From Data Modernization to Data Monetization: Make the Play for Better Business ROI


This article was written by Sriram Sitaraman, Director of Technology at Material


People, devices and data. These three entities are no longer monolithic in nature and don’t exist independently. They operate as an extension of each other and are intricately intertwined. The deluge of data that has been generated from millions of people and devices has spurred the fanatical demand for cutting-edge data landscapes and analytics. To derive any business outcome, organizations must be able to make sense of such unstructured data and that data needs to be processed or refined, governed and enabled for obtaining insights.

No matter their size, most organizations are in the process of becoming more data driven and are consolidating their data silos, such as data warehouses, data lakes and analytics applications, in the cloud. The consensus is unanimous and universal – it certainly pays to be a data-driven organization. This is where data modernization plays a pivotal role, since it cuts across various data dimensions:


Essential Building Blocks of Data Modernization

Data modernization is not just limited to breaking silos in a business or moving core competencies to the latest and greatest cloud technologies. It’s also about leveraging the incredible amounts of enterprise data to have a positive impact on the bottom line of a business. The key parameters that constitute a successful data modernization plan are:

  • Easily available information – Data, in its rawest form, doesn’t serve any real purpose for an organization, particularly if it’s not easy to access. Business stakeholders need to have easy and quick access to unadulterated and consolidated information to make well-informed, fact-driven decisions. This kind of information should be made securely available for different stakeholders and across different devices.
  • Well-entrenched platforms – Fully functional data platforms should provide a harmonious user experience, economical scaling, on-demand performance, powerful AI and analytics features and so much more to ensure an organization’s potential to flawlessly leverage data in a secure and flexible manner. Such platforms must drive, instead of restrict, operational effectiveness and innovation.
  • Vigorous data governance – Data-driven organizations and their systems require agile management and controls for the administration, application and integrity of enterprise data. Data governance defines and establishes the processes and responsibilities that guarantee the integrity and security of the data being used across the organization. It sets in stone the kind of actions that a business stakeholder can take, the type of data that can be leveraged, the circumstances that dictate such usage and the methods that can be used.
  • Airtight data security – As one of the most crucial and prized assets of an organization, data must be secured and protected. Data security is not limited to only digital protection for dormant or active data, but also includes physical security, business processes that prevent unsanctioned access, management of personally identifiable information and incident response procedures.


The appropriate systems and processes must be in place to ensure that data is the catalyst behind business decision-making, reduction of costs, saving of workforce time and effort and so much more. Data modernization is just the beginning; the next step in the transformation journey is to figure out how to extract value from an organization’s enterprise data.

Kick your Data Modernization ROI into High Gear

Technology-driven modernization blends data integration and management with AI, subsequently allowing organizations to reap the benefits of intelligent data management through the cloud. That blend is vital in leveraging the cloud to grow beyond old data integration methods that have no place in meeting today’s business goals.

AI-powered intelligence is essentially the cutting-edge, holistic data management foresight that organizations need. This will help them get the better of highly splintered, manually-dependent data preparation and data pipeline creation methods as well as transformative processes that lead to sluggish data life cycles, preventing them from attaining true data ROI.

Here are some of the ways that organizations can implement to achieve significant ROI traction with intelligent and comprehensive data management.

  • Break down data warehouse and data lake silos with cloud-native services – Organizations need to take a step back and assess different options that can help form a unique, consolidated data architecture that exploits intelligent, cloud-native data management. As a result, they can sustain different kinds of workloads, at any given latency or scale, and on any cloud for all types of users. This could involve the merging of data silos for streamlined data management and access.
  • Refine data quality and precision through metadata insights – The data that provides insights about data is called metadata. It enables organizations to govern data better, enhance data quality and hasten the incorporation of new data. AI-powered intelligence and cloud-native competencies allow organizations to automate and expand metadata detection and management. Metadata falls under four categories: technical, business, operation and infrastructure.
  • Provide integrated data management to ensure robust and comprehensive data pipelines – All types of workloads need to have sturdy and exhaustive data pipelines and production processes; for example, business intelligence (BI) dashboards, AI/ML development, data science, etc. Organizations can initiate automated data pipelines to ensure real-time, large-scale data ingestion from varied sources into data lakes. They require an end-to-end data platform to easily and keenly observe and organize various active data pipelines at different levels of ingestion (profiling, data cleansing, and transformation). Organizations can supply data much faster for different workloads at the same time, leading to increased productivity and user satisfaction.
  • Augment data governance and assurance with consolidated data integration and metadata intelligence
    Organizations have to make sure that data governance processes make use of metadata assets efficiently. Also, the span of their metadata should extend across the complete hybrid, multi-cloud environment. Significant amounts of time and manual effort required to update data lineage records can be saved with AI-powered automation enabled in a consolidated data integration system.
  • Strike a balance between stability and agility
    Organizations across the globe have been able to successfully implement Agile methods and frameworks (DevOps, DataOps and MLOps) to remove the limitations of data silos and manage far-reaching development and operationalization. Apart from technology and development best practices, these methods place prioritize effective communication and collaboration between stakeholders from business, IT, data science and development teams, which can fast-track the development and deployment of data pipelines during the data life cycle.


Data Monetization: Making Bank with Enterprise Information

Competitive and inventive organizations are always on the lookout for new ways and methods to increase their success in the world of business. With the arrival of cutting-edge data and analytics technologies, data is considered a strategic and constantly evolving asset, which can successfully pave the way for new opportunities for revenue generation.

Enterprise data lakes, data warehouses and various other platforms are filled with data assets that can be effectively and continuously monetized. Data monetization is the process through which organizations pivot their data assets to achieve new fiscal benefits, such as new products or services, process improvements that lead to additional revenue and reducing operational costs. According to a Grand View Research report, global data monetization (valued at $1.3 billion in 2019) is projected to grow at a CAGR of 24.1% from 2020 to 2027.

Here are the common ways that organizations monetize their data:

  • Data distillation – Enterprise data, analyzed data or any kind of assets gleaned from data can be sold or licensed. The data (either raw, refined or summarized) can be sold as independent records, a collection of records or as part of a bigger collection of software, services and products. Additionally, it can be offered as a one-time package of data or even as a subscription.
  • Data productization – Organizations can use their enterprise data or data assets to create revenue-generating products or services, which they can sell, license or provide on a subscription basis to external customers.
  • Data operationalization – Enterprise data can also be used by organizations to positively impact business outcomes via data-driven processes or activities to substantially increase revenues and reduce costs.


Organizations must step up their game by merging wayward, siloed data and invigorate their technology-driven data integration and management practices.

On the flip side, enterprise users demand quick and secure access across different touchpoints in diverse systems, which include multiple cloud platforms. They need access to real-time data insights to quickly arrive at decisions that impact both short-term and long-term business goals.

There is more to data ROI than meets the eye

Revitalizing your data landscape is crucial not only to address constantly changing business requirements and keep up with the demand for business-driven analytics, but also to remain relevant in the market, now and in the future. As users gain more acumen about data, they will lean heavily on trusted data sets and expect the blending of new data sources to provide different viewpoints and obtain contextual comprehension. While several methodologies are available to inspect, clean, transform and model the data sets, we recommend these:


Data serves as the cornerstone of an organization’s internal and external collaboration and can help stakeholders in a business make well-informed and synchronized decisions, particularly when unforeseen business challenges arise.

As a data modernization partner, we can help you navigate through uncharted data landscapes, assess your current data architecture and help you adopt a more agile, streamlined and responsive data ecosystem.