Data analytics & Data Science
Big data analytics
One of the aspects offered by leveraging cloud computing is the ability to use big data analytics to tap into vast quantities of both structured and unstructured data to harness the benefit of extracting business value.
Retailers and suppliers are now extracting information derived from consumers’ buying patterns to target their advertising and marketing campaigns to a particular segment of the population. Social networking platforms are now providing the basis for analytics on behavioural patterns that organizations are using to derive meaningful information.
Data Visualization and Presentation
Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.
The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data.
What is data visualization used for?
Data, especially a lot of data, can be difficult to wrap your head around. Data visualization can help both you and your audience interpret and understand data.
Data visualizations often use elements of visual storytelling to communicate a message supported by the data. There are many situations where you would want to present data visually.
Data visualization can be used for:
- Making data engaging and easily digestible
- Identifying trends and outliers within a set of data
- Telling a story found within the data
- Reinforcing an argument or opinion
- Highlighting the important parts of a set of data
Database Management systems
Database Management Systems (DBMS) refer to the technology solution used to optimize and manage the storage and retrieval of data from databases. DBMS offers a systematic approach to manage databases via an interface for users as well as workloads accessing the databases via apps.
The management responsibilities for DBMS encompass:
- Information within the databases
- The processes applied to databases, such as access and modification
- The database’s logic structure
- All businesses undergoing a digital transformation should be mindful of the new opportunities for domain transformation that present themselves with the incorporation of new technology.
Business Intelligence
Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information. BI solutions provide historical, current and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
- A successful digital transformation demands more than just updating technology or redesigning products. If an organization fails to align its digital transformation efforts with its internal values and behaviors it can have a knock-on effect on an organization’s culture.
- Negative repercussions range from slow adoption of digital technologies to loss of market competitiveness and inevitable failure of the initiative and lost productivity and revenue. On the other hand, a comprehensive and collaborative effort can help shift the culture to understand, embrace, and advance digital transformation.
- Leaders should formulate a clear vision of the transformation and effectively communicate it throughout the organization on an ongoing basis. They need to be clear about what the smart risks are and why they’re worth taking.
ML /Cognitive Computing Development
Each cloud service provider currently offers their customers similar computational services and upsell their offering through unique and compelling AI functionality. Due to the complexity of the available services, companies often lack a set of criteria to clearly distinguish between providers, platforms, or product instances in order to make the best decision for their needs. Additionally, performing predictive analysis in a real-time scenario is not a simple task. Besides the technical and administrative aspects that need to be considered, it requires a strong commitment from the main stakeholders. The secondary objective of the paper is to offer a set of guidelines for a successful cognitive computing approach.
Data Engineering and Data Warehousing
Data Engineering- Data engineering is a set of operations aimed at creating interfaces and mechanisms for the flow and access of information. It takes dedicated specialists – data engineers – to maintain data so that it remains available and usable by others. In short, data engineers set up and operate the organization’s data infrastructure preparing it for further analysis by data analysts and scientists.
Datawarehouse--In a data warehouse (DWH) you have typically structured data and optimised them for business users to query. If you dig a little deeper, you offload data from the trucks in the back of the physical shop, before it gets sorted and structured into the warehouse for the customers to buy. In a DWH you basically do the same, just with data. As you see in the DWH architecture below, the offloading area in the back of the store is your stage area where you store the source data from your operational systems or external data.
- A successful digital transformation demands more than just updating technology or redesigning products. If an organization fails to align its digital transformation efforts with its internal values and behaviors it can have a knock-on effect on an organization’s culture.
- Negative repercussions range from slow adoption of digital technologies to loss of market competitiveness and inevitable failure of the initiative and lost productivity and revenue. On the other hand, a comprehensive and collaborative effort can help shift the culture to understand, embrace, and advance digital transformation.
- Leaders should formulate a clear vision of the transformation and effectively communicate it throughout the organization on an ongoing basis. They need to be clear about what the smart risks are and why they’re worth taking.
Cloud Implementation for data engineering
We offer the following services in the cloud implementation
Cloud readiness
Roadmap & POCs
Data migration
Cloud Native Capabilities
Cloud selection
Infrastructure Migration
Operational Management
Ansible automation
