Overview

Data Analytics as a Service (DAaaS) represents a new approach to an extensible platform that can provide cloud-based analytical capabilities over a variety of industries and use cases and delivering value in an agile manner.

Architecturally, and due to the intrinsic complexities of analytical processes, the implementation of DAaaS represents an important set of challenges, that organizations can not face by them selves and usually require a vast amount of resources, cost and knowledge to implement and  maintain whether in the cloud or on-premise.

Our aim is to shield organizations from these complexities by providing data analytics and business intelligence in a software as a service, turnkey manner with actual intelligence delivered in weeks rather than years and make it accessible to all with the minimum possible cost.

From a functional perspective, the platform covers the end-to-end capabilities of an analytical solution, from data acquisition to end-user visualization, reporting and interaction. Beyond this traditional functionality, it extends the usual approach with innovative concepts, like Analytical Apps, artificial intelligence and big data.

  • Build on one of the largest and fastest growing intelligent clouds Azure
  • Build using Microsoft Power BI, the number 1 Business Intelligent tool according to Gartner
  • Build on a secured analytics platform Azure
  • Full and unparalled interoperability with Microsoft 365
  • Customized & easy to use dashboards
  • Compelling data visualization
  • Drillable Graphs and tables to get deeper answers on your data
  • Fully managed end-to- end platform
  • Transform data into actionable intelligence
  • 360 view of the data by connecting siloed & disparate data sources into a single view
  • Provides accurate & transparent view of live performance
  • Analyse data with greater speed, accuracy & understanding
  • Rich interactive reporting of live metrics
  • Utilize Artificial Intelligence to manage complex data and statistical issues and drive predictions

Business Intelligence & data analytics as a service

Hotel Industry

In a dynamic, evolving, and competitive hotel industry, success depends largely on how hotels make use of their data. Effective data collection and analysis methods can give insightful ideas to increase sales and footfall through efficient processes and great marketing. Hotel Analytics help hoteliers explore travel trends so that they can model likely outcomes and anticipate the future. This, in turn, helps them improve decision-making, drive productivity, and increase the predictability of revenue. Hotel analytics help hoteliers explore travel trends so that they can model likely outcomes and anticipate similar results in the future. This, in turn, helps them improve decision-making, drive productivity, and increase the predictability of revenue.

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Restaurant chains

Restaurant data analytics is the process of analysing every data point related to your business and converting them into meaningful insights, which can help improve everything from menus and staff straining to restaurant policies and marketing campaigns.

Unlike restaurant reporting, which involves looking at a compiled list of core metrics to compare sales and profits between specific periods, restaurant analytics allows you to do a deep dive into the numbers to better understand why your business is performing in a certain way.

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Wholesale/distribution Industry

An important task for many managers is to improve profit margins. Low sales figures, high inventory costs, and not enough hours in the day are the most common issues faced by managers in wholesale distribution.
Traditional approaches do not assure success. With data analytics, you have instant access to real-time and fact-based analysis with just the click of a button. Furthermore, your report can be drilled down for more detailed information.

Retail Industry

Retail analytics is the process of providing analytical data on inventory levels, supply chain movement, consumer demand, sales, etc. that are crucial for making marketing, and procurement decisions.
Retail data analytics is the process of collecting and studying retail data (like sales, inventory, pricing, etc.) to discover trends, predict outcomes, and make better business decisions.

Business Intelligence & data analytics Custom

Not all things are created equal. Some industry segments required more specialized processes, and require specialized analytics to create business value.

Sectors that continuously generate and consume large amounts of data, and have the need to coalesce this disparate data from multiple sources to arrive at forecasts, key trends, and analytics; are major consumers of data and require solutions that are being deployed to perform predictive fraud analysis, detect false or erroneous claims, and enhance security. Quantifying and assigning numerical values to factors like prospective profits, credit/investment risks, and the likelihood of a policy ending up in a claim; is another usage that required specialized needs that require specialized modes.

Banking, Financial Services, and Insurance are one of the major consumers of data warehousing and analytics. Other major sectors include Telecom & IT, Healthcare, Retail, Manufacturing, and Governments, etc.

Our role is to assist these organizations in realizing and extracting the expected value in their investments and providing them with the skills required to make the correct decisions during this journey. The correct Data management strategy is essential to the realization of this value.

Insurance Industry

For everything from developing new products to handling claims, explore upselling opportunities and risks, customer segmentation and product performance can be considerably improved through data analytics. Insurance companies can combine data from otherwise distinct line of businesses and achieve a 360-degree view of the business and their customers, helping them identify opportunities and risks, that will optimize revenue and reduce costs.

Telecom Industry

The telco industry is using data analytics to improve in several key areas, including customer experience, fraud reduction, churn prediction, and dynamic pricing. And with the rollout of 5G, data plays a key role in network planning, monitoring and management. The increase of data available for analysis demands flexible solutions and architectures that can handle and analyze and product actual intelligence for the organization.

Pharmaceutical and Health Care industry

Data analytics in healthcare and medicine aims to deliver better and more efficient services to patients. A large amount of data is collected every time a patient visits a healthcare facility. Additionally, devices like fitness trackers and smartphones with health monitoring apps record health-related data in real-time. Data analytics allows such complex data to be collected, stored, and analyzed by healthcare organizations and professionals, effectively reducing cost and providing better patient care. It offers doctors the possibility of identifying any risks before it may cause any significant complications, and then subsequently act on it.

Data warehouse Management and operations

Organizations generate tremendous amounts of data each day from several different systems, ERP, CRM, OSS, Billing Systems in a structured or unstructured form. In order for this data to be useful for decision making it needs to translate this resource into value, an organization needs a place to aggregate, store, organize and analyze the data – that is a data warehouse.

Organizations are under more pressure to save costs as they transition to data-driven value-based service models that may reside on premise or in the cloud. As one might imagine, data warehouses can be quite large and costly to build and maintain.

Growth Drivers

• Rising need for data warehouses for disparate data storage
• Growing demand for data mining for BI and data analytics
• Increasing use of historical data for enhancing customer experience
• The proliferation of cloud technology in data warehousing

Industry Pitfalls & Challenges

• High deployment costs and IT complexity
• High maintenance cost and lack of resources
• Data Architecture rigidity and inefficiency
• Expanding needs for storage and performance

Data Warehouse as a Service or DWaaS for short is an outsourcing model where a service provider has delegated the responsibility of creating, managing, and upgrading a data warehouse.

Data Warehouse-as-a-Service addresses this challenge by providing the full-featured capabilities companies need, without much of the administrative overhead. DWaaS is optimized for the purpose and provide organizations with improvements in:

Cost

A cost model that aligns with the needs of modern businesses, pay for the services and capacity you need, when you need to use it and leverage economies of scale and refined processes to manage large amounts of data.

Performance

Leverage modern infrastructure platforms that are continuously upgraded to provide enhanced performance, accelerating the time required to transform raw data into actionable insights.

Scalability

Expand your storage footprint as you acquire more data. Cloud services offer a seemingly infinite capacity, so you don’t have to worry about limited storage space. Add more compute resources to process complex analytics and enable faster decision making.

Elasticity

As your data consumption needs change based on your business cycles and the structure of your operations you can scale your warehouse processing engine up when you need it and scale it down during slow periods and avoid the costs of underutilized capacity.

Time-to-value

Your data warehouse can be provisioned within weeks rather than months and to realize value almost immediately.

You can start small and start growing – expanding your data warehouse as your company data increases. For companies looking to implement a new data warehouse, or modernize an existing data warehouse solution, Data Warehouse-as-a-Service is an easy choice.