Enterprise data and analytics is a fast-evolving field in enterprise IT, where new technologies and solutions are creating revolutionary ways to extract insights from data.
To keep pace with these changes and drive value creation through data analytics initiatives, organizations must be willing to adopt innovative solutions, embrace new and emerging best practices, and move beyond obsolete or outdated methods that are no longer effective.
Our blog post this week is all about transforming your enterprise data architecture to elevate your data management and analytics capabilities.
We’ll explore,
Enterprise data architecture is a strategic framework that guides how an organization manages data throughout its entire life cycle, from defining data requirements and collecting data to storage, processing, and analytics.
An organization’s database architecture defines how data flows from its original sources to downstream storage systems and analytics applications to deliver data-driven insights that empower business leaders to make better decisions.
A data warehouse architecture using Extract-Transform-Load (ETL) to capture data from multiple sources, process and standardize it, then load the data into a data warehouse. From there, organizations can run analytical queries, mine the data, or build reports/visualizations to communicate the results of analysis.
Change has been a constant throughout the development of enterprise data and analytics.
The growing popularity of the Internet in the early 2000s allowed companies to collect more data than ever before, but growing data volumes in siloed relational databases made the data expensive to store, highly fragmented, difficult to access, and slow to analyze. Then change happened: data warehouses were created and organizations could now store all of their data in a single centralized location.
Continuous growth in the volume, variety, and velocity of enterprise data prompted more technological progress in the mid-late 2000s. First came the release of Hadoop in 2006, which enabled distributed processing of datasets across multiple computers. We also saw the emergence and widespread adoption of the cloud computing model, which allowed organizations to minimize their data storage costs by moving data warehouses into the cloud.
With the public cloud in place as a reliable data storage solution, change accelerated in other areas: data indexing and querying solutions, data transformation techniques, visualization tools, and advanced analytics technologies like AI and machine learning.
The most cutting edge-organizations are embracing the rapid change in enterprise data and analytics, challenging established practices, and finding new ways to leverage enterprise data into business value. The most successful enterprise organizations have embraced change by updating their enterprise data architectures to leverage new technologies, accelerate time-to-insights, make better decisions, and ultimately drive value creation.
So what’s next? Today, data experts can transform their enterprise data architecture and create a modern data strategy that leads their organizations into the future.
Business leaders seeking to transform data and analytics within their organizations today should first understand the major challenges and key forces that are driving change and innovation in enterprise data architecture. Below, we identify three key factors at play and their implications for the future of enterprise data & analytics.
Large organizations depend on a growing number of applications to run their daily operations. As a result, they are generating and collecting more data than ever before, faster than ever before, and in a great diversity of structured, semi-structured, and unstructured formats.
While data storage is no longer a major stumbling block, the growth of big data is driving the development of new data indexing, cleaning, and analysis tools that can speed up the insight generation process and make it easier for organizations to draw insights from ever-expanding data streams.
Data security, privacy, and sovereignty laws have been implemented globally by countries interested in protecting the data rights of their citizens.
These data governance requirements create challenges for companies doing business internationally. To maintain compliance, organizations are adapting their data architectures to ensure centralized control and governance of all organizational data.
The ability to transform data into insights, and insights into action, is a competitive advantage for the modern, data-driven organization. Accelerating time to insights requires the adoption of enterprise data architectures and technologies that streamline the data life cycle and reduce latency between data creation and analysis.
Here’s our take on the failings of current enterprise data architectures and why they’re no longer meeting our needs in a big data world.
Data warehouses follow a schema-on-write approach, meaning the data must have a defined schema before writing into the database. As a result, all warehouse data has been previously cleaned and transformed, usually via some iteration of an ETL process. When business intelligence (BI) teams access the data warehouse, they’re accessing processed data - not raw data.
The problem here is that analyst teams are only exposed to data that’s been transformed in a specific way to support predetermined use cases. The lack of access to raw enterprise data limits innovation and prevents BI teams from transforming data in different ways to reveal new insights or uncover new use cases.
In the ETL process, data is captured from transactional databases and other sources, transformed in a staging area, then loaded into an online data/analytics warehouse where business intelligence teams can run queries.
But as organizational data assets continue to grow at 30-40% per year, the ETL process is not getting 40% faster. This often leaves enterprise data teams with a tough choice: reduce data utilization to speed up processing times, or accept increased time-to-insights.
Enterprise organizations are now deploying solutions like serverless Elasticsearch, OpenSearch, and ELK Stack to index their data, making it searchable and supporting analytics and BI use cases. These solutions use the Lucene database storage format which does a good job of supporting fast analytics but comes with a significant shortfall: Lucene indices can become extremely large, up to 2-5x the size of the data source, resulting in degraded performance along with increased costs and complexity.
Organizations still need a fast querying approach, but there’s a clear need for a new approach to data indexing that compresses the source data rather than expanding it to unmanageable proportions.
As innovators like ChaosSearch continue to push boundaries in data and analytics technology, business leaders have the opportunity to reimagine their enterprise data architectures, outpacing their competition.
Here’s how our powerful new approach to cloud log analysis is inspiring data leaders to upgrade their enterprise data architectures.
Our Chaos Index®, a proprietary data format that delivers auto-normalization and transformation, supports text search and relational queries, and can index all data from any source with up to 95% compression. Many current tools, like the ELK stack, don’t perform well at scale.
The ability to fully index raw data with high compression gives enterprise organizations a replacement for OpenSearch or ELK Stack that uses less storage, network, and compute resources to support their analytics needs. Chaos Index also performs well at scale, so data architects can achieve rapid time-to-insights, even with large data sets.
Our solution to the ETL process is our ChaosSearch Data Refinery, an in-app tool that allows our users to create views that prepare and virtually transform your index data for analytics with no data movement. For most enterprise organizations, the largest delays in the data pipeline happen because of data movement and the ETL process. With the ability to index and transform data directly in Amazon S3 buckets with ChaosSearch, enterprises can eliminate those delays, accelerating their time-to-insights.
By creating better ways for enterprise organizations to index and transform their data, we’re advancing our large-scale vision for the promise of data lake architectures.
Ready to try ChaosSearch? Our data platform will help you define a future-proof enterprise data architecture, accelerate time-to-insights, lower costs, and reduce the complexity of data analytics for your organization.
Download our ChaosSearch Technical White Paper to discover how the ChaosSearch data platform uses innovative database technology to enable a more functional, cost-effective, and future-proof approach to enterprise data architecture.