What is Data Infrastructure
Many firms have a wealth of data. However, most of them are scattered across systems and in silos. Business functions have a tough time in the access and analysis of this data to make any decisions. Even with teams of data analysts, they are often hindered by slow processing times and inconsistent data quality. What companies must realize is that leveraging data is key to business growth and gaining a competitive edge. The answer is to have a reliable data infrastructure. Data infrastructure definition can be understood as follows – It is the foundational technology and architecture which enable the storage, management, and processing of data. It can consist of hardware, software, servers, storage devices, database infrastructure, analytical & business intelligence tools and networking resources to support data storage, data processing and data analysis. The infrastructure can ensure a centralized view of data making it easy for every business function to collaborate and share insights. The data will remain secure and compliant while remaining available for business use. Decisions can be made quickly and more accurately due to up-to-date information. There is less time spent of data acquisition and analysis, including proper data governance. Data is available on-demand to analyze customer behavior and market trends, which can help in sustaining competitive advantage. Big data infrastructure is critical in today’s data-driven world and must be custom-built based on the needs and requirements of the company. It is an enabler for innovation and customer experiences and requires maintenance, monitoring, and optimization on a regular basis.
What does Data Infrastructure Include
A comprehensive, well integrated big data infrastructure is critical for a company’s growth in the digital age. It should have the data infrastructure tools and processes to integrate data from various business functions. By integrating data, stakeholders can get a unified, comprehensive view of data for identifying opportunities for improvement or growth. It should have processes to improve data quality, such as data cleaning, validation, and enrichment. Data infrastructure also ensures data governance by establishing data access controls and compliance. Stakeholders will be able to derive insights from data using analytics and reporting tools to know the market trends, optimize operations and enhance decision making. Just in case of a breach or system outage, the infrastructure will ensure the data is restored, and there is business continuity. It is also recommended to have a cloud-based data infrastructure as it can be scaled up or down based on business needs, while handling large amounts of data, it has strong data governance and data security protocols reducing data breach risks, and more importantly, it almost eliminates the need for investments in hardware and software to manage database infrastructure, data warehouses, and data management systems.
Why do we need Data Infrastructure
Data infrastructure establishes data quality, data accuracy and data consistency. This helps in making informed decisions. Data can be integrated from various sources, ensuring there are no silos of data. There is better data security mitigating risks of a malicious breach or unauthorized access. A cloud-based data infrastructure will bring scalability to meet any growing volumes of data as the business grows. The crucial aspect of having data infrastructure is that due to the availability of AI-enabled data analytics and reporting tools, businesses can get insight into customer behaviors and preferences, identify market trends and patterns and optimize their operations accordingly to drive growth. There is improved compliance with regulations such as GDPR and CCPA, including better flexibility and cost savings due to the cloud infrastructure. Moreover, it offers a single unified source of truth for the data, and there is a low risk of any data errors leading to increased productivity, innovation, and better decisions. ROIs can be tracked, ensuring the spending is optimized for marketing campaigns and while developing products and services, enabling companies to track and measure investment success through the financial year. As data infrastructure forms the foundation of any data-driven organization, a data infrastructure engineer is equally responsible for the data infrastructure design, deployment, and maintenance of a secure and reliable data ecosystem. A few data infrastructure examples include Amazon Web Services (AWS) Elastic MapReduce (EMR), Microsoft Azure Synapse Analytics, Google Cloud Bigtable, Snowflake Data Cloud, Apache Kafka, and Cloudera Data Platform.
Challenges in Building a Data Infrastructure
The complexities of building a robust data infrastructure. Here are some challenges that need to be surmounted to realize the full potential of data.
- Ensuring data accuracy, consistency, and completeness is a common challenge
- Another complex undertaking is establishing processes and policies for data management
- Integrating data from multiple sources and systems can be time consuming, especially with legacy systems
- Data storage and management of large data volumes are expensive
- Data security needs advanced technologies to ensure resilient security protocols & to provide the right data to the right people at the right time
- Meet data privacy regulations such as GDPR and CCPA is not an easy task for companies dealing with customer data
- Scalable data infrastructure is only possible through a cloud infrastructure which requires specialized technical expertise for data infrastructure design and deployment
- Cloud infrastructure integration with legacy systems is challenging and needs diligent planning and execution
- Institutionalizing a data-driven culture in the organization needs requires a paradigm shift in employee mindset
- Implementing a data infrastructure need changes in existing processes, and employee roles and responsibilities
- The cost of building a data infrastructure is high
- Overcoming data silos is not easy for organizations having a fragmented data landscape
- Ensuring the efficiency of a data infrastructure can be difficult if the KPIs and success metrics are not properly defined
How to Build Modern Data Infrastructure: Tips and Best Practices
Here are tips and practices which can be used while designing and building a reliable data infrastructure. Have clarity on your goals, objectives, and the KPIs you intend to achieve, and make sure they are aligned with business needs. Choose the data resources, both internal and external, that you need. Establish a data governance model with the required processes and policies to meet access and security needs. Ensure data is of high quality by maintaining accuracy, completeness, and consistency. Pick the right technology portfolio which fits your needs and budget. Always opt for cloud-based data infrastructure to leverage cloud features such as scale, flexibility, and reduced costs. Have a centralized data warehouse capable of unifying data from several sources. Utilize data modeling approaches to structure data. Adopt tools and methods that can enable data analytics and data visualization. Automate data processing to reduce mistakes. Encourage a data-driven culture to motivate employees to use and value data for decision making. The impact of your data infrastructure must be measured through clear success metrics and KPIs to ensure it is meeting your business objectives.
Best Tools for Modern Data Infrastructure Automation
A modern data analytics infrastructure can be automated to make it more efficient, reliable, and scalable. Here are some of the data infrastructure tools for data infrastructure automation. Apache Airflow is a platform to author, schedule, and monitor workflows programmatically. Jenkins is an automation service to automate tasks such as building, testing, and deploying software. Ansible, an open-source tool, can be used for the automation of configuration management and application deployment. Puppet is to automate the deployment and management of infrastructure and applications. For automating, deploying, scaling and managing containerized applications, Kubernetes can be used. Docker can be used for packaging and deployment of applications as containers, and Terraform, to build, change, and versioning infrastructure. For the cloud infrastructure AWS CloudFormation, Google Cloud Deployment Manager or Microsoft Azure Resource Manager can be used. Grafana can be used for data analysis and visualization, and Elk Stack for data collation, process, and analysis of log data. Chef is used to automate infrastructure configurations, and Nagios is suitable for monitoring the health of data infrastructure and applications.
How to get started with data infrastructure
The business objectives must be clear and ensure you know how data should be used to achieve them. Involve the key stakeholders in the planning process. Do a critical analysis of the existing state of your data infrastructure and find the gaps for improvement. Design a roadmap on how to deploy the data infrastructure. Find a data management system that fits your company’s needs. A data catalog will be of help to organize data assets. Establish a data ingestion process to capture data and implement a data processing framework to make data fit for analysis. The storage and data retrieval architecture must be designed in such a way that it meets the performance and scalability needs. Design the architecture with security in mind – this is best done by implementing data governance policies and procedures to meet data privacy and security needs. Also, choose the data visualization tools that enable users to comprehend data easily. Monitor the effectiveness of your data infrastructure and ensure it is meeting your business needs. It is also recommended to leverage the data infrastructure engineering services vendor such as Ness, who has a data infrastructure engineer talent pool to design and implement data architectures that enable organizations to store, manage and analyze vast volumes of data.