Manage Your Organization’s Overall Data Infrastructure with Data Fabric

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Data engineering

Manage Your Organization’s Overall Data Infrastructure with Data Fabric

February 7, 2022

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There is no doubt that next-generation companies are looking to becoming more and more data driven — to future-proof their businesses. These enterprises want to empower their decision-makers with cutting-edge business intelligence and analytics tools, to garner insights from data as fast as possible.

Which product is performing well? In which geography? Why do customers love our product? Can we get to know our customers’ needs better, so we can plan our next product or brand? Are customers satisfied with our offering? What are some risks in our business? Where is customer service falling short?

The answer to each of these questions is buried somewhere in bytes and bytes of data – from all over the company.

This data could be present inside a business application, like an ERP, CRM, etc. It could be in of the many SaaS tools which the company uses to run customer service and help desk operations. It could be hidden somewhere in the financial statements. The point is – there’s tons of data residing on the cloud, on-premise, and even on edge and IoT devices. To answer some of these questions, one data source may not be enough. We need to unify data from multiple sources, before feeding relevant inputs to the analytical model.

To become a truly insights-driven organization (IDO), companies must modernize their data operations or DataOps, as it is referred to these days.

A data fabric – which is a data management architecture that brings together data stored in various sources – is ideal for truly integrating and unifying data from across the enterprise.

Essentially, a data fabric presents a “single source of truth” to data consumers, who are usually business analysts, business intelligence experts and other business users.

A data fabric approach streamlines and drive efficiency into the following DataOps processes:

  1. Data visibility from across the enterprise
  2. Data governance and security
  3. Data access and data quality validation
  4. Overall data management

The data fabric approach helps draw actionable insights to improve problem-solving, compliance, security, and scaling up and down compute power and storage capacity based on need. Consistent capabilities across endpoints in hybrid, multi-cloud environments are made possible by the data services that the data fabric provides. As a result, businesses can standardize their data management practices across edge devices, cloud, as well as on-premises systems. This improves overall end-to-end performance, lowers costs, and streamlines IT operations, making it easier in terms of configuration and infrastructure management.

A Gartner report identifies data fabric as a key trend for 2022 that can help businesses improve their existing infrastructure while automating overall management of data, integrating traditional and emerging ones.

For Data Management Modernization and Integration

Data fabric architecture has become essential for machine-enabled data integration and providing the much-required agility in data management, a crucial element for organizations grappling with diverse, distributed, and complex environments. By integrating it with AI, businesses can also improve the quality and RoI of data management practices.

Using machine and human capabilities, data fabric runs analytics continuously on existing metadata assets that are discoverable and inferenced. This helps in designing, deploying, and utilizing integrated and reusable data across all environments.

By identifying and connecting data from different applications, it helps businesses understand unique and relevant relationships between the different data points that can improve decision-making. Data fabric monitors data pipelines and provides productive alternatives to automate manual tasks, freeing up resources to do more creative tasks.

To know more about Indium’s data engineering expertise

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Benefits of Data Fabric

The constant evolution of business models to keep up with an uncertain economic environment, technological advancement, demanding customers, increasing competition due to globalization, and the vagaries of a global supply chain requires businesses to have access to the latest data in real-time. By implementing data fabrics, businesses can:

  • Maintain disparate data repositories, decentralizing data storage for easy data management
  • Leverage their existing data architectures with greater efficiency instead of rebuilding their applications or data stores.
  • Handle challenges of greater scalability and complexity caused by disparate environments that need to integrate existing and modern applications powered by microservices.
  • Prevent the creation of data silos by different teams and facilitate reuse of enterprise data by different users to suit their needs without affecting data integrity
  • Provide a unified, consolidated view of data stored in the different apps in different formats
  • Deduplicate data
  • Map data from various applications to access the right data at the right time
  • Aggregate data from multiple environments, on-premises, cloud, and hybrid
  • Support preparation of data, quality as well as governance
  • Efficiently integrate data between applications and sources

Learn more on Data Fabrics and Data integration here: Why Data Fabric is the key to next-gen Data Management?

Use Cases of Data Fabric

Data fabric being a flexible architecture, some of its many uses include:

  • Improving Machine Learning (ML) Models: Data fabric provides access to relevant data that can help with improving the learning capability of machine learning (ML). The ML algorithms can monitor the data pipelines, enable quick preparation of data, and fetch relevant data by identifying relationships and integrations.
  • Drawing Customer Insights: Businesses can improve customer engagement by drawing insights from holistic data on customer activities and identifying ways to improve services.
  • Deploy AI Solutions: The data from across the enterprise can be leveraged to develop AI solutions right from fraud detection, enterprise security to improving predictive analytics.
  • Operational Excellence: The holistic view of enterprise data can also help identify ways to automate, improve efficiency and productivity across operations, enabling teams to effect continuous improvement.

Indium — Making Data Work for You

The world of data is vast and confusing, with similar sounding words and concepts floating freely. Businesses can find it difficult to choose the right data architecture that can help them achieve their business objectives. Indium Software, which has more than two decades of experience in developing next-generation data engineering solutions, can help you streamline your DataOps and BI/analytics workflows.

Indium’s data engineering services include the following:

  • Build data pipelines (ELT and ETL)
  • Build data products for internal use, with necessary APIs
  • Ensure data quality
  • Design and build the architecture for DataOps
  • Data warehousing, Data virtualization and Data migration

Till date, we’ve worked with customers across the globe across several sectors including BFSI, education, manufacturing, retail, and technology, among others.


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