Today’s businesses have turned to data-driven competition as data has become the new dollar. However, much like crude oil, data also shows somewhat similar properties. Data and oil both need refinement to become valuable. Again, such refinement procedure requires infrastructure before they are structured and analyzed where to use. With data infrastructure backed by large data centres, enterprises get a foundational system and organized framework to store, process, and manage data.
On the other hand, fetching data from these organized data repositories for analytics empowers enterprises to derive actionable and business-oriented insights. However, having a balance between infrastructural efficiencies and appropriate analytics is essential for a business to thrive. This article will deliver a comprehensive guide on data infrastructure and data analytics. It will also shed light on critical components, a contrasting view without each other’s existence, how to build an effective data pipeline with appropriate infrastructure and analytics, some best practices, and how enterprises can leverage the cloud to reap the maximum benefits of data.
What is data infrastructure?
Data infrastructure is a collective term that determines the physical, digital, and virtual systems, such as servers, data centres, networking, software, databases, protocols, security, and various forms of virtualization, that support data flow management and storage for enterprise workloads. These systems offer robust computation and are the backbone for any data operation. It also helps organize, share, and consume data meaningfully. One of its most critical functions is to ensure that the exact amount and the accurate data can get to the right users or systems at the right time. It boosts effective data-driven decisions. To fulfill this goal, enterprises should have a solid data infrastructure strategy in place that can retain data flows, protect data quality, eliminate data redundancy (if not used as backup), and prevent crucial data from being isolated into silos.
Understanding Data Analytics: From Descriptive to Predictive
Data analytics is the art and science of examining crude data to extract insights, identify trends, predict responses, and derive insightful decision-making power for business ROI. It involves collecting data by cleaning it, analyzing it, visualizing it, and aligning it with business requirements. Data analytics also applies statistical methods, machine learning techniques, and predictive estimation to transform data into actionable intelligence. While descriptive analytics summarizes past data, diagnostic analysis explains why the graph or data shows a particular pattern. Predictive analytics helps forecast future trends.
Key Components of Data Infrastructure
Several components work in conjunction to enable data infrastructure within an enterprise. Here are the primary components associated with data infrastructure.
1. Data storage
Other than hardware, the cloud storage, or a data center, comprises servers and storage drives – data infrastructure also requires databases (SQL and NoSQL), data warehousing, and data lakes to organize the data appropriately.
2. Data processing
This mechanism transforms, aggregates, and processes data dynamically or in batches. Various data go through a series of data pipeline processes, like collection, cleaning, analyzing, and reporting.
3. Data integration
Moving data between various enterprise systems is essential so that different professionals or departments can use these diverse forms of data. ETL (Extract, Transform, Load) and ELT solutions are also a part of the data infrastructure to move data between systems.
4. Networking and security
Appropriate networking between systems, along with proper configuration, is essential for a data infrastructure to operate. The data packets flowing through the network (data in transit) should also remain secure and encrypted.
5. Cloud and on-premise systems
Data infrastructure can also span from on-premise servers to cloud services. While on-premise servers are rigid and less scalable, cloud-native data storage offers instant flexibility and scalability.
Key Stages of Data Analytics
Several stages work one after another in conjunction to enable data analytics within an enterprise. Here are the stages associated with data analytics.
1. Data collection
Data collection is the systematic approach of gathering data from various sources and accumulating it as a dataset. It can help later in answering research questions, evaluating outcomes, and forecasting trends. It identifies data types, data sources, and methods for creating data dictionaries and metadata.
2. Data cleansing and preparation
It involves conditioning crude data for analysis. Professional data analysts had to identify and address issues like inconsistencies, errors, missing facts/figures, and outliers, confirming the data are proper, trustworthy, and relevant for analysis.
3. Data analysis
It involves the process of extracting meaningful insights from raw data that can help businesses identify patterns, trends, and insights that can help make informed decisions and improve outcomes.
4. Data visualization
Data visualization helps represent the resultant analyzed data using charts, plots, graphs, infographics, and animations. These visual representations of information can easily express complex data relationships and data-driven business insights seamlessly.
5. Analytics automation with ML
Automated machine learning in data analytics provides a comprehensive experience verifying, training, and developing custom models to extract insights from stored data. Through automation support and artificial intelligence, enterprises can make intelligent business choices without human intervention and in real-time.
How Enterprises Can Design a Robust Data Infrastructure for Data Analytics
Since we are in a data-powered business era, data has become more than an asset. Enterprises consider data analytics a strategic differentiator for making smarter decisions and gaining a competitive edge. Any miscalculation through data-driven analysis can lead to a loss of millions – if not billions, for an organization. So, bolstering the data infrastructure plays a crucial role in data analysis. A well-designed infrastructure for data analytics should be scalable, adaptable, agile, and reliable with the paradigm shift or change in business needs.
1. Data lineage
To accumulate data for analysis, enterprises should understand the data source and what errors to identify in the data pipeline. It also enables enterprise professionals to understand the metadata associated with different data. Some well-known data lineage tools used as part of data infrastructure operations are Open Lineage, Airflow, and Marquez.
2. Scalability
Any robust data infrastructure has to be highly scalable – both horizontally and vertically. Enterprise data may grow and shrink. To withstand this change, enterprises can create distributed systems for data storage where adding more machines can distribute the workload. Therefore, leveraging distributed nodes or cloud solutions can bring high availability for big data workloads.
3. Data governance
Another factor that makes a data infrastructure great for data analytics is maintaining quality, access control, and data privacy. Enterprises must configure data infrastructure to ensure validation rules. Also, anonymization, pseudonymization, and encryption (for both data at rest and in motion) are necessary. Furthermore, adhering to compliance policies is also essential through GDPR, CCPA, and HIPAA.
4. Automation and orchestration
Manual errors in handling volumetric data might enhance the chances of mistakes. Therefore, automating data to schedule event-driven data collection and real-time data workflows can reduce data management and operational time. Data infrastructures can also deploy pipelines using Terraform for data orchestration along with retry and alert mechanisms for failed tasks.
Role of Cloud in Data Analytics and Data Infrastructure
Cloud solutions and services play a transformative role in shaping modern data analytics and infrastructure. From cost-effective scalability to processing and SaaS integration, they offer everything a data lifecycle needs. Cloud is essential because the exponential data growth is unpredictable. On-premise systems often struggle to keep up with the storage and processing demands. With compute-intensive data processing power and elastic storage, enterprises can withstand the stress of data analytics as per business dynamics. It also eliminates the need for heavy upfront investments in hardware for startups.
Cloud-based data warehouses (e.g., Redshift, BigQuery, and Snowflake) and data lakes foster the storage and management of vast amounts of structured and unstructured data for enterprises under one central repository. It offers both real-time and batch processing for retrieving and storage. Additionally, the cloud enables collaboration by encouraging secure, remote access to data and dashboards for team collaboration across geolocations.
Conclusion
We hope this article provided a concrete overview of how data analytics and data infrastructure have become the powerhouse of different enterprises. Since data has become the next crude oil, these twin engines power the digital transformation for different sectors worldwide. With the appropriate infrastructure, data management techniques, and analytical strategies, businesses can harness the maximum potential of data analytics to innovate, optimize, and grow. Enterprises investing in robust data practices and scalable infrastructure with power-packed computation can become the cornerstone of success in the data-centric enterprise. Contact us or Visit data solutions for a closer look at how VE3 can drive your organization’s success. Let’s shape the future together.