Data is the asset of every organization. Traditional data services cost both time and money in prepping and cleaning, and producing ad-hoc reports typically requires either a tech-savvy team or patience while waiting on someone more knowledgeable to step in. Many companies are turning to Business Intelligence to support this analysis — yet still run into data management issues rooted in poor-quality data.
Placed side by side, Business Intelligence and data management make one thing clear: every organization needs to understand how data management directly shapes BI outcomes.
What Is Business Intelligence?
Business Intelligence is a set of processes that convert raw data into meaningful information using architecture and technology, driving profitable business decisions. That data becomes actionable knowledge shaping strategic, tactical, and operational planning — grounded in historical fact rather than assumption. BI tools typically produce dashboards, reports, graphs, charts, and maps that help users understand the nature of the business in detail.
How Business Intelligence Is Implemented
Implementing BI follows a fairly consistent sequence: data gets extracted from the corporate database — often spread across multiple systems — then cleaned, restructured into tables and data cubes, and finally transformed into a data warehouse. From there, the BI system generates ad-hoc reports and supports broader business analysis.
The BI Implementation Pipeline
Four stages from scattered corporate data to a functioning BI report
How BI Depends on Data Management
Business Intelligence depends on good-quality data — treating data management as the pipeline for BI isn't far off the mark. BI needs filtered data extracted from multiple sources and cleaned through disciplined data management processes.
The quality of data in a reporting application says a lot about the underlying management process. Companies have realized that cleaner data directly improves the return on their BI investment. There's also a balance to strike between flexibility and consistency in data governance — a tough ratio to maintain, but an essential one, since inconsistency becomes a genuine barrier to BI success.
Data services make life easier for companies that don't want to get pulled into complex exploratory data modeling, paving the way toward self-service business intelligence.
"Turning data into actionable insights for smarter business decisions."
What Is Data Management?
Data management is the process of collecting, filtering, and securely keeping data for future use — helping organizations and individuals make better decisions and maximize benefit. A strong data management strategy has become as critical as any other business asset, spanning databases, big data management systems, and more across a diverse data tier.
Build a Data Foundation Your BI Can Actually Trust.
Best Practices for Big Data Management
Creating a Discovery Layer
Built over the data tier so analysts and scientists can access and browse relevant data quickly, without wading through the entire database.
Developing a Data Science Environment
Automates data transformation work, streamlining and evaluating data models while eliminating manual testing and rework.
Autonomous Performance Monitoring
Continuously monitors queries and optimizes indexes, keeping the database responsive and freeing scientists from time-consuming upkeep.
Using a Standard Query Layer
Lets users and scientists access data without needing to know its physical storage location — no manual transformation required.
The Future of BI Is Enhanced Data Services
When businesses have access to clean, well-managed data, the whole enterprise gets empowered. Consistent data sources and reliable recovery tools rapidly transform what analytics can do. Improved data management lets businesses act on the market with more precision — while non-technical users no longer risk missing the right insight simply because the underlying data wasn't authentic or reliable.
Data management enables self-service Business Intelligence, cutting through technical bottlenecks and reducing the overall cost of any data solution.
"Empowering organizations with intelligent analytics and effective data management."
Turn Reliable Data Into Reliable Decisions.