, mitigating the dependency on manual procedures. With the emergence of AI and automation, firms can now manage data at scale, generate metadata automatically, and decrease the requirement for frequent human intervention. This move not only ensures that data is updated and standardized continuously but also boosts the accuracy and speed of data discovery significantly.
One of the vital advantages of data cataloging is better data discoverability. In big enterprises, data is scattered across many databases, systems, and departments. This fragmentation makes it difficult for users to identify the data they require, resulting in inefficiencies and missed opportunities. A well-curated data catalog addresses this problem by offering a searchable data index of data assets, complete with comprehensive metadata that describes every dataset’s origin, content, and relevance. Not only does this make it effortless for users to identify the data they require but also helps AI systems to access and process data more effectively.
Furthermore, data cataloging improves data compliance and governance. In today’s environment, enterprises must ensure that their data practices comply with numerous rules and regulations. Data catalogs enable enterprises to maintain prominence and control over their data assets, helping them to track data lineage, enforce data government policies, and monitor data usage. Specifically, this level of oversight is significant in different AI applications, where the capability for bias and ethical concerns is paramount. Enterprises can ensure that their AI systems operate ethically and transparently by cataloging metadata and documenting data resources.
As AI is evolving continuously, it’s changing the way metadata is handled. Usually, metadata management is a time-consuming process, need data stewards to document accurately and update metadata for every dataset. However, with AI, organizations are now able to streamline this process, automating much of the metadata generation and management.
One of the most important developments in this area is the implementation of AI to generate and enrich metadata automatically. This has led to a shift in how organizations scale their data management capabilities. The algorithms of machine learning can analyze different datasets to extract relevant metadata like relationships, data types, and strategies. This not only reduces the burden on data stewards but also ensures that metadata is updated continuously as new data is ingested. Furthermore, artificial intelligence can be utilized to find and resolve metadata inconsistencies like wrong or missing information, further improving the reliability and accuracy of data catalogs.
Enterprises can scale their data operations while ensuring that metadata remains actionable and correct across multiple datasets by adopting AI-driven automation. In metadata management, the role of AI is not only regarding efficacy but also regarding exploring the capability for real-time, scalable data cataloging that supports enterprise-wide decision-making at a larger scale than was possible previously.
Also, AI-powered metadata management helps with more advanced data discovery and analytics. For instance, natural language processing (NLP) techniques can be implemented to metadata to enable more context-aware and intuitive search abilities. Users can search for data using natural language queries, and AI algorithms can understand the objective behind the query and find the most relevant data assets. This makes data discovery easier for non-technical users and improves data catalog usability.
Another evolving trend is AI usage to improve data lineage tracking. Data lineage refers to the history of data since it moves through different systems and processes within an organization. Data lineage understanding is important to ensure data compliance, data quality, and transparency, specifically in different AI applications. AI can automate the tracking of data lineage by analyzing data flows and making detailed lineage diagrams that visualize the transformation and movement of data across the organization. Specifically, this potential is crucial in complex environments where data is processed by different stakeholders and systems.
Modak delivers innovative data cataloging solutions that empower enterprises to fully utilize the potential of their data. Our skill sets lie in generating comprehensive data catalogs that not only manage and organize metadata but also improve governance, data discoverability, and compliance across different platforms. Implementing advanced artificial intelligence and machine learning tools, Modak automates lineage tracking, metadata generation, and data classification, making it effortless for businesses to maintain data integrity and quality. With our deep understanding of AI-driven analytics and cloud-native technologies, we help firms optimize their data management approaches, ensuring that metadata becomes a very robust enabler of business insights and operational efficacy.
As we look into the future, in AI-driven enterprises, the role of data cataloging and metadata will only grow. Evolving technologies like metadata generation, advanced search abilities, and automated data lineage tracking are set to transform the way firms use and manage their data assets. These innovations will make metadata management more scalable, effective, and integrated with AI systems, further improving the value of data in the organization.
But there are challenges along the way to effective metadata management and data cataloging. Enterprises must invest in the correct technologies, tools, and talent to make and maintain strong data catalogs. Also, they must establish a culture of data stewardship, where metadata is considered an important component of the organization’s data strategy. Finally, enterprises must stay updated on the innovations in AI and metadata management, evolving their practices continuously to lead the curve.
On data cataloging and metadata management, the transformative power is profound, paving the way for more innovative and effective data practices. Since firms continue to create and rely on huge quantities of data, the role of AI to manage this data becomes more necessary. Adopting AI in data management is not only regarding keeping up with technology-but also it is regarding the speed of innovation and efficacy in the digital era.
Building a modern data platform is a transformative endeavour, particularly for organisations aiming to unlock the value of their data. While IT teams often focus on building a robust, scalable infrastructure, the real KPI for a successful data platform lies in its adoption by business users. Business teams, who typically sponsor these projects, prioritise seeing quick and measurable returns on investment (ROI) from their data platform, making user adoption a critical success factor. For this to happen, the platform must support both well-defined, familiar use cases and exploratory projects that help uncover new insights. In the early Proof of Concept (POC) phase, business teams often operate in what can be termed the “known-known” stage. They understand the specific data product they want to create and have clarity on the data sources required for this purpose. Developing data products with this level of clarity is generally straightforward. Because the required data sources are known, data engineers can quickly build pipelines, address data quality issues, and test the product. Once the business team validates the product, it can be easily moved to production, often using agile methods and CI/CD processes that streamline deployment. The Agile methodology, widely used in software and web development, has demonstrated its value in accelerating development cycles and enhancing product quality through iterative improvements. DataOps teams frequently try to replicate these agile principles, using them to build data products quickly when the requirements and data sets are clearly defined. For these well-understood use cases, agile development allows teams to swiftly create, test, and move data pipelines from development to production environments, giving business users faster access to valuable data insights.
However, real-world use cases often extend beyond the known-known stage. These projects tend to be more exploratory and complex, falling into an “unknown-unknown” category. Here, business users or data scientists may not know what data products they need at the outset. Instead, they require a platform where they can explore and discover data, experimenting with different data sets to surface new insights or identify patterns. For these exploratory projects, the data platform must provide access to clean, up-to-date, and well-organized data that users can readily interact with to fuel innovation and uncover hidden insights. Ensuring that the platform supports exploration requires a data engineering-heavy approach. The data engineering team must design processes that automate data preparation and leverage machine learning to handle large volumes of data and complex data transformations. Automated data preparation enables the platform to consistently ingest, clean, and organise data, making it accessible and ready for analysis. This level of automation is essential for ensuring that the platform provides a seamless experience, allowing business users to focus on discovery without the distractions of data wrangling or quality issues. The adoption of machine learning in data preparation also enhances the platform’s ability to support unknown-unknown projects. Machine learning models can assist in identifying patterns, anomalies, and relationships within the data, helping business users derive meaningful insights faster. Additionally, these models can automate tasks such as data classification, entity matching, and anomaly detection, which would otherwise be labour-intensive and time-consuming. A successful modern data platform must be designed with both structured and exploratory use cases in mind. By combining agile development practices for known data products with automated data preparation and machine learning for exploratory projects, organisations can maximise their platform’s value. This approach not only accelerates ROI but also promotes widespread adoption, transforming the data platform from a simple IT infrastructure into a powerful tool for business innovation.
The concept of data products is evolving rapidly, reflecting both the growing sophistication of data users and the increasing capabilities of modern data platforms. In the early days, data products were simpler and more predictable; they served specific business needs, and the data required to create them was clearly defined. Agile methods in DataOps enabled these well-structured, easily understood products to reach production quickly, allowing organisations to generate immediate value from their data. In these cases, business users knew precisely what data sets they needed, and the features of these data products were straightforward. However, as organisations recognise the potential of their data beyond well-defined use cases, a shift has occurred. Increasingly, data products are being created to enable exploration, allowing business users to enter a discovery phase where insights are neither obvious nor predefined. This shift has led to the creation of “second-generation” data products that emphasise flexibility, discovery, and adaptability. Automated data preparation has been a key driver in this transformation. By using automated processes to ingest, clean, and prepare data, platforms can provide business users with ready access to vast amounts of well-organized data, ideal for exploratory projects. Automated preparation unlocks opportunities for these users to dive into “unknown-unknown” use cases, where the aim is to uncover patterns or relationships that may not have been apparent before. In this scenario, the platform, data, and business goals intersect to create a powerful environment where discovery flourishes. New technologies, such as data fingerprinting, tagging, and profiling, have been crucial in enabling these exploratory products. Data fingerprinting and tagging help to surface relationships between data entities that would otherwise go unnoticed. Knowledge graphs, for example, can visually map these relationships, making it easy for users to explore connections and derive insights that go beyond traditional reporting. Additionally, by profiling and organising “dark data” (data previously underutilised or difficult to access), these techniques make it possible to reveal valuable information hidden within the organisation’s data ecosystem.
Despite these advancements, the usability of such sophisticated tools remains a challenge. Knowledge graphs, for instance, require users to understand tools or query languages like SQL, making them less accessible to non-technical business users who might not be skilled in querying. While these tools are highly effective, they can be complex for a general audience who may need to write SQL queries to access insights. Today’s business users are accustomed to the ease and immediacy of tools like ChatGPT and AI-powered co-pilots, which have become valuable assets in everyday operations. With the rise of large language models (LLMs) in the market, there’s a growing demand for intuitive, conversational interfaces that allow business users to interact with data products without the need for specialised technical knowledge. These users want a ChatGPT or co-pilot-like interface that enables them to explore data simply by asking questions in natural language. This demand for user-friendly interfaces has given rise to what we can call “next-generation data products.” These advanced products are no longer just data repositories but interactive, AI-enabled platforms that empower business users to extract insights seamlessly. By integrating LLMs and conversational AI into data products, these next-gen solutions bridge the gap between technical data capabilities and user accessibility. They make it possible for business users to interact with complex data structures, such as knowledge graphs, without needing SQL knowledge, empowering them to focus on decision-making rather than data retrieval. Next-generation data products represent a shift in the role of data platforms. They’re transforming from passive tools to active enablers of insight, combining the power of automation, AI, and conversational interfaces to create a truly user-centric experience. As organizations embrace these advancements, data products will increasingly serve as intuitive collaborators, delivering value to the business and driving innovation in unprecedented ways.
Generative AI, once labelled as the next revolution in enterprise technology, has hit a rough patch. According to Gartner, many organizations find themselves navigating what can be called the “Trough of Disillusionment.” The initial excitement surrounding large language models (LLMs), and their generative capabilities has given way to a more sober reality—results are not matching expectations. Enterprises are grappling with deployment challenges, underwhelming outcomes, and the stark realization that broad, one-size-fits-all AI models are not living up to the promise.
But disillusionment is not defeat. In fact, the path forward is becoming clearer. As organizations refine their use of AI, specialized models, and complementary technologies such as graph-enhanced retrieval-augmented generation (RAG) are emerging as practical solutions to bridge the performance gap.
The hype surrounding generative AI was driven by its potential to revolutionize industries, automate content creation, and improve decision-making. But this potential came with a set of assumptions—that LLMs trained on vast datasets could effortlessly generalize to any context, and that deploying AI would immediately yield productivity gains. However, as Gartner highlights, many enterprises have hit significant roadblocks.
Performance inconsistency is the primary challenge. While LLMs excel at generating human-like text, they often lack the domain-specific accuracy needed for nuanced business tasks. Enterprises need answers to specific questions, but generic models often deliver incomplete or irrelevant results. Moreover, the scale of LLMs introduces operational complexities, from computational costs to integration hurdles.
These shortcomings have led many to question whether GenAI is ready for prime time. But the real issue is not the technology itself, it is the misalignment between expectations and practical applications. The solution lies in a more specialized, targeted approach.
Specialized large language models (LLMs) are a targeted solution designed to address the performance gap. Unlike general-purpose models, specialized LLMs are fine-tuned for specific industries, use cases, or even individual companies. By focusing on a narrower dataset and a defined task, these models offer superior performance, delivering more accurate and contextually relevant results.
For example, a healthcare focused LLM trained specifically in medical literature and terminology can provide more precise diagnostic insights than a generic model trained on vast, unrelated data. Similarly, an LLM tailored for financial services will understand industry-specific regulations, market trends, and client data, allowing for better risk assessment and compliance automation.
The key to making the most of GenAI lies in customization. Instead of relying on a one-size-fits-all model, enterprises should invest in developing and training specialized LLMs that can truly address their unique needs.
Another breakthrough in closing the performance gap is graph-enhanced retrieval-augmented generation (RAG). While traditional RAG systems leverage vectors to retrieve relevant data from knowledge bases, graph-based RAG takes it a step further by mapping and utilizing the relationships between data points.
In a graph-enhanced RAG system, entities (e.g., products, customers, or business processes) are represented as nodes, and their relationships (e.g., dependencies, transactions, interactions) are edges. This allows the model to retrieve similar data points and contextually relevant ones based on how they are interconnected.
This approach dramatically improves contextual accuracy. Rather than a flat retrieval from a database, graph-based RAG provides a rich, multi-dimensional view of information. This is particularly useful in complex industries such as supply chain management, where understanding the relationship between suppliers, products, and logistics is critical to decision-making.
Integrating graph technology with GenAI bridges the gap between generic outputs and actionable insights, enabling businesses to navigate complex environments with greater precision.
As organizations move through the Trough of Disillusionment, it is important to shift the narrative. GenAI is not underperforming because the technology is flawed; it is struggling because it is being applied too broadly. The way forward is to adopt a more focused approach, one that integrates specialized LLMs, and graph enhanced RAG to solve the real, nuanced challenges enterprises face.
Here’s how businesses can start to close the GenAI performance gap:
1. Identify specific use cases: Do not deploy generative AI across the entire enterprise. Instead, focus on high-value, clearly defined use cases where AI can make a measurable impact. Whether it is automating customer support in a specific industry or optimizing procurement processes, narrow down the scope to ensure better results.
2. Invest in specialized models: Off-the-shelf LLMs are not the answer for every business. Enterprises should invest in customizing or fine-tuning models that understand their industry, business processes, and specific pain points. By tailoring models to their needs, companies will see more relevant and reliable outcomes.
3. Leverage graph technology: For industries that rely heavily on understanding relationships and dependencies, integrating graph-based RAG can significantly enhance the contextual accuracy of AI outputs. This approach goes beyond simply retrieving data; it retrieves data that is meaningfully connected to the task at hand.
4. Partner with the right expertise: Building specialized AI solutions requires deep technical expertise. Enterprises should consider partnering with companies that have experience in both AI development and the specific technologies like graph databases that can optimize their performance.
The trough of disillusionment is not the end of the GenAI story, it is a turning point. For enterprises, this phase represents an opportunity to refine their AI strategies and adopt solutions that are better aligned with their needs. Specialized LLMs and graph-enhanced RAG systems are key components of this new approach, offering more precision, context, and relevance than ever before.
In the world of research and development, one of the biggest challenges scientists face is how to make sense of vast amounts of data. For a top five life sciences company in the U.S., this problem became a barrier to accelerating drug discovery and optimizing research processes. The question wasn’t about gathering data—they had plenty. The real challenge was finding meaningful insights by connecting data from multiple, often siloed, sources. This is where advanced graph technology came into play, offering a powerful alternative to traditional databases. Our client, a leader in life sciences, was struggling with fragmented datasets—ranging from clinical trials and genome studies to patent filings and research papers. Traditional relational databases couldn’t handle the complexity or reveal the hidden relationships between the data. They needed a more flexible solution that could connect both structured and unstructured data sources and allow their R&D teams to explore relationships in real-time. The solution came in the form of Neo4j where, instead of storing data in rigid tables, Neo4j captured data as nodes and relationships, offering a more intuitive way to model and query complex datasets. What does this mean in practice? For scientists working with drug compounds and disease pathways, graph technology enables them to instantly visualize how different entities are connected. This dramatically reduces the time spent querying the data, allowing them to focus on analyzing potential drug interactions, adverse events, and genomic correlations. But adopting a new data platform isn’t just about switching technology. The success of graph analytics depends on how quickly and efficiently data can be prepared and loaded into the system. That’s where Modak’s Nabu comes into play. Modak Nabu™ automates the process of ingesting, preparing, and orchestrating data. It transforms complex datasets—both structured and unstructured—into a format that can be easily consumed by Neo4j. By streamlining this data preparation, Nabu cuts down on manual effort and ensures that the client’s R&D teams have clean, ready-to-use data at their fingertips. Our pipelines feed data directly into Neo4j, enabling the client to unlock the maximum potential of graph analytics. With Modak Nabu™ working behind the scenes, researchers no longer have to spend months waiting for their data to be ready for analysis. Instead, they can quickly discover hidden relationships and patterns that were previously obscured by data silos.
The switch to graph analytics wasn’t just a technical change—it led to measurable business outcomes for the client. By combining the power of Neo4j with Modak’s Nabu, the client saw a significant reduction in time to value. Previously, it could take up to 12 months to prepare data and generate meaningful insights. With the new solution in place, this process was accelerated by 4x, cutting the timeline to just three months. Additionally, the new system enabled the R&D team to reduce costs by 40%—a direct result of better data orchestration, faster insight generation, and reduced manual intervention. But more importantly, the adoption of graph technology enabled their researchers to ask bigger, more strategic questions. With Neo4j, they could explore new hypotheses, investigate drug interactions faster, and potentially reduce the time it takes to bring a drug to market. In addition to Modak and Neo4j, another partner played a key role in this transformation: Process Tempo. They ensured that the insights generated by the client’s R&D teams were effectively translated into actionable business strategies. By providing real-time visibility into data workflows, Process Tempo added an extra layer of efficiency to the overall process.
The switch to graph analytics wasn’t just a technical change—it led to measurable business outcomes for the client. By combining the power of Neo4j with Modak’s Nabu, the client saw a significant reduction in time to value. Previously, it could take up to 12 months to prepare data and generate meaningful insights. With the new solution in place, this process was accelerated by 4x, cutting the timeline to just three months. Additionally, the new system enabled the R&D team to reduce costs by 40%—a direct result of better data orchestration, faster insight generation, and reduced manual intervention. But more importantly, the adoption of graph technology enabled their researchers to ask bigger, more strategic questions. With Neo4j, they could explore new hypotheses, investigate drug interactions faster, and potentially reduce the time it takes to bring a drug to market. In addition to Modak and Neo4j, another partner played a key role in this transformation: Process Tempo. They ensured that the insights generated by the client’s R&D teams were effectively translated into actionable business strategies. By providing real-time visibility into data workflows, Process Tempo added an extra layer of efficiency to the overall process.
Traditional relational databases, while still useful for certain applications, fall short when it comes to understanding relationships within data. Their rigid structure is not built to handle the complexity of modern data landscapes. This is especially true for organizations in life sciences, where data spans everything from clinical research to real-world evidence. Graph databases like Neo4j offer a different approach. By visualizing data as nodes and connections, they allow users to explore relationships dynamically. This flexibility is crucial when analyzing complex datasets such as gene-disease associations, drug interactions, or patient health records. For our client, the move to graph technology was a natural evolution of their data strategy. It enabled them to move beyond basic data-driven insights and towards a more intelligence-driven approach, where data relationships are explored at the speed of thought. Looking ahead, the possibilities for graph analytics are endless. As more organizations adopt this technology, we expect to see even greater advancements in areas like personalized medicine, clinical trial optimization, and drug discovery.
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