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Seth Rao

CEO at FirstEigen

How to Build Data Infrastructure: Strategies and Platforms for a Robust System

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      Building a robust data infrastructure is essential to survive in today’s ultra-competitive environment. Do you know how to create an efficient and effective data infrastructure? There are several vital steps to take, starting with defining your data strategy and cleaning your data for the highest possible data quality.

      Quick Takeaways

      • A robust data infrastructure enables secure and efficient data consumption
      • Building a robust data infrastructure requires defining your data strategy, building a data model, choosing a data repository, cleaning and optimizing the data, building an ETL pipeline, and implementing detailed data governance
      • The amount of data, its accessibility, and its quality make maintaining data infrastructure a challenge

      What is a Data Infrastructure?

      Technopedia defines data infrastructure as a type of digital foundation that enables the sharing and consumption of data. A robust data infrastructure includes several key elements, including:

      • Data ingestion
      • Data storage
      • Data security
      • Data retrieval 
      • Data analysis

      The right data infrastructure enhances employee productivity and collaboration while enabling easy yet secure access to key data. The appropriate data infrastructure should reduce an organization’s operational costs while increasing employee efficiency. 

      Why Do We Need Data Infrastructure?

      In today’s data-driven world, businesses generate and use vast amounts of information. A robust data infrastructure helps organizations:

      1. Ensure Reliable Decision-Making: Accurate data supports informed strategies.
      2. Enhance Collaboration: Unified data systems prevent silos and foster teamwork.
      3. Improve Data Security: Protect sensitive information with built-in safeguards.
      4. Support Scalability: As data volumes grow, infrastructure ensures continued efficiency.
      5. Ensure Regulatory Compliance: Align with GDPR, HIPAA, and other standards.

      Without proper infrastructure, organizations struggle with disorganized, inaccessible, or low-quality data, hampering overall performance.

      Eliminate 80% of Manual Checks with Automated Data Monitoring

      Key Steps for Building a Robust Data Infrastructure

      Building a robust data infrastructure involves six key steps – starting with defining your overall data strategy.

      How to build a robust data infrastructure.

      1. Define Your Data Strategy 

      The first step in establishing a robust data infrastructure is determining your organization’s needs. This typically involves performing a detailed data audit, where you identify:

      • What data you collect
      • Where that data comes from (different systems and databases)
      • What form the data currently exists in (structured vs. unstructured, etc.)
      • How the data is currently secured
      • Who needs access to that data – and why

      This information will help you define your overall data strategy and guide you through the remaining process of building your new data infrastructure.

      2. Build a Data Model

      The data model defines how your data will be structured and will reflect the type of data you create and ingest and how that data will be used. There are three types of data models in which you may engage:

      • Conceptual, which defines high-level business structures and operations
      • Logical, which defines different classes of data, their attributes, and the relationships between them
      • Physical, which defines internal database tables, columns, and other schema

      Your organization may work exclusively with one data model or utilize all three in successive project stages. 

      3. Choose a Data Repository

      Not only do you have to define your data structure, but you also need to decide what type of data repository to use and where to house it.

      There are three primary types of data repositories you can choose from:

      • Data lake, typically used to store raw, often unstructured data
      • Data warehouse, typically used to store structured and filtered data
      • Hybrid approach, which includes elements of both a data lake and a data warehouse
      Data lake vs data warehouse.

      You also need to determine whether you’re going to host your data repository on-premises, on your servers, or in the cloud at a cloud-hosting service. While on-premises data storage puts the entire operation under your internal control, it isn’t as flexible as cloud-hosted storage, enabling easier and more secure remote access. It’s not surprising that 50% of all enterprise data is currently stored in the cloud.

      4. Clean and Optimize Your Data

      If you choose to create a data warehouse, you need to clean and optimize the data as it is ingested into the structured database. If you create a data lake, you need to clean and optimize data as it is retrieved. In either case, data quality management software is essential to ensure that the data you ultimately use is accurate, complete, and up-to-date. 

      The more data you manage, the more difficult this process becomes, which is why many organizations utilize automated data monitoring tools, such as FirstEigen’s DataBuck. This tool monitors data not just during ingestion but throughout the data lifecycle to eliminate errors from being introduced and ensure more usable data. 

      5. Build an ETL Pipeline

      Moving data from other systems into the data warehouse or data lake involves the extract, transform, and load (ETL) process. The ETL process is where a standard structure is imposed on your data, making it more organized and ultimately more accessible. 

      The ETL process.

      The ETL process works like this:

      • Data is extracted from multiple sources – CRM systems, data lakes, other databases, etc.
      • Data is transformed and processed into the standard data model
      • Data is loaded into the target database

      Your ETL pipeline must be secure against data leaks that affect the quality of data feeding into the ultimate data repository. Losing just 0.25% of the data transferred can affect the quality of later analysis. 

      6. Implement Data Governance

      Once the data is loaded into your infrastructure, you need to implement detailed data governance that defines how that data is managed and controlled. A data governance framework typically deals with the following issues:

      • Data quality
      • Data availability
      • Data usability
      • Data integrity
      • Data compliance
      • Data security

      Proper data governance results in numerous benefits to the organization, including: 

      • Improved compliance
      • More consistent operations
      • Enhanced security
      • Defined audit trail
      • Improved disaster recovery

      Challenges for Building and Maintaining Data Infrastructure

      Every organization faces some key challenges when building and maintaining a data infrastructure. These challenges can be overcome with the right data management solution but must be addressed proactively. 

      1. Data Volume

      Businesses today generate an overwhelming amount of data. According to IDC, the amount created each year increased from 15.5 ZB in 2015 to 64.2 ZB in 2020. That number is expected to increase by 23% a year through at least 2025. 

      Dealing with all this data is a tremendous challenge. The more data your organization creates, the bigger the strain on your data infrastructure. It’s a significant challenge for your IT staff not to get buried under this ever-growing flow of data. 

      2. Data Accessibility

      The more data you collect, the more difficult it is to find and retrieve specific content, especially if it is not properly stored and organized. Data that is siloed in different parts of an organization can be hidden from employees who need to use it. Data that is improperly labeled or filed can be almost impossible to find. With so much data coursing through your organization, finding that one document you need is often like searching for a needle in a haystack.

      3. Data Quality

      Even properly stored and organized data loses its value if it isn’t of high enough quality. According to Gartner, 20% of all data is compromised because of its poor quality, rendering it unusable.

      To provide the most benefit to an organization, data must be:

      • Accurate
      • Complete
      • Consistent
      • Timely
      • Unique
      • Valid

      This is true of newly created data, and data from other sources ingested into an existing data infrastructure – and is why data quality management needs to be a part of a robust data infrastructure solution. 

      Future Trends in Data Infrastructure

      To future-proof your data infrastructure, consider these emerging trends:

      • AI and ML Integration: Automating data quality checks and analytics.
      • Edge Computing: Enhancing real-time data processing at the source.
      • Data Fabric: Connecting disparate data sources for a unified view.
      • Blockchain: Securing data sharing with immutable records.

      Discover How DataBuck Reduces Data Errors by 90%

      Let DataBuck Help You Build a Robust Data Infrastructure 

      High-quality data is essential to the usability of any data infrastructure. When you need to ensure the highest possible data quality, turn to DataBuck from FirstEigen. DataBuck is an autonomous data quality management solution that automates more than 70% of the data monitoring process. Our data management system ensures that the contents of your data infrastructure are complete, accurate, and timely.

      Contact FirstEigen today to learn how you can use DataBuck in your data infrastructure plans.

      Check out these articles on Data Trustability, Observability & Data Quality Management-

      FAQ

      1. What is data infrastructure?

      Data infrastructure is the system and tools that manage and process data within an organization. It includes hardware, software, networks, and protocols that store, retrieve, and analyze data, ensuring it’s accessible and usable for decision-making.

      2. How do you build a scalable data infrastructure?

      To build a scalable data infrastructure, start by using cloud-based solutions for flexible storage and processing. Implement a modular design that allows easy expansion. Focus on automation to handle large datasets efficiently, and choose tools that support distributed computing.

      3. What are the key components of a robust data infrastructure?

      Key components include data storage systems, processing units, networking, and security protocols. It also involves tools for data management, integration, and analytics, ensuring that data is well-organized and accessible for business use.

      4. What role does big data play in data infrastructure?

      Big data requires infrastructure that can handle large volumes of information in real time. It uses advanced storage solutions, distributed processing systems like Hadoop, and strong analytics tools to make sense of massive datasets and drive business insights.

      5. What is a data infrastructure strategy, and why is it important?

      A data infrastructure strategy is a plan for managing and using data systems effectively. It’s important because it helps businesses align their data practices with goals, ensuring that data supports decision-making, scalability, and security.

      6. How to choose the right data infrastructure platform?

      When choosing a data infrastructure platform, consider factors like scalability, ease of integration, and security features. Look for platforms that support your data volume and processing needs while providing reliable performance and strong data governance tools.

      7. What are the challenges in building a data infrastructure?

      Common challenges include ensuring data security, managing large and complex datasets, integrating various systems, and maintaining performance at scale. Data governance and compliance with regulations are also key concerns.

      8. What are the best practices for maintaining data infrastructure?

      Best practices include regularly updating software, monitoring system performance, and ensuring data security through encryption and access controls. It’s also essential to back up data frequently and conduct regular audits to maintain data quality.

      9. What is the difference between traditional and modern data infrastructure?

      Traditional data infrastructure relies on on-premise servers and fixed storage systems, while modern infrastructure often uses cloud-based solutions, allowing for greater flexibility and scalability. Modern systems also integrate advanced tools for big data and real-time processing.

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