Analytics

It has become more important than ever for organizations to be able to quickly and effectively build enterprise-wide analytical systems to better serve their customer base. The Analytics journey starts when an organization recognizes that it must become more efficient and initiate an analytics transformation journey. Due the increasing pace and demands of the business, as data is key to respond back efficiently, you would need to promote a data-driven culture, enhance current data governance framework to increase data integrity and reliability and empower business users to enable self-service analytics and deep learning from data flowing from different sources, coming in different formats.

Business Analytics

As the data stored by companies grow rapidly, how do organizations gain valuable insights from their Data? This is where Business Analytics helps businesses understand their data and adjust their decision making according to what their data tells them. Organizations can use business intelligence to make smarter decisions and find the edge over their competition.
Business intelligence helps business units, managers, top executives and other operational workers make better-informed decisions backed up with accurate data. The following illustrates the Business Analytics maturity graph. As organizations grow, they move from descriptive analysis to prescriptive analysis and finally reaching decision automation.

Business Analytics helps:

  •  To understand the customer more deeply and effectively
  • To improve the revenue and drive the performance
  • To identify the sales trends
  • To improve efficiency and transparency within the business
  • To deliver outstanding customer experience.

Data Management

The first step to build a right and solid business analytics solution is to set up the right data flows within the organization. Examining the reliability and availability of this data at the right time with the right quality, generates the initial agenda for any analytics journey. In order to build the analytics capability, it is also necessary to put in place the right governance processes as well as choosing the right data integration methodology. And finally, such initiatives need to be sponsored right from the top with correct, timely governance and review processes.

Some of the key phases in Data Management are:

  •  Ingestion of Data: Data needs to be properly ingested from all the data sources an organization has. It is important to do have a data discovery session to first understand the types of data available and redo the sessions if necessary to fine-tune this understanding.
  • Data Integration: The Data Integration process will integrate the data from various data sources into a single, unified view. Integration will begin with the ingestion process and includes steps such as cleansing, ETL mapping, verifying the quality and transformation.
  • Data Profiling and Governance: Automatically discover, model, define, and govern information content and structure, as well as understand and analyze the meaning, relationships and lineage of information.
  • Data Store: Data Store contains the database and other components that provide the main structured storage area for data integrated from various application data sources. This data is transformed by ETL processes and integrated into the Data Warehouse and Data Marts repositories and other components, where data about any distinct category of information has a single point of reference, enabling current, accurate, consistent, high-quality data to be made available to analytical systems

Data Science

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data – both structured and unstructured. Because of the large amounts of data, modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.

Data Mining Vs Data Science

Data science is often confused with data mining. However, data mining is a subset of data science. It involves analyzing large amounts of data (such as big data) in order to discover patterns and other useful information. Data science covers the entire scope of data collection and processing.

A data science and machine learning platform built from the ground up for an AI-powered business helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.

Finance and Reporting

Often organizations need to plan and budget for their financial year and these need to translated into reports and dashboards by the finance teams to the management board in order to understand and project their investments, mitigate risks, track projects, increase transparency, decrease costs and increase profitability.

An enterprise Planning Analytics solution should provide a complete, dynamic environment for developing timely, reliable and personalized forecasts and budgets. Organizations can then rapidly analyze data and model business requirements for their entire organization and use the results to budget and forecast with confidence for better business outcomes.

Typical Planning Analytics solutions include:

  • A multi-dimensional engine that provides exceptionally fast performance for analyzing complex and sophisticated models, large data sets and even streamed data
  • Support for a full range of enterprise planning software requirements—from high-performance, real-time profitability analysis, financial analytics and flexible modeling to enterprise-wide contribution from all business units
  • The ability to create personal scenarios with advanced personalization to enable an unlimited number of ad-hoc alternatives so individuals, teams, divisions and whole companies can respond faster to changing conditions
  • Managed contribution that makes it possible to personalize hierarchies, dimensions and rollups and to receive an up-to-the-minute status of every participant in the planning process

GBM Qatar has extensive experience in implementing and supporting Planning Analytics solutions for several customers in Qatar and is well versed with various financial organizational structures and the key.

IOT and Artificial Intelligence

The Internet of Things delivers the data. AI powers the insights.

Organizations today can unlock the power of data with AI and IoT to innovate asset management, optimize real estate and facilities, improve software and systems engineering and advance their digital transformation.

An integrated IOT platform powered by AI insights can provide organizations across the industry verticals to derive value out of their infrastructure by enabling organizations to capture and explore data for devices, equipment, and machines, and discover insights that can drive better decision-making.

An organization can with an integrated IOT platform

  • Process IoT data instantly to help identify valuable insights related to devise behavior and operations in the field. Spot trends before they impact the bottom line. Optimize operations and resources
  • Reduce operational expenses by understanding IoT devices to operate them more effectively and efficiently. Visualize IoT data to better plan your operations and increase productivity.
  • Enrich, augment and interact with IoT data from the IoT Platform with analytics using simplified data ingestion and curation.
  • Increase trust and transparency by enabling IoT assets to validate provenance and events in a trusted, immutable ledger with integrated blockchain service.

Vehicle and fleet management, connected cars, health care devices integration are but a few examples with what organizations can achieve to improve customer services and provide greater business value.

Business Analytics

As the data stored by companies grow rapidly, how do organizations gain valuable insights from their Data? This is where Business Analytics helps businesses understand their data and adjust their decision making according to what their data tells them. Organizations can use business intelligence to make smarter decisions and find the edge over their competition.
Business intelligence helps business units, managers, top executives and other operational workers make better-informed decisions backed up with accurate data. The following illustrates the Business Analytics maturity graph. As organizations grow, they move from descriptive analysis to prescriptive analysis and finally reaching decision automation.

Business Analytics helps:

  •  To understand the customer more deeply and effectively
  • To improve the revenue and drive the performance
  • To identify the sales trends
  • To improve efficiency and transparency within the business
  • To deliver outstanding customer experience.
Data Management

The first step to build a right and solid business analytics solution is to set up the right data flows within the organization. Examining the reliability and availability of this data at the right time with the right quality, generates the initial agenda for any analytics journey. In order to build the analytics capability, it is also necessary to put in place the right governance processes as well as choosing the right data integration methodology. And finally, such initiatives need to be sponsored right from the top with correct, timely governance and review processes.

Some of the key phases in Data Management are:

  •  Ingestion of Data: Data needs to be properly ingested from all the data sources an organization has. It is important to do have a data discovery session to first understand the types of data available and redo the sessions if necessary to fine-tune this understanding.
  • Data Integration: The Data Integration process will integrate the data from various data sources into a single, unified view. Integration will begin with the ingestion process and includes steps such as cleansing, ETL mapping, verifying the quality and transformation.
  • Data Profiling and Governance: Automatically discover, model, define, and govern information content and structure, as well as understand and analyze the meaning, relationships and lineage of information.
  • Data Store: Data Store contains the database and other components that provide the main structured storage area for data integrated from various application data sources. This data is transformed by ETL processes and integrated into the Data Warehouse and Data Marts repositories and other components, where data about any distinct category of information has a single point of reference, enabling current, accurate, consistent, high-quality data to be made available to analytical systems
Data Science

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data – both structured and unstructured. Because of the large amounts of data, modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.

Data Mining Vs Data Science

Data science is often confused with data mining. However, data mining is a subset of data science. It involves analyzing large amounts of data (such as big data) in order to discover patterns and other useful information. Data science covers the entire scope of data collection and processing.

A data science and machine learning platform built from the ground up for an AI-powered business helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.

Finance and Reporting

Often organizations need to plan and budget for their financial year and these need to translated into reports and dashboards by the finance teams to the management board in order to understand and project their investments, mitigate risks, track projects, increase transparency, decrease costs and increase profitability.

An enterprise Planning Analytics solution should provide a complete, dynamic environment for developing timely, reliable and personalized forecasts and budgets. Organizations can then rapidly analyze data and model business requirements for their entire organization and use the results to budget and forecast with confidence for better business outcomes.

Typical Planning Analytics solutions include:

  • A multi-dimensional engine that provides exceptionally fast performance for analyzing complex and sophisticated models, large data sets and even streamed data
  • Support for a full range of enterprise planning software requirements—from high-performance, real-time profitability analysis, financial analytics and flexible modeling to enterprise-wide contribution from all business units
  • The ability to create personal scenarios with advanced personalization to enable an unlimited number of ad-hoc alternatives so individuals, teams, divisions and whole companies can respond faster to changing conditions
  • Managed contribution that makes it possible to personalize hierarchies, dimensions and rollups and to receive an up-to-the-minute status of every participant in the planning process

GBM Qatar has extensive experience in implementing and supporting Planning Analytics solutions for several customers in Qatar and is well versed with various financial organizational structures and the key.

IOT and Artificial Intelligence

The Internet of Things delivers the data. AI powers the insights.

Organizations today can unlock the power of data with AI and IoT to innovate asset management, optimize real estate and facilities, improve software and systems engineering and advance their digital transformation.

An integrated IOT platform powered by AI insights can provide organizations across the industry verticals to derive value out of their infrastructure by enabling organizations to capture and explore data for devices, equipment, and machines, and discover insights that can drive better decision-making.

An organization can with an integrated IOT platform

  • Process IoT data instantly to help identify valuable insights related to devise behavior and operations in the field. Spot trends before they impact the bottom line. Optimize operations and resources
  • Reduce operational expenses by understanding IoT devices to operate them more effectively and efficiently. Visualize IoT data to better plan your operations and increase productivity.
  • Enrich, augment and interact with IoT data from the IoT Platform with analytics using simplified data ingestion and curation.
  • Increase trust and transparency by enabling IoT assets to validate provenance and events in a trusted, immutable ledger with integrated blockchain service.

Vehicle and fleet management, connected cars, health care devices integration are but a few examples with what organizations can achieve to improve customer services and provide greater business value.

How can we help?

Our team has expertise across the full range of digital solutions. We are here to help you progress on your journey towards digital transformation.