We provide tools, methods and services to collect data from internal data sources as well as external sources such as, software as a service (SaaS), social media, and commercial data sources. The collected data can be channeled to a number of data destinations or structured for analysis[…]
We clean, map and group your data into a data mapping platform. This process ensures the collected data is structured for feed into the warehouse or any other data destinations as required for further analytics. […]
Once the data has been cleaned and structured, we feed the data into any data destination like data warehouse, dashboard solution, Google Data Studio or Sheets, or any other tool that the client is using. […]
We provide data analytics reports (trends, patterns and behavior) to assist organisations in decision making. We can to handle complex analytics queries and well as handling large volumes of data. […]
We provide provide clients with data analytics:
- Solution Architecture
- Internet of Things (IoT) Apps
- Prototyping, Platform Integration
- Realtime Dashboards
- IoT Managed Services
- Data Management
Our tools and services will help clients to:
- Consolidate digital marketing data automatically (online interface, Google Data Studio, BI Tool, Google Analytics or Google sheets)
- Get an updated overview of the client digital marketing performance across all channels
- Easily group cost data with conversation data
- Save time and cost by automating digital marketing reporting
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Big Data Analytics Considerations
Many enterprises are moving quickly to adopt “big data analytics”—specifically, the application of advances in analytics techniques to the rapidly-expanding pool of information that enterprises have at their disposal to enable better decision making. As this trend of adoption continues, information security, risk and audit professionals are likely to become increasingly aware of the possible technical and operational risk that may arise as a result of adoption in their enterprises. However, non-adoption can also carry its own risk—particularly in the arena of business competitiveness. To analyze risk holistically, practitioners need to evaluate both technical risk and the business risk, in equal measure. Understanding the “use case”—the reasons why big data analytics is appealing from a business perspective—can help ensure that both angles are considered and practitioners are helping their enterprises remain maximally competitive.
Enterprises need to ask the following questions to address key potential challenges before they can, with confidence, realize the gains from big data analytics:
- Does the enterprise have the people, process and technology in place to build capabilities that will make productive use of data that the enterprise has collected?
- Has the enterprise established roles and responsibilities and identified stakeholders?
- Does the enterprise have (or can it get) data on which to apply advanced analytics?