The ever-increasing influx of data from diverse sources has become a significant challenge for organizations and their data engineers to continuously manage using incumbent, outdated tool stack that lack flexibility. Due to the lack of control over the data structures provided by external sources, organizations struggle to identify and respond to changes in data, which can be catastrophic for downstream analysis and decision-making by business users. All these issues point to a reality that without effective data observability, companies will struggle to treat data as an asset.

Data observability ensures that data is reliable, accurate, and available through real-time monitoring, analysis, and alerting. Its core pillars maintain the health of modern data stacks and provides visibility for swift issue detection and diagnosis. All stakeholders, including data engineers and scientists, can gain visibility, ensuring data quality throughout its lifecycle thanks to these five key pillars of data observability:

• 1. Data monitoring and alerting: detect patterns and anomalies, and alerts are generated when issues arise. The process also involves validating the quality, consistency, and completeness of data while ensuring that it is readily accessible to those who require it. This is done by embedding data quality checks in data pipelines. These embedded data quality checks search for patterns and anomalies, and generate alerts when problems occur. They also involve tracking and detection of schema drift, data change, pipeline run-time, frequency, and identifying bottlenecks or other issues that can impact the flow of data.
• 2. Observing data infrastructure: monitors metrics such as compute, storage, memory utilization, and network traffic. This is accomplished by monitoring databases, storage systems spread across on-prem and in private and public clouds, and identifying issues that can impact the performance and availability of data.
• 3. Data usage: observes metrics like query performance, user behaviour, and data access patterns. It also identifies any problems that can affect the efficiency and effectiveness of data-driven decision-making based on the utilization of stakeholders such as data analysts, data scientists, and business users.
• 4.Utilization and cost monitoring: tracks expenses related to the management of data pipelines, such as infrastructure and storage costs, as well as resource consumption. The approach also involves identifying opportunities to save costs and optimizing the utilization of resources to maintain high performance and reliability of data pipelines and systems.

Organizations can follow a basic structure to implement data observability. The first step involves defining the strategy by scoping the efforts, involving stakeholders, and setting goals, metrics, and a roadmap. Next, choose the right tools by selecting monitoring, alerting, log management, and visualization tools that fit your requirements and budget. Then, design the control center by setting up monitoring and tracking for data pipelines, ETL processes, databases, storage systems, and cloud platforms. Utilize log aggregators and dashboards to track metrics like latency, throughput, error rates, resource usage, and network traffic.

It is also important to establish processes for incident management – including reporting, triage, and resolution – and to define roles and responsibilities, establish escalation paths, and develop playbooks for common scenarios. Finally, continuously improve data observability practices by analyzing metrics and alerts, identifying areas for improvement, and implementing changes to your monitoring and alerting processes.

Successful implementation of data observability practices enables enterprises to mitigate risks, improve data quality, expedite decision-making, ensure compliance, reduce downtime, proactively address data pipeline issues, and optimize modern data environments.

Investing in data observability is essential to unlock the full potential of data and gain a competitive edge in the digital age. It is crucial for enterprises managing modern data stacks, and ensures dependable, accurate, and available data. Ultimately, this paves the way for informed decisions and driving business outcomes. Real-time monitoring and analysis of data pipelines improve operational efficiency and minimize downtime. By implementing data observability practices organizations can meet critical compliance requirements while optimizing data infrastructure.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

DataOps, which aims to streamline and automate the end-to-end data pipeline operations, has gained prominence in recent years because of its ability to create an agile strategy. Combining best practices from DevOps and data governance, DataOps delivers high-quality data with improved security and compliance and fosters a culture of collaboration and continuous improvement among data citizens, IT and business teams. But many organizations still struggle with the concept and how to get started.

DataOps involves automating data collection and analysis in an effort to increase reusability, democratize data access, and continuously monitor the data pipeline. DataOps ensures valuable metrics are identified, relevant data sets are utilized, and technologies are applied to extract, transform, and analyze data.

Key principles include:

• Collaboration: Brings together data engineers, data scientists, and DevOps engineers to ensure that everyone is working towards the same goal, and that data is shared and used effectively. Allows for the leveraging of diverse perspectives and skills, and leads to more robust data solutions and informed decision-making.
• Automation: Automates as much of the data lifecycle as possible to improve the speed and accuracy of data processing and reduces the risk of human error. Automated data processes accelerate time-to-market by enabling faster data delivery, empowering individuals to generate insights and make data-driven decisions more quickly. It also facilitates scalability by allowing organizations to handle large volumes of data without compromising performance, ensuring flexible scalability as data needs continue to grow.
• Continuous improvement: Constantly monitors and improves processes to ensure that DataOps is optimized and adaptable to evolving business requirements.

Conventional data management approaches tend to be siloed and fragmented, which leads to data quality issues, slow time to market for data-driven insights, and increased operational costs. As a result, these data management practices tend to struggle with manual efforts, lack of automation which leads to slow and inefficient data delivery. Further the lack of data quality and governance issues slows down the process of onboarding new data users. DataOps, on the other hand, is a more comprehensive style of data management. It brings together business
stakeholders, allowing them to work together and automate the data lifecycle and ensures that data is always available, up-to-date, and of high quality and used by technical and business stakeholders across the enterprise.

The key components of a DataOps lifecycle include:

• Data ingestion: involves loading data into a data warehouse or data lake. This can be a complex process, as data may come from a variety of sources, such as operational systems, transactional databases, and external sources.
• Data curation: which is the process of cleaning, transforming, and enriching data. This helps to ensure that data is of high quality and ready for analysis.
• Data observation: monitors data quality and performance. This helps to identify any potential issues with the data, such as data drift or data corruption.

Achieving faster time-to-market for data-driven solutions enables organizations to accelerate the time to market for data-driven solutions. Because DataOps helps to automate the data lifecycle – from data collection to analysis to deployment – it frees up data scientists and other business users to focus on more strategic tasks, such as developing new insights and generating new business opportunities. It also improves data quality and governance by facilitating effective management of the data lifecycle, which ensures that data is consistently available, up-to-date, and of high quality.

Finally, operational costs are reduced by automating data processes and eliminating manual tasks. This improves operational efficiency, minimizes human errors, and optimizes resources for higher-value activities. Automation enables scalable data operations, accommodating growing data volumes without significant cost increases, and allows organizations to manage larger datasets and deliver timely insights without incurring substantial expenses.

Implementing DataOps requires a thoughtful and systematic approach. To get started, enterprises need to assess the current data landscape to understand the existing data management processes, systems, and pain points within their organization and identify areas where DataOps can bring the most value. Companies must also set achievable goals and priorities by defining clear objectives for why they are implementing a DataOps strategy. Start with specific use cases or projects that can demonstrate the benefits to stakeholders and then gradually expand the scope to cover broader data operations.

To ensure a successful DataOps implementation, consider the following best practices:

• Build a skilled and collaborative DataOps team: Assemble a cross-functional team with expertise in data engineering, data science, and DevOps. Encourage collaboration and provide opportunities for continuous learning and skill development.
• Establish a robust data governance framework: Define data governance policies, standards, and guidelines to ensure data quality, security, and compliance. Implement data cataloging and metadata management practices for improved data discoverability.
• Leverage cloud-based technologies for scalability and flexibility: Cloud platforms offer scalability, flexibility, and cost-effectiveness for DataOps implementations. Utilize cloud-native data services, infrastructure, and analytics tools to leverage their capabilities.
• Continuously monitor and optimize DataOps processes: Regularly evaluate and refine DataOps processes to identify bottlenecks, improve efficiency, and enhance data quality. Implement performance monitoring and analytics to gain insights into data operations.

DataOps enable organizations to overcome data management challenges and make rapid business decisions. It also unlocks the full potential of data, providing a competitive advantage in the data-driven era allowing organizations to experience faster time-to-market, improved data quality and governance, increased collaboration, and reduced operational costs. But implementing DataOps requires a combination of effective processes, automation, and a collaborative culture that aligns both IT and business teams as well as data citizens. With continuous improvement and the right strategies in place, organizations can leverage the inherent agility of DataOps and apply it to drive innovation, enhance operations, and make confident data-driven decisions.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

The future belongs to organizations that are led by business-outcomes, rather than being driven by technology. These companies are laser-focused on delivering business value at all times and have an urgency to transform fast, quickly stand-up analytics use cases, and continuously innovate. However, this often requires adopting a hybrid approach that integrates the best of centralized infrastructure with domain-driven data product development. It also needs to lead with the user experiences/needs in mind. As a result, this method helps deliver results faster and aligns well with organizational culture and skills, creating solutions with more value to clients/customers.

Partners who provide an integrated platform that supports active metadata management save their customers time and money while also delivering trusted business outcomes. The time saving comes from avoiding the need to integrate several technologies and by making the business significantly more efficient. For example, organizations can easily measure the benefits such as the ratio of successful projects, deployed use cases, and the frequency of new releases resulting in a higher trust in data. They can also leverage the approach to create economies of scale and to avoid unnecessary downtime.

Finally, these products gain from economies of scale, and like an ML model gets better by retraining itself frequently, so do these cloud-native multi-tenant data frameworks. By flipping the focus from technology to outcomes, organizations that consider IDAP are finally achieving the aspirational goal of becoming truly data driven.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

Determining the best approach is not a straightforward task due to the multitude of standards and approaches available. Some key considerations include:

1. Best-of-breed vs. integrated: The debate between a centralized (bundled or integrated) and decentralized (unbundled or decoupled) approach is ongoing. An integrated approach has been prevalent in recent years but may lead to IT bottlenecks. On the other hand, the best-of-breed method offers specialized products but it comes with higher integration overhead. Organizations need to align with their corporate standards and guidelines to determine the most suitable approach.
2. Proprietary vs. open platform: Proprietary solutions provide peace of mind and superior user experiences but often come at a higher cost. Open source products offer lower license costs and benefit from community contributions; however, they may introduce unforeseen risks. The decision between proprietary and open platforms depends on an organization’s IT skills maturity and risk tolerance.
3. Control vs. managed: Some organizations, especially heavily regulated ones, prioritize control over their IT assets and have skilled staff to manage advanced technologies. Others, particularly medium to small-sized companies, prefer managed services to reduce operational burdens. Modern architectures with numerous moving parts often require managed services for effective operation and debugging.
4. No/low-code vs. programmatic: Different roles within an organization require varying levels of coding capabilities. Data scientists often prefer programmatic access to raw data using specific technical languages, while data analysts may rely on curated data. Non-technical roles may opt for no/low-code tools to interact with data through a semantic layer. A hybrid approach that supports these varying needs is crucial for enabling different personas within an organization.

In light of these considerations, a hybrid approach that combines the best aspects of different options proves to be the preferred choice. Organizations can create a business-led intelligent data architecture platform that unifies data and metadata, facilitating faster development of data products.

This option allows for centralized data infrastructure and metadata discovery while enabling decentralized development. Metadata use cases, such as data quality and observability, are also given due attention from the outset. Ultimately, these intelligent data architecture platforms empower business users by providing timely and trustworthy information while ensuring data security and trust.

To truly leverage data to its fullest and create a solid and trusted data pipeline, organizations must recognize the importance of delivering it at the speed expected in today’s fast-paced world. By embracing a business-outcome first approach, and adopting intelligent data architecture platforms, organizations can overcome challenges, accelerate time-to-value, improve reliability and data quality, and effectively leverage their data assets when needed to achieve a competitive advantage.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

“Gartner predicts that through 2024, organizations that adopt active metadata capabilities can decrease the time-to-delivery of new data assets to users by as much as 70%.”

In today’s fast-paced and data-centric business landscape, organizations are generating and accumulating vast amounts of data at an unprecedented rate. This data holds valuable insights that can drive informed decision-making and fuel innovation. Although, the sheer volume and variety of data can also pose significant challenges, making it essential for companies to effectively manage their data assets. Metadata, the data about data, is essential for understanding, organizing, and extracting value from datasets. However, traditional passive metadata practices have become inadequate in coping with the complexity and scale of modern data environments. Enter active metadata management, a revolutionary approach that promises to address these challenges and unlock the full potential of data assets.

Passive metadata practices suffer from several limitations in today’s data-rich ecosystem. One of the key problems is the lack of real-time updates and synchronization with data sources. As data constantly changes and evolves, passive metadata quickly becomes outdated, leading to inaccurate insights and poor decision-making. Additionally, passive metadata often remains siloed and fragmented, making integrating and understanding the relationships between various datasets challenging.

Active metadata practices offer a solution to these issues by providing a dynamic and interconnected metadata ecosystem. Active metadata enables seamless and rapid metadata flow throughout the whole data stack, integrating rich context and data into each data stack tool. In addition to the basic technical metadata active metadata management provides business (semantics enrichment, business tags) operational (infrastructure performance metadata), and social (user-generated information) metadata.

By effectively activating the metadata, organizations can enhance seamless data orchestration, improve data quality and observability, increase data utilization, streamline data governance, maximize the value of data assets, and enable a data-driven culture. When data and analytics solutions use active metadata, the positive impacts on automation, insight discovery, and prescriptive recommendations to optimize data usage drive additional demand for even more utilization of active metadata. The enhanced demand encourages data and analytics leaders to focus on metadata capabilities when selecting new vendors or technologies. It also pushes them to seek more interoperability among their ecosystems to share and exchange metadata.

Why Active Metadata Management Matters?

• Data Discoverability and Governance: Active Metadata Management enhances data discoverability by providing accurate and up-to-date information about datasets. It acts as a data catalog that enables users to find relevant data quickly and understand its lineage, quality, and usage restrictions. Additionally, it ensures data governance by enforcing data policies, privacy regulations, and access controls.
• Data Integration and Interoperability: In a data-driven organization, data is spread across various systems and platforms. Active Metadata Management facilitates data integration by providing insights into data structure, formats, and transformation rules. It allows data to flow seamlessly across different systems, enabling better data interoperability.
• Data Quality and Trust: Maintaining data quality is crucial for making reliable business decisions. Active Metadata Management ensures that data quality rules and checks are applied consistently across the data ecosystem. This, in turn, builds trust in the data, empowering stakeholders to make data-driven choices with confidence.
• Impact Analysis and Data Lineage: In a rapidly evolving data landscape, it’s essential to understand how changes in one dataset can affect others. Active Metadata Management allows organizations to perform impact analysis, identifying potential consequences of data changes. Furthermore, it provides comprehensive data lineage, showing the origin and transformation history of data, increasing data transparency.
• Data Collaboration and Knowledge Sharing: Active Metadata Management fosters collaboration among data users and stakeholders. It enables them to contribute their knowledge and insights about datasets, enriching the metadata with valuable context. This collaborative approach facilitates knowledge sharing and promotes a data-driven culture within the organization.

Active Metadata Management is a critical component of data-driven organizations’ successful data management strategy. By actively capturing and maintaining metadata throughout the data lifecycle, organizations can improve data discoverability, integration, quality, and governance. This, in turn, enables better decision-making, fosters innovation, and enhances the overall value derived from data assets. In an era where data is a strategic asset, organizations that embrace Active Metadata Management will gain a competitive edge, unlocking the full potential of their data resources and driving success in the data-driven world.

Organizations continue to face a pressing challenge of harnessing the power of data swiftly and effectively, as traditional data management approaches no longer suffice in the face of increasing volumes and complexity. Compounding the problem is that most data consumers rely solely on IT teams for data-related tasks which can lead to bottlenecks, delays, and limited agility. While there are many technology options that can help minimize the impact, it’s clear that enterprises need a solution that empowers business users to access, analyze, and utilize data themselves. While self-service data management is not a new approach, it remains a game-changing alternative that empowers decision-makers at all levels with direct access to data and enables them to make informed choices in real-time.

Self-service data management is driven by several factors that have transformed the way organizations handle and leverage their data. These drivers have emerged because of the rising volume, diversity, and speed at which data is generated, along with the growing demand for agility and effectiveness in making data-driven decisions.

One of the key drivers is data democratization. With self-service data management, organizations empower business users to access and analyze data without heavy reliance on IT departments. This transition helps eliminate obstacles, facilitates quicker decision-making, and fosters a data-driven culture across the organization.

Another crucial element of self-service data management is the growing complexity of today’s data ecosystems. As data sources multiply and data formats diversify, self-service tools provide necessary capabilities for tasks like data preparation. They streamline the integration, transformation, and analysis of data from various sources in a unified and user-friendly manner. This reduces the dependence on specialized technical skills and simplifies data operations.

The demand for agility and responsiveness also drives the adoption of self-service data management. Traditional approaches often involve lengthy requests and development cycles, hindering timely insights. Self-service tools on the other hand empower users to explore and analyze data at their own pace, enabling quick experimentation, discovery, and iterative decision-making.

Self-service data management tools are instrumental in enhancing data integration and dismantling data silos within organizations. It is challenging to integrate and thoroughly evaluate data because of these silos, leading to fragmented insights and inefficient decisionmaking. Fragmented data silos across organizations hinder data integration and sharing across systems, making it difficult to gain a comprehensive view of the data.

Also, self-service data management tools provide a no-code/low-code, UI-driven approach for all data integration tasks. Users can transform and prepare data according to their requirements, ensuring its consistency and usability. Data governance features enforce security and compliance policies, safeguarding sensitive information during integration and sharing processes.

By empowering users to create and manage their own data pipelines, self-service tools reduce dependency on IT teams and enable faster integration cycles. Collaboration features promote cross-functional teamwork and knowledge sharing, breaking down data silos across departments. Additionally, real-time data integration capabilities ensure that organizations can leverage the most up-to-date information for timely decision-making.

Self-service data management tools greatly enhance the process of data cataloging within organizations. These tools used machine learning to automate the discovery of data assets by analyzing metadata and content, expediting the cataloging of large volumes of data. Users can enrich metadata with contextual information, such as descriptions and tags, improving the understanding and searchability of data assets.

Furthermore, self-service tools capture data lineage, providing insights into data dependencies and impact analysis. They also assess data quality, identifying and prioritizing assets for improvement. Integration with data governance frameworks ensures that cataloged data aligns with established policies, standards, and compliance requirements.

Ultimately, self-service data management tools streamline data cataloging by automating discovery, facilitating user-driven annotation, capturing lineage, assessing data quality, and integrating with data governance. This leads to comprehensive and accurate data catalogs that enhance data understanding and utilization.

Self-service data management tools play a vital role in augmenting data quality and observability within organizations. These tools provide capabilities that enable users to assess, monitor, and improve data quality.

Users can embed data quality and observability checks to automate the identification of data anomalies, schema drift, data change, inconsistencies, and inaccuracies in the data. By highlighting data quality issues and observability alerts, self-service tools empower users to take corrective actions, such as data cleansing and enrichment, to improve the overall quality of the data.

Organizations can follow these best practices to efficiently adopt self-service data management and avoid any barriers to foster successful implementation:

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

In today’s digital age, cloud storage has become the backbone of business operations, offering advantages such as scalability, flexibility, and improved customer interaction.

However, as cloud adoption continues to soar, a new challenge has emerged – cloud-flation. This phenomenon refers to the inflationary pressures faced by enterprises as cloud service costs rise, creating a significant impact on their budgets and strategies.

“Cloud-flation” is a term coined to describe the rising costs of cloud-based services. This trend is essentially a global concern that has impacted businesses of all sizes. According to a report by Anodot, nearly 50% of IT professionals struggle to control cloud costs, with a third witnessing a 25-50% surge in prices.

Several factors contribute to cloud-flation:

Cloud cost optimization combines strategies, best practices, and tools to reduce cloud expenses while maximizing business value. It is a critical approach to ensure that cloud spending aligns with business goals and avoids waste.

Before, during, and after cloud implementation, organizations should address these key questions:

Cloud cost optimization tools are essential for managing and controlling cloud expenses effectively. They provide clear visibility into cloud spending, configuration settings, and cost-saving opportunities. These tools fall into two categories:

FinOps is a method for efficient cloud financial management with a holistic business development approach. It ensures cost management aligns seamlessly with broader business objectives. While addressing the challenge of cloud waste, accounting for up to 32% of business expenses, FinOps actively shapes cloud spending to deliver tangible results.

Key to successful FinOps implementation is collaboration with cloud providers or independent solutions. These tools enable continuous optimization through specialized strategies tailored to organizational requirements.

FinOps operates according to an organization’s cloud cost maturity, categorizing them as crawl, walk and run. This spectrum acknowledges the distinct stages of optimization journeys, offering a business development driven approach.

Incorporating FinOps into cloud management aligns spending with business goals, optimizes costs, and maximizes ROI, ensuring that cloud expenses remain efficient in supporting the overall success of the organization.

While cloud computing offers numerous benefits to enterprises, the challenges of cloud-flation and rising costs cannot be ignored. It’s evident that poorly designed cloud architectures, inaccurate usage estimations, and resource overestimations can all contribute to ballooning cloud expenses. By adopting cloud cost optimization strategies, organizations can maintain control over their cloud budgets, optimize resources, and ensure that cloud spending remains aligned with their business objectives. Additionally, leveraging automation and embracing the principles of FinOps can help organizations not only reduce costs but also ensure that their cloud spending aligns with their broader business objectives. To thrive in the cloud era, it’s essential to navigate the cloud-flation landscape with vigilance and smart management.

This may be why Gartner said that by 2024, data fabric deployments will quadruple efficiency in data utilization, while cutting human-driven data management tasks in half. Yet, despite the awareness of data fabric as a potential solution, the absence of appropriate tools and technologies continue to hinder the efficient extraction, transformation, and loading of data from various sources. The diversity of data types (structured, semi-structured, and unstructured data), and data sources requires different approaches for integration and processing. Additionally, the incompatible data formats and the coexistence of on-premises data centers and cloud platforms add to the complexity of the task.

Enterprises need an efficient data management strategy for integrating and orchestrating data across multi-cloud and hybrid environments. While solutions such as data virtualization have been used to eliminate data silos and provide a consolidated view, the lack of automation capabilities makes it hard to address key data quality requirements. In contrast, data fabric offers an intelligent orchestration engine with metadata at its core, enhancing value and business outcomes.

Implementing an efficient data fabric architecture is not accomplished with a single tool. Rather, it is a unique design concept encompassing various technology components such as data integration, data catalog, data curation, metadata analysis, and augmented data orchestration. Working together, these components deliver agile and consistent data integration capabilities across a variety of endpoints throughout hybrid and multi-cloud environments.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

As per the findings of Gartner CIO and Technology Executive Survey 2023, cloud platforms continue to be a top priority for organizations worldwide, with half of them, i.e., 50%, planning to increase their spending on cloud modernization.

Enterprises are investing and migrating towards cloud platforms to stay competitive and meet the changing needs of customers and users. In the contemporary and highly competitive business environment, the strategic adoption of cloud migration is no longer a mere choice but an imperative. Cloud adoption enables greater scalability, flexibility, and cost efficiency. Organizations are leveraging cloud platforms for a variety of services, from infrastructure to software as a service (IaaS, PaaS, SaaS).

Decreasing investments in legacy infrastructure and data warehouses suggest that organizations recognize the need to modernize their IT environments to keep pace with the evolving technology landscape. Legacy systems and applications can be rigid and inflexible, making adapting to changing business needs and emerging technologies challenging.

Along with the scalability and flexibility issues, the integration of legacy systems with modern applications and services can be complex and costly, leading to compatibility issues. The transformative phase is a cost-saving measure and a strategic move to align technology with business objectives and future growth.

Businesses must adapt rapidly to meet evolving customer expectations, deliver innovative products and services, and remain agile in the face of market shifts. Cloud migration stands as a linchpin in this transformative journey, enabling businesses to realign their technological infrastructure with digital aspirations.

To unlock the full potential of cloud migration, organizations must leverage the benefits provided by the cloud. We are going to delve into the factors that further improve the optimization during the entire process of cloud migration. These factors are the cornerstone in achieving the strategic objectives while harnessing the myriad benefits that the cloud has to offer.

Cloud platforms offer on-demand scalability, allowing organizations to easily scale up or down based on fluctuating workloads. The scaling can be done in both ways, i.e., Vertical Scalability (Scaling Up) and Horizontal Scalability (Scaling Out). In a cloud environment, vertical scalability involves increasing the capacity of an individual virtual machine (VM) or instance. On the other hand, the horizontal scalability approach spreads the workload across multiple instances, distributing traffic and preventing performance bottlenecks. The professional cloud migration service providers offer unmatched auto-scaling capabilities, enabling organizations to automatically adjust the number of instances based on predefined criteria.

Elasticity involves dynamically provisioning and de-provisioning resources as needed. When demand increases, additional resources are provisioned, and when demand decreases, excess resources are automatically de-provisioned to optimize resource utilization and reduce costs. Resource Orchestration is another way to improve elasticity. Elasticity is often achieved through resource orchestration tools, i.e., auto-scaling tools. These tools enable the automatic adjustment of resource allocation based on predefined policies and triggers.

Proactive monitoring entails the continuous tracking of Service Level Indicators (SLIs) against predefined Service Level Objectives (SLOs). Real-time data is collected through monitoring tools and processes to evaluate service performance and adherence to Service Level Agreements (SLAs). Achieving a resolution rate of 95% or higher signifies a commendable level of efficiency in promptly addressing issues. It includes vigilant monitoring of SLIs and their comparison with established SLOs to uphold stringent service quality standards.

Cloud migration brings the invaluable advantage of enhanced disaster recovery at lower costs. In the event of a disaster or system failure, the cloud’s redundancy and failover capabilities allow for swift and seamless failover to backup resources, ensuring uninterrupted service delivery to the clients.
Cloud-based backups, replication, and disaster recovery solutions enable organizations to minimize downtime, ensuring business continuity and reducing the financial impact of data loss. The accelerated data recovery capability represents a critical asset in today’s data-driven landscape, enhancing an organization’s resilience and responsiveness to unforeseen challenges.

Cloud migration is a transformative strategy that not only empowers organizations but also maximizes their Return on Investment (ROI) in several critical ways. By transitioning from legacy infrastructure to the cloud, organizations gain access to on-demand scalability, allowing them to efficiently scale resources up or down based on fluctuating workloads. Increased flexibility translates directly into cost optimization as organizations pay only for the resources they use.

Cloud migration fosters innovation by reducing time-to-market for new products and services, giving businesses a competitive edge. In a nutshell, cloud migration represents a path toward improved efficiency, innovation, and ultimately, a higher ROI for organizations seeking to thrive in the data-driven digital age.

Read more about Cloud Migration on our website.

In the ever-evolving landscape of modern business operations, the cloud has emerged as a game-changer, offering scalability, flexibility, and efficiency. However, the plethora of benefits comes with a caveat – the rise of cloud costs. Organizations find themselves at a crucial crossroads with the responsibility of managing these escalating expenses without compromising the benefits of cloud technology.

The rising cost of the cloud is a challenge for enterprises, but it is not insurmountable. There are several steps that an organization can take to respond to rising cloud costs.

‘FinOps’ is a set of practices designed to manage cloud costs effectively. It emphasizes collaboration among finance, IT, and business teams to align cloud expenses with business objectives. This approach involves setting clear cost goals, tracking cloud expenditure, identifying, and eliminating waste, using flexible pricing options, automating tasks, and investing in monitoring tools. By implementing FinOps practices, businesses can gain visibility into cloud costs, make informed decisions, enhance collaboration, and increase agility. As cloud costs rise, adopting FinOps can be a valuable strategy for optimizing spending, maximizing value, and achieving financial goals.

Enterprises hold the tools to navigate rising cloud costs strategically. By adopting a cloud-first mindset, optimizing usage, embracing hybrid solutions, and implementing FinOps practices, businesses can effectively manage their cloud expenditures. Coupled with flexible pricing, multi-cloud strategies, automation, and data-driven insights, organizations can ensure that the cloud remains not only a technological asset but a financial boon, aligned with the long-term goals of their organizations.

In the ever-evolving landscape of artificial intelligence (AI), Generative AI has been generating a lot of attention. Generative AI is a field of AI that uses techniques to learn from existing data artifacts to generate new content based on the training datasets. GenAI can produce various content such as images, audio, music, stories, speech, text, and code.

Generative AI employs a variety of techniques that are in a constant state of evolution. At the forefront of these techniques are foundational AI models, which undergo training on extensive collections of unlabelled data. These models can subsequently be fine-tuned for various tasks. Despite the demanding nature of creating and training these models, involving intricate mathematical processes and significant computational resources, they essentially function as prediction algorithms.

One of the foundational AI models is Large Language Models or LLMs. LLMs are trained on vast amounts of text data to generate and produce new textual content.

A subset of artificial intelligence known as GenAI is focused on the production of novel and distinctive content. This field involves the development and utilization of algorithms and models capable of generating original outputs, which can encompass a wide range of media including images, music, text, and even videos. The ultimate aim of generative AI is to mimic or surpass human levels of creativity and imagination.

The process of generative AI entails training these models on extensive datasets to discern the underlying patterns, structures, and characteristics of the data. Once this training phase is complete, these models can autonomously generate fresh content by either selecting samples from the learned distribution or ingeniously repurposing existing inputs.

Beyond its role in enhancing individual creativity, generative AI serves as a valuable tool to augment human efforts and improve various activities. For instance, it plays a crucial role in data augmentation by creating additional training instances, thereby enhancing the efficacy of machine learning models. Additionally, generative AI can enrich datasets with lifelike graphics, proving invaluable in computer vision applications like object recognition and image synthesis.

Language Models, on the other hand, are a subset of Generative AI focusing specifically on processing and generating human language. These models are trained on vast datasets of text, learning the intricacies of grammar, syntax, semantics, and even nuances of language use. Large Language Models can comprehend textual input, answer questions, write essays, and engage in conversations that often feel remarkably human-like.

Generative AI and Language Models have ushered in a new era of AI capabilities, pushing the boundaries of creativity and human-machine interaction. Generative AI extends beyond language to encompass a wide array of content creation, while Language Models specialize in understanding and producing human language with remarkable fluency. From art to science, these technologies are impacting industries in profound ways, offering efficiency, creativity, and innovation.

As these technologies continue to evolve, ethical considerations and responsible usage become paramount. Striking a balance between the potential benefits and ethical concerns will shape the future of AI-driven creativity. Whether it’s generating a captivating story or providing insightful information, Generative AI and Language Models are shaping a world where AI is not just a tool, but a creative collaborator.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

“Dirty Data” is the biggest challenge to overcome in Machine Learning, according to a 2017 survey by Kaggle with over 16,000 data scientists.

This statistic underscores the pervasive challenges data silos create for businesses. Today, industries across the globe find themselves impeded by their siloed data, hindering their ability to tap into the full potential of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). This is where FAIR-driven data comes into play.

FAIR introduces a universal framework, capable of transforming data into a coveted asset irrespective of the industry, through adherence to principles rendering data Findable, Accessible, Interoperable, and Reusable. FAIR empowers advanced computational techniques, ensuring the delivery of precise and actionable insights.

Data silos, which are isolated storage systems for structured, semi-structured, and unstructured data sources like Electronic Health Records (EHRs), clinical research data, and patient-generated data, hinder data accessibility and integration across organizations. FAIR principles tackle this challenge by ensuring data becomes Findable, Accessible, Interoperable, and Reusable.

In practical terms, this means FAIR-driven data platforms seamlessly blend data from various sources, such as sales, marketing, and production, into a unified ecosystem. This integration creates a comprehensive organizational view, transcending individual departmental boundaries. As a result, businesses can make data-driven decisions, breaking free from the limitations imposed by data silos, and harnessing the full potential of their information assets..

Artificial Intelligence (AI) and Machine Learning (ML) encounter universal challenges rooted in the complexity, ambiguity, and variability of unstructured data. FAIR data confronts these challenges head-on, eliminating ambiguity and offering a clear path for machine learning algorithms. It ensures terms are correctly associated with their intended entities, guarding against costly misinterpretations.

Furthermore, FAIR data leverages ontologies, and structured knowledge models expediting the learning process for AI models. These ontologies provide AI models with a structured foundation of domain knowledge, significantly expediting the learning process. Consider the example of an ontology, encoding the relationship between “Concept Z” and “Attribute A.” AI models can swiftly grasp this connection, significantly enhancing their accuracy and efficiency. FAIR data doesn’t just enhance AI/ML training; it also provides high-quality data inputs necessary for accurate results in applications like sentiment analysis and anomaly detection.

Semantic enrichment, a fundamental aspect of FAIR data, supercharges data Findability, revolutionizing search accuracy, and precision. Users can tackle complex queries using ontology-based searches, a feature with widespread applicability across industries.

FAIR data goes a step further by incorporating deep learning techniques into the mix. Deep learning equips modern search engines with the ability to discern the intent behind a query, similar to everyday search engines. This transformative capability empowers users to employ natural language queries, opening doors to a treasure trove of information. Complex questions, such as predicting market trends or customer behavior, become accessible and solvable through the power of FAIR data-driven platforms.

In a data-driven world where businesses are constantly seeking a competitive edge, FAIR-driven data platforms emerge as pivotal catalysts for unleashing data’s latent potential. By embracing the FAIR principles, organizations elevate data to the status of a strategic asset, capable of driving innovation and yielding valuable insights. As organizations strive towards becoming more data-driven, FAIR principles stand as a guiding “North Star”, empowering businesses to realize the true potential of their data.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

Organizations face the challenging task of efficiently and securely managing their IT infrastructure in the ever-evolving data-driven business landscape. The absence of specialized skills, proactive monitoring, and scalable solutions often results in operational setbacks, security breaches, and inefficiencies.

As technology evolves, the absence of dedicated IT management resources hampers organizations from harnessing the full potential of digital transformation, ultimately undermining competitiveness. That is where managed services come into play to address these challenges by providing expertise, monitoring, and scalability to bridge the gap between IT capabilities and evolving business needs, fostering growth and resilience.

According to the anticipated projections of Mordor Intelligence, the managed services market is poised for substantial expansion with an aim of achieving a significant milestone of USD 380.83 billion by the year 2028. These anticipated projections highlight the escalating demand for managed services, reflecting their integral and indispensable role in optimizing IT operations and bolstering business efficiency across industries. The dynamic technologies in the market underscore the continued growth trend of the managed service industry, reflecting the ever-increasing demand for specialized IT support in our intricately interconnected and evolving world.

Managed services are specialized solutions designed to oversee and manage the day-to-day operations of specialized applications within an organization. It offers enhanced capabilities to end-users, enabling them to leverage advanced functionalities with ease. By entrusting routine management tasks to a managed service provider, in-house IT teams can redirect their efforts toward more strategic IT initiatives.

Managed services are looked after by a managed services provider (MSP). MSP oversees and optimizes the on-prem servers and cloud computing environment of the organizations while taking care of tasks such as provisioning resources, monitoring performance, ensuring security, managing backups, tracking costs, and handling software updates. Managed service solutions not only optimize operational efficiency but also allow businesses to concentrate on their core competencies and key business objectives.

A managed service approach signifies various aspects of managing the business environment in a business organization. With managed services, businesses can achieve numerous benefits from improved scalability to cost efficiency. Let’s explore the distinct gains an organization can get with a managed service approach.

Managed services play a significant role in driving efficiency, bolstering data protection, and delivering specialized skills without hampering the operational workflow of ongoing projects. By offloading management burdens, businesses can allocate resources strategically and propel their success.

Managed services provide a competitive edge through seamless scalability. As business needs change, operations effortlessly adjust to match evolving demands. This adaptive approach with efficiently managed services spans resources like computing power, storage, and personnel, enabling smooth growth or contraction without disruptions or shortages.

Operational interruptions are significantly reduced with managed services. Downtime, whether due to system failures, maintenance, or upgrades, can be minimized through proactive monitoring and maintenance provided by managed service providers. It leads to uninterrupted workflows, allowing organizations to operate smoothly and maintain continuous functionality, ultimately contributing to better customer satisfaction and operational efficiency.

Managed services contribute to enhanced productivity by streamlining operations. Professionals managing the IT infrastructure of an organization ensure optimal performance and efficiency. With systems operating at their best, teams can focus on tasks that directly contribute to the core business objectives, maximizing output and efficiency across the organization.

Security is paramount, and managed services excel in bolstering protection. Expert-guided security measures safeguard critical data of the organizations and systems against potential threats. Regular monitoring, updates, and proactive measures mitigate vulnerabilities, ensuring that business operates in a secure and robust environment.

Efficient load configuration ensures that resources are allocated judiciously, hence improving cost-efficiency. It means that businesses only pay for the computing, storage, and network resources they need, reducing unnecessary expenditure on over-provisioned resources. With optimized resource allocation and the reduction of expenses related to downtime and system failures, businesses will realize substantial cost savings. The proactive approach of managed services prevents costly disruptions and repairs, leading to a more efficient allocation of resources and lower overall costs.

Managed services provide access to a pool of specialized knowledge and skills from professionals who are well-versed in the latest technologies and industry best practices. These subject matter experts ensure that IT solutions of businesses are optimized, effective, and aligned with business objectives. Informed decision-making becomes the norm in the business workflow because of the access to insights that contribute to better strategic planning and implementation.

In the complex landscape of the data-driven business world, the integration of managed services emerges as a strategic decision in the long run. The synergy of technology and methodology converges to fuel efficiency, strengthen security, and enhance operational flexibility. In this realm of constant change, managed services ensure the resilience of systems where operations are streamlined, and workflow is organized.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

In the fast-paced business world, data is the lifeblood that fuels strategic decision-making and drives organizational success. However, even the most seasoned professionals can occasionally find themselves entangled in a web of data quality mishaps.

In the bustling headquarters of a thriving multinational corporation, resided Mr. X, a highly regarded senior manager renowned for his exceptional leadership skills and strategic acumen. With years of experience under his belt, he was trusted implicitly with critical decision-making and the company’s most valuable asset- data. While working on a crucial report to understand the clinical trials data for a specific drug discovery, unknown to Mr. X, lurking within the depths of the data was a discrepancy that was missed during the initial analysis. A minor glitch in data extraction had caused a miscalculation, leading to an inflated projection of data.

As the blunder slowly emerged, the blame fell on Mr. X. The senior manager, once regarded as a beacon of expertise, found himself at the center of a storm, grappling with the harsh consequences of a data quality blunder. In the aftermath, the organization was forced to remove Mr. X from his position, reassess its data governance policies, implement stringent data quality measures, and invest in advanced data analytics tools to prevent such incidents from occurring in the future.

Despite the unfortunate outcome of Mr. X’s experience, his story is not an isolated incident. In fact, data quality issues are pervasive in today’s data-driven landscape, affecting organizations across industries and of all sizes. The implications of data quality mishaps can be far-reaching and devastating, leading to erroneous decisions, lost opportunities, damaged reputation, and significant financial losses. As businesses increasingly rely on data to gain a competitive edge and respond to dynamic market conditions, the need for accurate, reliable, and high-quality data becomes paramount.

Organizations need to develop and implement data quality practices to detect and rectify all data quality issues as early as possible to not treat data quality as an afterthought. Organizations can enable this with tools that can incorporate and embed data quality rules in data pipelines, facilitating the flow of data through an organization’s systems, to ensure consistent high-quality data delivery to data consumers. To implement robust data quality practices, organizations need a tool that provides capabilities such as embedded data quality rules, threshold setting, customized business-specific data quality (DQ) checks, ensure data governance and data quality alerts.

To enable data pipelines to deliver high-quality data for consumption, it is essential to embed data quality rules directly within the pipelines. These rules can include industry-standard checks, such as verifying non-null values, validating date formats, or ensuring data falls within specific ranges. Additionally, organization-specific data quality rules, unique to each business or domain, should be added to the pipelines.

Business rules are specific criteria or conditions set by the organization to define what constitutes good data quality. A good data quality solution empowers the users to customize the business data quality checks. These rules act as guidelines for data validation, ensuring that data adheres to specified business standards. Thresholds, on the other hand, represent the acceptable limits or ranges within which data must fall to be considered valid. If data fails to meet these predefined thresholds, alerts are triggered to notify relevant stakeholders of potential data quality issues.

Data pipelines can be equipped with alert mechanisms to promptly notify stakeholders when data quality rules are not met. Depending on the severity of the data quality issue, different levels of alerts can be configured. For instance, a hard pause can be set to halt the pipeline’s operation until the issue is resolved, or a soft pause can be utilized, allowing the data to continue flowing while triggering an alert for investigation.

Personally Identifiable Information (PII) is sensitive data that can directly or indirectly identify an individual, such as names, addresses, social security numbers, etc. Good data quality and governance processes involve establishing policies, procedures, and controls to manage and protect PII and other critical data assets. A robust governance process ensures data is handled ethically, securely, and in compliance with relevant regulations, while also addressing data quality concerns.

Schema changes or drifting occur when there are alterations to the structure or format of the data. In data quality, it is crucial to monitor schema changes to detect any deviations that might affect data consistency and accuracy. AI-based and ML-driven data quality checks are employed to automate data quality checks, identify patterns, and predict potential issues.

The journey towards impeccable data quality is an ongoing one. Organizations must continuously adapt their approaches to keep up with the evolving data landscape and the emerging technologies that shape it. Organizations should prioritize robust data quality practices. Modern data quality tools, with the ability to incorporate data quality checks, alert mechanisms, industry and organization-specific data quality rules, contribute to ensuring enhanced data quality. As a result, organizations can mitigate the negative impacts of poor data quality, drive better decision-making, enhance customer experiences, and ultimately achieve their data-driven goals. Leveraging data pipelines ensures that poor-quality data does not infiltrate the organization’s data ecosystem, safeguarding the integrity and reliability of valuable data assets.

Modak is a solutions company dedicated to empowering enterprises in effectively managing and harnessing their data landscape. They offer a technology, cloud, and vendor-agnostic approach to customer datafication initiatives. Leveraging machine learning (ML) techniques, Modak revolutionizes the way both structured and unstructured data are processed, utilized, and shared. 

Modak has led multiple customers in reducing their time to value by 5x through Modak’s unique combination of data accelerators, deep data engineering expertise, and delivery methodology to enable multi-year digital transformation. To learn more visit or follow us on LinkedIn and Twitter. 

Modak and SciBite are proud to work together with a joint mission to expedite the generation of insights from research publications, patents, and documents; crucial to advancing scientific discovery.

Modak’s data orchestration platform, Modak Nabu™, enables enterprises to automate data ingestion, curation, and consumption processes at a petabyte scale and within a robust data governance framework. As part of the partnership SciBite’s named entity recognition tool, TERMite, is connected into Modak Nabu™. This connection is made possible by leveraging Almaren; Modak Nabu’s rich connector framework built on Apache Spark.

As a result, TERMite can be run automatically within Modak Nabu™ across on-premise, cloud, and external data sources, allowing for machine-readable FAIR data to be fed to downstream applications.

The integration of Modak Nabu™ with SciBite’s NER capability, TERMite, will empower Life Sciences customers with the ability to:

Modak is a solutions company that enables enterprises to manage and utilize their data landscape effectively. They provide technology, cloud, and vendor-agnostic software and services to accelerate data migration initiatives. Using machine learning (ML) techniques to transform how structured and unstructured data is prepared, consumed, and shared. Find out more at modak.com

Modak Nabu™ enables enterprises to automate data ingestion, curation, and consumption processes at a petabyte scale. Modak Nabu™ is a data orchestration platform, combining data discovery, ingestion, preparation, meta-data repository, unification, and profiling. For more information, visit Modak Nabu™.

SciBite’s data-first, semantic analytics software is for those who want to innovate and get more from their data. SciBite believes data fuels discovery and is leading the way with its pioneering infrastructure that combines the latest in machine learning with an ontology-led approach to unlock the value of scientific content. Find out more at www.scibite.com.

TERMite (TERM identification, tagging & extraction) is at the heart of SciBite’s semantic analytics software suite. Coupled with SciBite’s hand-curated VOCabs, TERMite, can recognise and extract relevant terms found in scientific text. For more information, visit SciBite TERMite.

Data quality is a critical aspect of managing and utilizing data effectively within organizations. Data engineering and DataOps teams play a crucial role in ensuring the integrity, accuracy, and security of an organization’s data assets. In an ideal scenario, data quality issues should be addressed at the source, but this is often challenging in real-world environments. However, data pipelines, which facilitate the flow of data through an organization’s systems, can be enhanced for high-quality data delivery by incorporating data quality checks and rules. This article explores the concept of how embedded data quality checks can help organizations to improve data quality.

Data quality issues can originate from the data source itself, making it essential to identify and resolve these issues as early as possible. The timely identification and resolution of data quality issues significantly contribute to the overall data quality and the effectiveness of teams working with the data. Data pipelines, with their inherent ability to monitor data as it flows, can serve as a proactive mechanism for detecting defects and flaws in data quality.

To enable data pipelines to deliver high-quality data for consumption, it is essential to embed data quality rules directly within the pipelines. These rules can include industry-standard checks, such as verifying non-null values, validating date formats, or ensuring data falls within specific ranges. Additionally, organization-specific data quality rules, unique to each business or domain, should be added to the pipelines.

DataOps teams should have the flexibility to define and configure various data quality checks for each data pipeline. These checks can be customized to align with the specific requirements and characteristics of the organization’s data. By setting thresholds and criteria for data quality, the pipelines can evaluate and assess the incoming data in real time.

Data pipelines can be equipped with alert mechanisms to promptly notify stakeholders when data quality rules are not met. Depending on the severity of the data quality issue, different levels of alerts can be configured. For instance, a hard pause can be set to halt the pipeline’s operation until the issue is resolved, or a soft pause can be utilized, allowing the data to continue flowing while triggering an alert for investigation.

Data quality rules can be categorized into two types: those that apply across the industry and those specific to an organization or domain. Industry-standard rules, like common data formats, can be incorporated into data pipelines universally. Meanwhile, organization-specific rules that reflect the uniqueness of each business’s data should be integrated into the pipelines to address organization-specific requirements.

A survey by Experian Data Quality highlights that 94% of organizations believe they encounter data quality issues, with poor data quality estimated to cost around 12% of annual revenue. Consequently, data practitioners and business leaders recognize the significance of maintaining good data quality. Ensuring data quality is not just a key metric for DataOps teams but is also critical to overall business success.

Data pipelines, with their ability to monitor data flow and apply data quality rules, ensure high-quality data delivery for end-user consumption. By incorporating data quality checks, setting alert mechanisms, and addressing both industry and organization-specific data quality rules, data pipelines contribute to improved data quality. As a result, organizations can mitigate the negative impacts of poor data quality, drive better decision-making, enhance customer experiences, and ultimately achieve their data-driven goals. Leveraging data pipelines ensures that poor-quality data does not infiltrate the organization’s data ecosystem, safeguarding the integrity and reliability of valuable data assets.

Maintaining an accurate inventory of data is crucial, especially in today’s remote work and cloud-based application environment. Organizations today sit on stacks of data, both structured and unstructured, scattered across different locations within the company and in the cloud. Understanding and managing this data is crucial for efficient usage and safeguarding. Having a thorough data inventory is the first step in gaining an understanding of what data an organization owns, where it is located, and how it can be used.

The research firm Gartner predicts that 80% of customers currently do not have an accurate inventory of their data. This underscores the need for organizations to take their data seriously and treat it as a strategic asset.

In this blog, we will explore what data inventory is and how it can benefit an organization’s overall operations and growth.

A data inventory is not just a simple list of data assets that an organization maintains. It is a comprehensive and structured document that provides detailed information about each data source and how it is used within the organization. The data inventory includes metadata such as data ownership, format, location, access controls, data classification, and retention policies.

Data classification is a key component of a data inventory. It involves categorizing data according to its sensitivity, importance, and value to the organization. This enables the organization to determine the appropriate level of protection and access controls that should be applied to each type of data. For example, sensitive data such as financial information or personally identifiable information (PII) may require stronger security controls and stricter access restrictions than non-sensitive data.

In addition to the above, a data inventory should also include information about the relationships between different data sources, such as how data flows between different systems, and how it is transformed and processed. This is important for identifying dependencies and ensuring that data is being used appropriately across the organization.

Overall, a comprehensive data inventory is a valuable tool for managing data assets, improving data quality, and minimizing risks associated with data loss, privacy breaches, or non-compliance with regulations. It also helps organizations to make informed decisions about how to use data effectively and strategically to achieve their business objectives.

Data has become an asset for organizations, with McKinsey research showing that enterprises that are “datafied” are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times more likely to be profitable (ref here). With the growing number of IT systems, companies may have a low level of awareness about where they house sensitive information. Compiling a data inventory is essential for comprehending the value and whereabouts of an organization’s data resources and metadata, which can aid in decreasing risk and guaranteeing conformity with privacy and regulatory requirements.

Data inventory is an important aspect of an organization’s data management that provides immediate visibility into all its data sources, the information they acquire, where the data is stored, and what happens to it in the end. In addition to the benefits mentioned earlier, a comprehensive data inventory also helps organizations comply with regulations such as GDPR and CCPA, which require them to know what personal data they hold and how it’s being processed.

Data inventory can also help organizations manage risks associated with unauthorized access, data breaches, or data loss by identifying and mitigating potential risks. It is an essential part of data governance, which involves managing data to ensure its accuracy, completeness, consistency, and security. With a data inventory, organizations can ensure that their data is managed according to their data governance policies and standards.

A thorough data inventory is a crucial resource for enterprises in the complicated and fast evolving data landscape of today. A complete inventory offers a single source of truth that enables organizations to identify sensitive information subject to rules, locate important but underutilized data, assign tasks, and optimize the value of the data while minimizing risks. Organizations can construct an effective data inventory and utilize data as a strategic asset by establishing a monitoring authority, carrying out routine updates, and employing data mapping. Organizations can be better prepared to make data-driven decisions, retain customers, attract new ones, and boost profitability if they have an accurate inventory of their data.

Modak is a solutions company that enables enterprises to manage and utilize their data landscape effectively. We provide cloud-agnostic software and services to accelerate data migration initiatives. We use machine learning (ML) techniques to transform how structured and unstructured data is prepared, consumed, and shared.

Modak’s portfolio of Data Engineering Studio provides best-in-class delivery services, managed data operations, data mesh, data fabric, augmented data preparation, data quality, and governed data lake solutions.

To learn more, please download: https://modak.com/modak-nabu-solution/

The US Center for Medicare and Medicaid Services (CMS) has taken a step forward in advancing the interoperability and authorization process for the US Healthcare industry by advocating the adoption of the United States Core Data for Interoperability (USCDI) standard. This standard provides a set of health data classes and data elements to be included in patient records for sharing within the health information exchange, allowing insurers and providers to share patient data throughout their healthcare journey. As a result, when a patient wants to compare health plans to switch from one insurer to another, the patient can easily review the options available to make an informed choice, assuming the patient has consented to data sharing.

Healthcare insurance companies, who are custodians of information for millions of Americans, are now required to meet the standards set out by CMS. In addition to this, CMS has also implemented price transparency, enabling consumers to compare insurer plans. The CMS directive allows customers to make informed decisions based on the plans offered. Failure to comply with the CMS guidelines comes with a significant penalty to the insurer on a per member per day basis.

Within this context, a large US Healthcare Insurer set out on a path to extract and process data from disparate internal systems to create the standardized data sets in compliance with the USCDI standard across 25m+ members. The volume of data to be processed was significant, over 500 terabytes, representing approximately 500 billion rows of member records. Working with a leading system integrator the client adopted an incumbent software package to ingest data and use cloud provider big data services to profile and format the data into the common data format and meet the deadline set by the CMS.

However, the client faced massive last-minute issues with the project, incurring cloud processing costs in the hundreds of thousands for a few hours of processing time. And facing the possibility of not meeting the timeline set by the CMS and as a result, incurring penalties.

The client approached Modak on a Friday afternoon to review the approach taken by their strategic System Integrator (SI) and if Modak could provide a solution to (a) resolve the technical issues (b) reduce the cloud costs and (c) meet the timelines set by CMS.

Modak’s leadership and data engineering team spent the week reviewing the cloud services configuration and the code created by the SI. Within the week, the Modak team had re-written the code and demonstrated that the output met the USCIS standard specifications. Further, the cloud processing costs were reduced to a few thousand dollars.

The solution delivered by Modak helped the Healthcare Insurance provider achieve the following:

Modak is a solutions company that enables enterprises to manage and utilize their data landscape effectively. We provide cloud-agnostic software and services to accelerate data migration initiatives. We use machine learning (ML) techniques to transform how structured and unstructured data is prepared, consumed, and shared.

Modak’s portfolio of Data Engineering Studio provides best-in-class delivery services, managed data operations, data mesh, data fabric, augmented data preparation, data quality, and governed data lake solutions.

To learn more, please download: https://modak.com/modak-nabu-solution/

Data leaders are currently facing the challenge of not only managing large volumes of data, but also extracting meaningful insights from that data. In many cases, the connections and relationships between data points are more important than the data points themselves. To effectively analyze and understand complex datasets, organizations need to use graph database technology to capture those relationships.

Many organizations currently rely on Relational Database Management Systems (RDBMS) to store their structured data. However, the fixed and inflexible structure of RDBMS can make it difficult to capture and represent the complex relationships between data points. As a result, these systems are often inadequate.

Graph databases are designed to efficiently store and query connected data by using a node and relationship-based format, making them particularly equipped to solve problems when understanding those connections are critical.

One of the key advantages of graph databases is that they can mimic the way the human brain processes and understands associations. By representing data as nodes and relationships, graph databases provide a more intuitive and natural way of working with connected data.

However, before this data can be analyzed and queried, it often needs to be migrated and prepared for use with a graph database. This process, known as data orchestration, involves cleaning and organizing the data, as well as defining the relationships between different data points.

To fully leverage the power of graph analytics, organizations need to develop a robust data orchestration strategy that ensures their data is clean, organized, and ready to use. This can be a challenging task for many organizations, especially at a large scale.

The data orchestration process often involves a range of activities, such as discovering, ingesting, profiling, tagging, and transforming data. At a large scale, this journey can take months or even years to be completed.

To make the process more efficient, organizations need a modern data platform that can support their data preparation and orchestration efforts. By using graph database technology, organizations can ensure their data is ready for analysis and can be easily queried.

Graph analytics provide a visual representation of data and relationships between data elements. This visualization allows data scientists and analysts to quickly understand the structure and content of their data, and to identify patterns and trends that may not be immediately apparent from looking at raw datasets.

With graph analytics, data scientists and analysts can create visually appealing and intuitive data visualizations using graphs, charts, and maps. This helps effectively communicate and share insights with others and can facilitate collaboration and decision making within an organization.

In addition, graph analytics provide real-time insights into the performance and efficiency of data visualization, allowing the end user to identify and address potential issues before they impact the overall effectiveness of their research.

Ultimately, graph analytics is an invaluable tool for data analysis.

Modak Nabu™ is a modern data engineering platform that significantly speeds up data preparation and improves the performance of analytics. It achieves this by converging a range of data management and analytics capabilities, such as data ingestion, profiling, indexing, curation, and exploration.

Neo4j is a leading graph data platform for building intelligent applications. It is the only enterprise-grade graph database that offers native graph storage, a scalable and performance-optimized architecture, and support for ACID compliance. By using Neo4j, business teams can easily work with connected data and reduce complex and time-consuming queries.

Together, Modak Nabu™ and Neo4j provide a powerful solution for data preparation, visualization, and orchestration, enabling organizations to prepare their data quickly and effectively for analysis using graph technology.

The partnership between Modak and Neo4j brings significant benefits to enterprises across industries. Graph visualization enables faster relationship and pattern discovery in datasets, while the Cypher query language simplifies querying. It yields consumption-ready curated data products, provides self-service data engineering using a no-code/low-code platform, and supports multi-cloud and hybrid-cloud data engineering.

This partnership allows enterprises to take advantage of the powerful data management and analysis capabilities of both Modak Nabu™ and Neo4j, and drive greater business value from their data, lowering costs and accelerating this complex process.

Modak is a solutions company that enables enterprises to manage and utilize their data landscape effectively. We provide cloud-agnostic software and services to accelerate data migration initiatives. We use machine learning (ML) techniques to transform how structured and unstructured data is prepared, consumed, and shared.

Modak’s portfolio of Data Engineering Studio provides best-in-class delivery services, managed data operations, data mesh, data fabric, augmented data preparation, data quality, and governed data lake solutions.

Neo4j is the world’s leading graph data platform. We help organizations – including Comcast, ICIJ, NASA, UBS, and Volvo Cars – capture the rich context of the real world that exists in their data to solve challenges of any size and scale. Our customers transform their industries by curbing financial fraud and cybercrime, optimizing global networks, accelerating breakthrough research, and providing better recommendations. Neo4j delivers real-time transaction processing, advanced AI/ML, intuitive data visualization, and more.

To learn more, please download: https://modak.com/modak-nabu-solution/