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must be willing to adopt innovative solutions, embrace new and emerging best practices, and move beyond obsolete or outdated methods that are no longer effective.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,
- the key forces driving change in data architecture,
- the limitations of current enterprise data platforms,
- and the innovative technologies behind a modern approach to enterprise data and analytics.
What is Enterprise Data Architecture?
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.
Defining Data Management Functions
- Data Collection: modern organizations generate large volumes of data from transactional systems, cloud-based infrastructure, and other sources. Collection refers to how the organization will gather data from those sources to power its analytics initiatives.
- Data Processing and Transformation: how the organization’s data will be processed and transformed to ensure data quality and prepare it for analysis. Data processing involves cleaning, filtering, and enriching data to standardize it and enable more accurate insights. Some enterprises process data in batches, while others have architected a real-time data processing solution to accelerate insights.
- Data Integration and Aggregation: how data from multiple sources throughout the organization will be integrated or aggregated in a centralized location to enable optimized observability and support data analytics use cases.
- Data Storage: common choices for storage include data warehouse applications, data lakes, cloud data platforms, and modern data lakehouse platforms that combine data lake storage with warehouse-like analytics capabilities.
- Data Governance and Security: this includes policies and solutions that encrypt and secure data, regulate data access, maintain auditability, and control who can share data outside the organization.
- Data Analysis: how the organization will analyze data, including which analytical tools or querying methods will be used to generate insights.
- Data Visualization: used to present complex data analytics in a format that’s easy for business leaders to understand and interpret, this speaks to the tools and processes used to communicate analyses.
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.
How Enterprise Data and Analytics Has Evolved
Change has been a constant throughout the development of enterprise data and analytics.
2000 - 2006
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.
2006 - 2011
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.
2011 - 2020
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.
2020 - Present
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.
Driving Forces in Enterprise Data Architecture
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.
1. Rapid and Accelerating Data Growth
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.
2. Increased Global Data Regulation
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.
3. Competitive Enterprise Data Insights
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.
Critical Shortcomings of Modern Enterprise Data Architectures
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.
1. Limited Big Data Utilization
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.
2. Outdated, Expensive, and Slow ETL Processes
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.
3. Stunted Data Indexing Solutions
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.
Discovering a Powerful New Approach to Enterprise Data Architecture
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.
Scale Enterprise Analytics with Chaos Index
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.
Clean, Prepare, and Transform Data with No Data Movement
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.
Support Data Democratization with an Activated Data Lake
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.
Here’s how it works:
- Collect: you capture enterprise data of all types and from every source and store it in Amazon S3, leveraging public cloud object storage economies of scale to get the lowest possible storage costs.
- Store: once your data is stored in Amazon S3 buckets, it can be indexed by ChaosSearch to enable full-text search and relational querying. With our proprietary data model, you can index your data with up to 95% compression and no loss of fidelity.
- Transform: using the in-app Chaos Refinery tool, you’ll be able to clean, prepare, and transform your data at query-time using a schema-on-read approach with no data movement.
- Visualize: finally, you can use the integrated Kibana Open Distro tool to easily create visualizations and build dashboards to support analytics use cases.
- Utilize: the result is a fully functional data lake that stores data in its raw format at a low cost. This data can be accessed, transformed, and analyzed on-demand, supporting various analytics use cases such as user behavior analysis and security monitoring. With our schema-on-read approach, there is no need for data movement or an ETL process, making traditional data warehouses obsolete.
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.
Ready to learn more?
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.
