Today’s growing data volume present virtually limitless opportunities for organizations to optimize processes or increase profitability. But that’s only possible if data can be aggregated and analyzed to inform business decisions. Unfortunately, the large volumes of data that organizations ingest are typically siloed in many incompatible data sources, making analytics difficult.
With data aggregation and data transformation, however, data can be consolidated and translated into a standard format easily used by analytics systems. With these capabilities, organizational silos can be eliminated to provide immediate access to data and critical information for data-driven insights.
Why Organizations Must Transform Data for Analytics
As organizations increase their data stores, they end up with more and more inaccessible information. Leveraging all this data to improve the decision-making process requires aggregation and transformation, bringing it together in one place and translating it into a standard form for analytics.
Data transformation is the process by which organizations aggregate and convert data to make it conducive to integration, management, and analysis. This may include converting data types, cleaning data, enriching data, converting formats, and more. After the data is converted, organizations can access and use it for analysis.
The benefits of data transformation include:
- Accessibility: With data transformation, all available data is accessible to form a comprehensive picture. Business leaders can then use the information to impact strategy and operations based on data insights.
- Elimination of organizational silos: Aggregated information eliminates organizational silos. Departments, such as sales, marketing, and operations, have the same access to data for queries and reports. This allows them, for example, to get a comprehensive view of the sales funnel and use this information to improve lead generation.
- Timely access: With data transformation, users can access data that is real-time, or close to real-time. And they don’t need to spend exorbitant amounts of time locating data sources, possibly missing vital details along the way.
Three Solutions for Aggregating and Consolidating Data for Analysis
Data can be aggregated and consolidated through three different methods: Traditional extract, transform, and load (ETL) with data warehousing, ad-hoc mashing, and federated queries. Choosing between them depends on whether the organization wants a solid solution that’s managed by an IT team, a solution that business users can manage themselves, or real-time data access.
- Data warehousing: ETL technology is traditionally used to aggregate and consolidate data into a data warehouse, or a centralized data storage system where organizations integrate data from multiple sources into one place. This requires an IT team to set up and maintain.
- Ad-hoc data mashing: With data mashing, non-technical users have self-service access to data using tools which are typically part of a business intelligence solution. This allows business users to mix and match data or import new data sources.
- Federated queries: Some business intelligence solutions include federated query functionality. Instead of creating a data store or repository, these multi-source queries that provide answers to specific questions in real time, run against individual data stores.
Harness the Power of More of Your Data
Modern business leaders realize that data analytics is essential for driving business success. Aggregating data into one location is the key to real-time, effective analytics, and there are several options for how to do so. Organizations need to decide which of these is best suited to their unique needs.
Learn more about how data transformation can drive success for your organization our new e-book, Transform Data for Analytics. Download your copy.