Enabling Location Intelligent Insights Using Cloud-Native and Big Data Technology
Add agility to big data analysis
Companies struggle to generate positive returns on big data implementations. Many find it difficult to generate actionable insight from their big data assets. Geospatial processing changes the dynamics. Data and cloud-native location intelligence makes vast quantities of data consumable using data enrichment and automated catchment creation. Run spatial operations within native environments and big data. Then use the location intelligent results for AI applications linked to machine learning and augmented analytics.
Precisely offers a unique approach, embedding location technology within data and container processes. When you embed rather than connect, you can interpret transactional data faster and resolve critical business issues with the clarity you need.
The data/cloud-native challenge
Big data and cloud-native environments increasingly allow companies to store and process incredibly large datasets of customer calls, financial transactions, and social media feeds. Yet, many companies struggle to generate meaningful, actionable insights. Key performance indicators remain elusive as data volume and velocity continue to grow.
The challenge is to connect data within and across datasets in a way that:
- Ensures accuracy and precision
- Enables enrichment without disrupting existing processes
- Keeps pace with the extraordinary speed and scale required
The Location Intelligence advantage
Location Intelligence brings critical perspective to data analysis. Big data often contains location information. This could be a customer address, a mobile phone GPS signal, the location of an ATM, a store transaction, or a social media check-in.
Through geo-enrichment, a process of providing context to business data, organizations can augment records with both third party attributes and the results of geospatial queries.
With this enriched embedded insight:
- Rules-based workflows can utilize this appended data to automate business decisions.
- Spatial aggregation can condense data volumes, making them more manageable.
- Data can be generalized in a spatial context for results that are easier to model and visualize.
- Organizations can gain new perspectives into business drivers and subsequent company responses.