By Dan Potter, Vice President of Product Marketing at Qlik
The explosion of data, the vast array of new data capabilities, and the dramatic increase in data-consumer demands have changed how data needs to be moved, stored, processed, and analyzed.
This has created headaches for IT, with many existing processes and legacy technologies unable to scale and compounding complexity and bottlenecks. The reliable, real-time data delivery that the business is demanding requires a new approach to managing and supplying data for insights. Here’s what that means in practical terms.
Data must be integrated at both scale and speed. This means bringing together increasingly high volumes of data from an increasing array of sources, and replicating its analytics and BI tools, all without disrupting mission applications such as finance and CRM.
That data must be governed to ensure security and quality at every stage of its lifecycle—From the accessing of raw data to its transformation and delivery to systems for analysis and action.
And these processes must be designed to provide the organization with agility, where data warehouses and data lakes are fueled and managed through automation.
There are four key strategies in meeting the data supply needs of today’s businesses, all of which rely heavily on automation:
- Using change data capture to automatically identify and propagate data changes as they occur.
- Automating the creation of data warehouses for the rapid addition of new data sources and the creation of purpose-built data marts.
- Automating the creation of data lakes to provide continuously updated, accurate and trusted data sets.
- Leveraging an enterprise data catalog to automatically make every new data set available and accessible.
Automating the process of capturing changes in data does a few key things. It ensures that the most recent version is being used, and keeps the user informed of where the data came from, where it’s been and how it’s changed. This creates visibility and trust for the user when making decisions with the data. And when data warehouse creation is automated, new data sources can be brought online quickly to enrich users with fresh analytics-ready data, so insights and decisions are more accurate and in the moment.
Automating data lake creation removes scripting and coding, labor intensive efforts that slow down data flows and keep analytics-ready data from being available in real-time. With data automatically available through an enterprise data catalog, users across the organization can find, reuse, and share trusted data sets while keeping data governance requirements in place. This ensures data quality and consistent use of trusted data sources by every team while maintaining proper data security based on role and location.
Leveraging automation helps organizations better source, process, transform and make available trusted data across the organization at scale. It’s a key driver in getting more value from data across the entire business and speeds the organization on its journey to becoming a fully data driven enterprise.
Learn more about how Qlik’s data integration and cloud analytics solutions can help you turn insights into confident actions.