Transitioning to Data Fabric: Enhancing Big Data Management Strategies

Date : July 7, 2023

Company : Intellectus Corp.


In this article, we acknowledge the current processing problems of traditional database management systems that are not able to handle large sets of data. Managing big data is complex as it involves enormous volumes of data which can be generated over time or occur quickly in real-time such as social media feedback or sensor data from IoT devices. While big data can be structured and well-defined in a database, it may also be unstructured, thus complicating the data management process.

Consequently, we introduce a new modern approach to big data management using data fabric technology, which allows an enterprise’s existing data to remain in place yet gives businesses the ability to extract needed information to gain valuable insights, optimize operations, and drive real-time informed decision-making from all data sources.


Data fabric in comparison to data lakes and data warehouses, showing its adaptability to all Workloads and how it can process real-time data.

The concept of a data fabric revolves around integrating and connecting data from various sources and then making it accessible through a unified view. This process involves first integrating diverse data sources such as legacy databases, data warehouses, cloud services, or streaming platforms by using connectors such as APIs and data virtualization to map data from their sources so they are viewable in a unified environment.

Once the data layer exists, businesses can access and query the data as if it was coming from a single source.

1. Workroads, Data discovery & Insights

Utilizing data fabric technology, businesses that require massive data processing power are becoming more productive and profitable as they can process diverse data types, automate integration, and manage metadata.

2-1. Data Fabrics Reduce Workloads through Automation

A study conducted by PwC shows that Data Fabric Techniques decrease the amount of time spent on completing tasks [1].

Data fabrics leverage AI and machine learning to reduce the data management workload by automating tasks such as data integration, data preparation, data cleaning, and cataloging of new data sources.

Furthermore, a data fabric’s AI capabilities can be used to identify and fix data quality issues by automatically detecting anomalies or missing values.

Through automation, data fabrics also eliminate repetitive work, thus freeing up time for employees to focus on more complex tasks. By increasing workflow efficiency, companies save not only on labor costs but on operational costs as well.

According to research by PwC, even the most rudimentary data fabric automation techniques can save businesses up to 40% of the hours typically spent on such processes. [1]

2-2. Data Fabrics Produce Data Democratization

Statistical data from McKinsey & Company on Data Accessibility and its impact on Revenue Growth [2]

Data fabrics utilize metadata, machine learning, and automation to seamlessly connect and enable discovery and utilization of the data for the entire workforce irrespective of the employee’s technical know-how.
Known as data democratization, all employees are empowered to work closely with critical data necessary for them to complete their jobs more effectively and efficiently.

For example, doctors may use natural language processing (NLP) tools to quickly find and extract relevant information from research papers or medical records, lawyers are able to scan thousands of pages of documents on case law to help with arguments, and human resource associates can rapidly review thousands of resumes to identify key traits of employees they are seeking to hire.

2-3. Data Fabrics Enable Data-Driven Analytics

Statistical data from McKinsey & Company on the impacts of Data Fabric on EBIT [3].

Data fabrics support advanced analytical techniques, including statistical analysis, machine learning, and predictive modeling, which enables organizations to gain data-driven insights.

That is, data fabrics can provide libraries, frameworks, or interfaces that facilitate the implementation of advanced analytics algorithms. In the business world, this functionality helps uncover correlations in an enterprises’ integrated data such as seasonal purchasing patterns in various industries or prediction of trends in the data.

Using algorithms and data models that improve as they are given more data, data fabrics become scalable allowing businesses to quickly adapt at handling larger amounts of data.
With data fabric’s predictive abilities, businesses can better understand upcoming market trends or forecast demand and supply trends in their industry.

In fact, according to a report from McKinsey & Co, companies with the greatest overall growth in revenue and earnings receive a significant proportion of that boost from data and analytics that utilize data fabrics.

2-4. Data Fabrics Enhance Data Visualization

Data fabrics provide businesses with user-friendly visualizations of vast quantities of data, enabling interactive and real-time visualizations through tools like Tableau or Power BI. This includes charts, graphs, dashboards, and reports that facilitate the analysis of data patterns, trends, and outliers.

Data visualization is crucial for organizations as it makes complex data engaging and understandable, enabling quick comprehension of trends and relationships.

In fact, studies show data visualization in a graphic is processed 60,000x times quicker than plain text. Furthermore, visuals are said to increase learning and information retention by 78%. [4]

2-5. Data Fabrics Provide Data Enrichment

Data fabrics provide seamless access and querying of data from various sources across multiple devices and cloud platforms. By integrating platforms that offer data-as-a-service (DaaS), firms may combine proprietary data with external data sources, unlocking rich insights.
According to Statista, the market for DaaS is projected to reach $10.7B in 2023, highlighting its growing importance in the industry. [5]

Year over Year (YoY) growth of the DaaS Market Worldwide [5].

2-6. Data Fabrics in Action: Industry Focus

Five potential industries that Data Fabric technology affects.

Almost every industry can benefit from data fabrics, allowing them to focus on and improve their own organization’s business rather than worrying about the day-to-day management of their data.

  • Banking Industry - Data fabrics enable seamless integration of banking data, providing banks with a comprehensive view of customer transactions and banking operations. This allows for personalized banking experiences, enhanced customer satisfaction and loyalty. Real-time processing and analytics facilitated by data fabrics enable transaction monitoring, fraud detection, compliance streamlining, and risk mitigation. Additionally, customers gain real-time access to view credit card transactions and pay bills online, enhancing satisfaction and reducing labor costs associated with manual customer assistance.

  • Healthcare Industry - Implementing a data fabric in healthcare organizations enhances patient care by providing comprehensive views of patient data from various sources. This enables timely diagnosis and personalized care by doctors. Data fabrics improve the Revenue Cycle Mgmt. process by facilitating online patient activities such as appt. booking, pre-authorizations, online paperwork, test result access, & bill payment. As healthcare providers can focus more on patient care, the administrative burden is reduced, leading to increased efficiency and customer satisfaction.

  • Energy / Utilities Industries - Companies in the energy and utilities industry deal with extensive data from smart meters, power grids, sensors, and environmental monitoring. Therefore, data fabrics are essential at integrating the data and facilitating real-time monitoring of energy generation, distribution, and consumption, allowing operators to optimize performance and respond promptly to any issues or disruptions. Furthermore, data fabrics can integrate renewable energy sources by aggregating data from various renewable generation systems, weather conditions, and grid capacity, which can help to optimize energy production and reduce waste.

  • Logistics Industry – Data fabric technology plays a crucial role in the logistics industry by integrating data from various sources involved in transportation, storage, and distribution processes. It provides a unified view of data from suppliers, carriers, customers, and different systems like TMS, WMS, and IoT sensors. With data fabric, logistics businesses can analyze inventory levels, optimize the balance between customer demand and supplier performance, and reduce excessive inventory costs. It also helps identify efficient transportation routes, improve delivery timelines, and lower expenses.

  • Retail & E-commerce – By leveraging the capabilities of a data fabric, retailers and e-commerce businesses can improve customer engagement, optimize operations, enhance supply chain efficiency, and drive revenue growth. A data fabric also helps retailers optimize inventory management by integrating data from multiple sources, such as sales data, supply chain information, and demand forecasts. Retailers can analyze this data to improve demand forecasting accuracy, ensure optimal stock levels, reduce stock outages, and minimize overstock situations. This leads to improved inventory turnover, increased sales, and reduced carrying costs, saving businesses money.

3. Strategies for data intergrity & security

While a data fabric’s integrated AI, ML and data visualization capabilities greatly help businesses to automate tasks, predict trends, and gain data driven insights into their businesses, organizations must also consider the importance of properly managing the integrity and security of their company’s data, all of which can be handled by data fabric technology.

3-1. Data Fabrics Support Governance and Regulation

Image captioned “Data Governance.”

Data governance is the process of establishing policies and procedures to ensure the quality, integrity, and security of a company's data.

It involves defining a strategy, setting data management policies aligned with business objectives, and overseeing the process through data governance teams and data stewards.
As businesses face new data privacy regulations, implementing measures to protect data and ensure compliance with relevant data protection regulations, such as GDPR, PIPEDA, and PIPL, becomes vital for companies.

Analysts predict that by the end of 2023, approximately 65% of the world's population will be covered by such regulations, emphasizing the importance of governance globally.

3.2 Why Data Governance Matters

Overall, data governance is essential for organizations to maximize the usage of their data. Without proper management, data inconsistencies could occur in different systems across the organization.
Data governance ensures regulatory compliance, improves accessibility to essential data, reduces management costs, and enhances data quality.
By leveraging data fabrics, organizations enhance the decision-making process, leading to increased revenue and business success.

3.3 Data Lineage Tracking Enhances Data Governance

Data fabrics play a vital role in supporting data lineage reporting and auditing by capturing and maintaining information about data origin, transformation, and usage.
This establishes transparency, accountability, and supports data governance.
Data lineage helps identify data sources, detect errors, and navigate complex scenarios such as mergers or acquisitions with different data structures.

3.4 Data Virtualization Improves Data Governance and Security

Data virtualization solutions improve a businesses’ data governance and security as it integrates data access through a virtual data layer that provides a unified and consistent view of the data ensuring data accuracy, enforcing security, and facilitating data provisioning. 

This process eliminates the need for physical data replication or ETL (Extract, Transform, Load), which can introduce data quality issues and make data governance challenging.

As data virtualization acts as a single-entry point layer between the data sources and the end consumers, this provides a point of control for monitoring and enforcing who, how, and when users can access the data. 

As governmental regulations require businesses to follow certain privacy and confidentiality rules, data virtualization’s unified access layer becomes an important tool for data governance programs that need to comply with both government and industry regulations.

Image depicting what the Data Virtualization Layer looks like.


Data Fabric technology enables firms to integrate and maximize the potential of their enterprise data. By providing a unified view of data from various sources, data fabrics eliminate complexity and ensure data quality.

By utilizing analytical and visualization tools that are enhanced through the data fabric, companies can quickly analyze business trends, make predictions, and improve their operational efficiency.

Data governance, security measures, and compliance with government and industry regulations are simplified through data fabrics.
Businesses can establish data access controls, mask sensitive information, and complete auditing and tracking of the data’s movement within the data fabric.
With the ability to process and analyze real-time information, it would be prudent for all organizations to consider implementing data fabrics to leverage their enterprise’s data effectively and gain a competitive edge.


[1] Bernard, R. & Rao, A. (2021) It’s time to get excited about boring AI. PwC Strategy + Business.

[2] Marr, B. (2023). The top 5 data science and analytics trends in 2023. Forbes. bernardmarr/2022/10/31/the-top-5-data-science-and-analytics-trends-in-2023/?sh=3a194c6c5c41

[3] Survey. (2021). The state of AI in 2021. McKinsey & Company. our-insights/global-survey-the-state-of-ai-in-2021

[4] Mandal, R. (2022). 50 data visualization statistics that prove its important. Visme.

[5] Taylor, P. (2022). Size of the data as a service (DAAS) market worldwide from 2018 to 2023. Statista.

[6] Goasduff, L. (2020). Gartner says by 2023, 65% of the world’s population will have its personal data covered under modern privacy regulations. Gartner.