Artificial Intelligence
Consulting
Development ServicesAs many enterprises tend to implement a decentralized and bimodal managed Data Discovery approach today, the next-generation Business Intelligence (BI) tools are emerging to enable self-service capabilities and interactive visualization of identified patterns and trends. To cut it short, any average business user should have access to Big Data and the ability to translate it into the effective BI that results in better data-driven decisions, more accurate predictive analytics and optimization of processes and approaches.
Next-generation Data Discovery tools that are popping out today have capabilities no traditional tools could ever boast. Yet, choosing the right ones for enterprise Big Data strategy can be a daunting challenge not every company is able to overcome successfully.
When choosing tools for your corporate Big Data solution, check the following capabilities that are vital for successful data discovery and visualization in 2015 and beyond:
Data mashup and modeling
Make sure your Data Discovery tool allows for semantic autodiscovery, intelligent joins and profiling, hierarchy generation, data lineage and data blending on varied data sources, including multi structured data.
Integration capabilities
Evaluate your prospective tool for general ease of use and user friendly UI, install, query engine, shared metadata, and promotability across all platform components.
Platform administration
Check if the tool enables user management, security, scalability, high availability, performance optimization and disaster recovery.
Metadata Management
Data Discovery tools of new generation should have the ability to leverage the same systems-of-record semantic model and metadata. It should also provide a robust and centralized way for administrators to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics/KPIs, and report layout objects.
Cloud deployment
Platform as a service (PaaS) and analytic application as a service (AAaaS) for creating, deploying and managing predictive analytics and analytics apps in the Cloud based on both Cloud-stored and in-house data.
Development and Integration
The platform you're evaluating for corporate use by users not skilled in Big Data should provide a set of programmatic and visual tools and a development workbench for building reports, dashboards, queries and analysis; ability to scale and personalize data distribution, schedules and alerts, BI workflow and analytics content to web and/or mobile; embeddability and customization of BI components in a business process, app or web / mobile portal.
Interactive data exploration
Check tool's capability to explore data by manipulating charts (e.g., use of different colors, sizes and shapes) and visualization of the data sets being analyzed (e.g. heat and tree maps, geo-maps, scatter plots, different types of charts, etc).
Analytic dashboards
Make sure you also evaluate tool's capability to build interactive dashboards and content with embedded advanced analytics to be consumed by different types of business users
IT-authored dashboards and reporting
Tools should be able to create well-formatted and print-ready interactive reports with or without any parameters. Centrally authored (aka IT-authored) dashboards depict graphically performance measures and are able to publish linked multi-object reports with intuitive and interactive displays such as gauges, sliders, maps, checkboxes, etc.
Ad-hoc query
Business users should be able to question data without relying on IT to generate a report. Next-Gem Data Discovery and Visualization tools should provide a re-usable semantic layer to allow for navigation of available data sources, hierarchies, predefined metrics and so on; OLAP to allow for data analysis with fast query and calculation performance and, as a result, slicing and dicing of data.
Would you like to know more about how to build an effective enterprise data discovery strategy? Request our free Data Discovery white paper!
