Analytics As A Service in Australia
Let the experts take care of your data
Book A Free Discovery CallAre you looking to unlock insights and value from your data but don’t have the resources or expertise to do so? Introducing Analytics as a Service (AaaS)- a scalable and flexible solution to turn your data into actionable insights.
Our team of data engineers, data scientists, BI developers and data analysts will provide you with a range of services, including data ingestion, data visualization, predictive analytics, and machine learning, among others. Our solutions are designed to help you make data-driven decisions, optimize your operations, and gain a competitive edge in your industry.
Our AaaS Model Offers the Following Benefits:
Cost-effectiveness: AaaS provides a subscription-based model, granting you access to a diverse range of expertise skills at a reasonable cost, eliminating the need to hire multiple expensive specialists on a full-time or contract basis. We handle the management and deployment of resources according to your project needs, allowing you to pay only for the delivered work.
Additionally, we offer attractive yearly support packages that include experienced data consultants who can work for a few days a week instead of hiring them on a full-time basis. This arrangement costs less than hiring a full-time equivalent employee, providing you with the expertise you need without exceeding your budget.
Our subscription-based model enables you to access advanced data analytics technologies and services without significant upfront investments. Working in data analytics industry for 20+ years, we have developed lots of custom tools (sample of developed tools listed in our product section), which will help you to kick start your project and leverage our expertise at fraction of the cost.
Expertise: Our data engineers, data scientists, report developers, and data visualization experts possess deep knowledge and experience in data analytics, ensuring that you receive high-quality insights and recommendations.
Scalable: Our solutions can quickly adapt to your business needs, ensuring that your data analytics capabilities are always up-to-date.
Simplified management: Managing Data project and resources including data consultant is our responsibility which will reduce the complexity of managing data analytics and data migration projects on your end.
Advanced technologies: We invest heavily in the latest data analytics technologies, such as machine learning and artificial intelligence, providing you with the most advanced data analytics capabilities.
Don’t let your data go to waste. Contact us today to learn more about how our AaaS can provide you with a comprehensive and cost-effective data analytics solution, enabling you to turn your data into insights and value.
Why Partner with us?
To make a data analytics project successful, you need a combination of technical and non-technical skills, as well as access to certain resources. Here are a few examples:
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Technical Skills
You need to have technical skills in data engineering, data analysis, data management, data science and data visualization. These skills include proficiency in Power BI, SQL, ETL development, Power Query, Power Apps, Data Modelling, Data warehouse developers, Business / Data Analysis and other toolsets.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data Analytics Skills
You need to have a deep understanding of data patterns, data analysis techniques and statistical methods. You should be able to identify and analyze patterns in data, ingest and transform data, create predictive models, and interpret data insights.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Programming Skills
You should be familiar with programming languages such as T-SQL, DAX, MDX, C Sharp, BIML, Powershell, R or Python, as these skillsets might be required for Data Analytics projects including automating data workflows, build machine learning models, and create custom visualizations.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Non-Technical Skills
You need to have strong communication skills to effectively collaborate with stakeholders, such as business leaders or subject matter experts. Additionally, you should have project management skills to plan and execute the project effectively.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Resources
You need access to relevant data sources, including structured and unstructured data. You also need access to software and tools, including Microsoft Office, Power BI, and Azure, as well as hardware and infrastructure for data storage and processing.
Implementing Analytics As A Service Model for Microsoft On-Prem Toolset:
To undertake a business intelligence project using Microsoft SQL Server and Power Platform, you will need a combination of technical skills and resources. Some of the essential skills and resources you should consider include:
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
SQL Server
A comprehensive understanding of SQL Server is critical, as it is the foundation of the project. You should have a thorough understanding of how to build and optimize queries, design databases, and manipulate data using T-SQL.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Power BI
Power BI is a suite of business analytics tools used to analyze data and share insights. You should have a good understanding of Power BI to visualize your data and create reports and dashboards.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
PowerApps
PowerApps is a tool for building custom business applications without coding. If you plan to build custom applications, you will need to have a good understanding of PowerApps.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data Modeling
Data modeling is the process of creating a conceptual representation of data, which allows you to understand the data structure and relationships between tables. You should have a good understanding of data modeling to design efficient data structures.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
ETL (Extract, Transform, and Load)
ETL is the process of extracting data from various sources, transforming it, and loading it into a target database. You should have a good understanding of ETL tools like SSIS to automate this process.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Analytics
You should have a good understanding of analytics to interpret the data and provide meaningful insights to business users.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Project Management
You should have strong project management skills to manage the project timeline, budgets, and resources effectively.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Team Collaboration
Collaboration is essential for the success of any business intelligence project. You should have effective communication skills to collaborate with different teams, including developers, data analysts, and business users.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Infrastructure
You should have access to the necessary infrastructure, including hardware, software, and networking resources.
Overall, a successful business intelligence project requires a combination of technical expertise, analytical skills, and project management skills.
What Infrastructure do we need for On-Prem Data Analytics projects ?
Some of the infrastructure components that may be required for Business Intelligence (BI) project includes:
SQL Server Database Engine: The SQL Server Database Engine is the core component of SQL Server and is responsible for managing data storage, retrieval, and processing. It is required for any SQL Server BI project as it provides the foundation for storing and accessing data.
SQL Server Integration Services (SSIS): SSIS is a component of SQL Server that provides data integration and transformation functionality. It is used to extract data from different sources, transform it into a format suitable for analysis, and load it into a data warehouse or data mart.
SQL Server Analysis Services (SSAS): SSAS is a component of SQL Server that provides online analytical processing (OLAP) and data mining functionality. It is used to create multidimensional data models and to perform complex analysis on large datasets. SSAS is commonly used for creating cubes and data mining models. There are 2 types available – multidimensional cube and tabular cubes.
SQL Server Reporting Services (SSRS): SSRS is a component of SQL Server that provides reporting functionality. It is used to create, manage, and deliver reports that provide insights into business data. SSRS supports a wide range of report types, including tabular, matrix, and chart reports.
Data Warehouse or Data Mart: A data warehouse or data mart is a database used for storing large volumes of data from different sources in a format optimized for analysis. It is a key component of any SQL Server BI project as it provides a central repository for business data.
Business Intelligence Tools: There are a variety of tools available that can be used with SQL Server BI projects, including Power BI, Excel, and SharePoint. These tools are used to create reports and dashboards, perform data analysis, and provide insights into business data.
Hardware and Infrastructure: The hardware and infrastructure required for a SQL Server BI project will depend on the size of the data and the number of users. For smaller projects, a single server may be sufficient, while larger projects may require a multi-server environment with load balancing, clustering, and high availability.
In summary, a Microsoft SQL Server BI project requires a variety of infrastructure components, including the SQL Server Database Engine, SSAS, SSRS, and SSIS. It also requires a data warehouse or data mart, business intelligence tools, and hardware and infrastructure suitable for the size of the project. By understanding these infrastructure components, you can ensure that your SQL Server BI project is properly configured and optimized for performance. And we can help to configure your BI infrastructure.
What Infrastructure do we need for Data Analytics projects in Cloud ?
Some of the infrastructure components that may be required for a Microsoft Azure Business Intelligence (BI) project includes:
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Azure SQL Database
Azure SQL Database is a fully managed cloud-based relational database service that provides a scalable and highly available platform for storing and managing business data. It is the foundation of any Azure BI project as it provides the primary data storage and processing capabilities.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Azure Analysis Services (AAS)
Azure Analysis Services is a cloud-based service that provides OLAP and data mining functionality. It is used to create and deploy scalable and highly available multidimensional data models that can be accessed by BI tools such as Power BI.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Azure Data Factory (ADF)
Azure Data Factory is a cloud-based data integration service that provides a scalable and efficient way to move and transform data between different sources and destinations. It is used to extract data from different sources, transform it into a format suitable for analysis, and load it into a data warehouse or data mart.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Azure Stream Analytics (ASA)
Azure Stream Analytics is a cloud-based service that provides real-time stream processing and analytics. It is used to process and analyze high-volume, streaming data in real-time, and can be integrated with other Azure services such as Event Hub and IoT Hub.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data Warehouse or Data Lake
A data warehouse or data lake is a cloud-based storage service used for storing large volumes of data from different sources in a format optimized for analysis. It is a key component of any Azure BI project as it provides a central repository for business data.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Business Intelligence Tools
There are a variety of BI tools available that can be used with Azure BI projects, including Power BI, Excel, and SharePoint. These tools are used to create reports and dashboards, perform data analysis, and provide insights into business data.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Hardware and Infrastructure
The hardware and infrastructure required for an Azure BI project will depend on the size of the data and the number of users. Azure provides a scalable and highly available platform that can be scaled up or down as needed, and can be integrated with other Azure services such as Azure Virtual Machines, Azure Load Balancer, and Azure Traffic Manager.
In summary, a Microsoft Azure BI project requires a variety of infrastructure components, including Azure SQL Database, AAS, ADF, and ASA. It also requires a data warehouse or data lake, business intelligence tools, and hardware and infrastructure suitable for the size of the project. By understanding these infrastructure components, you can ensure that your Azure BI project is properly configured and optimized for performance.
How SQL Server can help you deliver ?
SQL Server is a relational database management system (RDBMS). It provides a secure, scalable, and high-performance platform for managing large amounts of data. To work with SQL Server, you should have a comprehensive understanding of its features and functionalities. Some of the essential skills include:
Creating and managing databases: You should know how to create and manage databases, tables, views, and indexes in SQL Server. This includes designing appropriate data structures, setting up relationships between tables, and creating constraints to ensure data integrity.
Querying data: You should be able to write efficient and optimized queries using T-SQL (Transact-SQL), the programming language used to query SQL Server. This includes selecting data from tables, filtering and sorting data, and using functions and aggregate functions to perform calculations.
Performance tuning: You should know how to optimize the performance of SQL Server by analyzing query execution plans, indexing strategies, and memory allocation. This includes identifying bottlenecks and using techniques like query optimization and index tuning to improve performance.
Security: You should have a good understanding of SQL Server security features and be able to manage user permissions and roles, set up auditing, and implement encryption and data masking.
Backup and Recovery: You should know how to set up backup and recovery strategies to protect data against loss or corruption. This includes creating backup schedules, testing recovery procedures, and implementing disaster recovery plans.
In summary, having a strong understanding of SQL Server is essential for building and managing a robust data infrastructure. It allows you to design efficient data structures, write optimized queries, and ensure data security and integrity.
Importance of Data Modeling and Data Warehousing in Business Intelligence project
Data modeling is the process of designing a data structure that supports efficient storage, retrieval, and analysis of data. In a business intelligence project, data modeling is critical as it determines how data is organized, stored, and analyzed. A well-designed data model can provide insights into business performance and enable informed decision-making.
Data warehousing is the process of consolidating data from different sources into a central repository, typically a data warehouse. Data warehousing allows for efficient data management, analysis, and reporting. It involves the integration of data from various sources, cleaning and transforming data, and loading the data into the data warehouse.
To work with data modeling and data warehousing in a business intelligence project, you should have a good understanding of:
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data modeling concepts
You should understand the basics of data modeling, including entity-relationship diagrams, normalization, and denormalization.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data warehousing architecture
You should know how data warehouses are designed, including the use of data marts, star schema, and snowflake schema.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
ETL (Extract, Transform, Load) processes
You should understand the ETL process, which involves extracting data from source systems, transforming the data into the desired format, and loading the data into the data warehouse.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data profiling and cleansing
You should know how to profile and cleanse data to ensure data quality and accuracy. This involves identifying missing, duplicate, or inconsistent data and resolving these issues.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Dimensional modeling
You should be familiar with dimensional modeling, which is a technique used to design data warehouses. This involves identifying dimensions (such as time, geography, or product) and fact tables (which contain numeric data).
In summary, data modeling and data warehousing are critical for a successful business intelligence project. They involve designing a data structure that supports efficient storage, retrieval, and analysis of data, integrating data from various sources, and ensuring data quality and accuracy. By understanding these concepts and techniques, you can build a robust data infrastructure that provides insights into business performance and enables informed decision-making.
Importance of Data Visualization and Reporting
Data visualization is the process of presenting data in a graphical or visual format, making it easier to understand and interpret. Data visualization is a critical component of a business intelligence project as it helps stakeholders make informed decisions based on insights drawn from the data.
To work with data visualization and reporting in a business intelligence project, you should have a good understanding of:
Data visualization best practices: You should know the best practices for creating effective data visualizations, including choosing the right chart type, using color effectively, and avoiding clutter.
Reporting tools: You should be familiar with reporting tools such as Microsoft Power BI or SQL Server Reporting Services (SSRS), which allow you to create interactive and visually appealing reports.
Dashboard design: You should know how to design effective dashboards that provide a snapshot of key performance indicators (KPIs) and allow stakeholders to monitor business performance in real-time.
User experience design: You should understand the principles of user experience (UX) design, which involve designing interfaces that are easy to use and understand, and that help users achieve their goals.
Data storytelling: You should know how to create a compelling narrative around the data, using storytelling techniques to help stakeholders understand the insights and make informed decisions.
In summary, data visualization and reporting are critical for a successful business intelligence project. They involve presenting data in a graphical or visual format, using reporting tools and dashboard design best practices, and creating a compelling narrative around the data. By understanding these concepts and techniques, you can create effective data visualizations that help stakeholders make informed decisions based on insights drawn from the data.
Importance of Business Analysis
Business analysis is the process of identifying business needs and determining solutions to business problems. In a business intelligence project, business analysis plays critical role as it helps identify key performance indicators (KPIs) and define requirements for data analysis and reporting. To work with business analysis in a business intelligence project, you should have a good understanding of:
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Business analysis techniques
You should know the different techniques used in business analysis, such as SWOT analysis, PESTEL analysis, and Porter’s Five Forces.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data-driven decision-making
You should understand how data can be used to inform business decisions, and how to identify KPIs that are critical to the success of the business.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Requirement gathering
You should know how to gather requirements for data analysis and reporting, and how to translate business requirements into technical requirements.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Stakeholder management
You should understand how to manage stakeholders and communicate effectively with different teams and departments to ensure alignment and buy-in.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Business process improvement
You should know how to identify areas for process improvement and how to design solutions that streamline business processes and improve efficiency.
In summary, business analysis is critical for a successful business intelligence project. It involves identifying business needs, defining requirements for data analysis and reporting, and using data to inform business decisions. By understanding business analysis techniques and best practices, you can identify key performance indicators, gather requirements, manage stakeholders, and improve business processes, all of which are essential for a successful business intelligence project.
Why Team Collaboration is one of the important step ?
Collaboration is essential for the success of any business intelligence project, as it involves working with different teams and stakeholders to achieve the project goals. Effective communication and collaboration skills are crucial to ensure that everyone involved in the project understands their role and responsibilities and works together to deliver a high-quality solution.
Some of the essential skills required for team collaboration in a business intelligence project include:
Communication: Effective communication is essential for ensuring that everyone involved in the project is on the same page. It involves conveying information clearly and concisely, actively listening to feedback, and responding appropriately. Communication can take many forms, including meetings, emails, instant messaging, or video conferencing.
Collaboration tools: Collaboration tools like Microsoft Teams, Slack, or Asana can help teams work together more effectively. These tools provide a platform for sharing information, assigning tasks, tracking progress, and discussing issues.
Conflict resolution: Conflicts can arise during the project, and it’s essential to have conflict resolution skills to address them effectively. This involves identifying the root cause of the conflict, listening to different perspectives, and finding a mutually acceptable solution.
Flexibility and Adaptability: Business intelligence projects can be complex and challenging, and it’s essential to be flexible and adaptable. This involves being open to new ideas and approaches, willing to learn new skills, and adjusting course when necessary.
In summary, effective team collaboration is crucial for the success of a business intelligence project. It involves communication, collaboration tools, conflict resolution, flexibility and adaptability, and empathy. By working together effectively, teams can overcome challenges, deliver high-quality solutions, and achieve project goals.
Importance of Data Security
Data security is the practice of protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction. In a business intelligence project, data security is critical as it ensures that sensitive data is protected from unauthorized access, and that data privacy regulations are adhered to.
To work with data security in a business intelligence project, you should have a good understanding of:
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data protection regulations
You should know the data protection regulations that apply to your organization, such as the Australian Privacy Act 1998, General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), or the Health Insurance Portability and Accountability Act (HIPAA).
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data classification
You should know how to classify data based on its sensitivity and how to apply appropriate security controls based on the data classification.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data access controls
You should know how to apply access controls to data to ensure that only authorized users have access to sensitive data.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Data encryption
You should know how to encrypt data to protect it from unauthorized access, both in transit and at rest.
![](https://technologycue.com.au/wp-content/uploads/2019/09/1_Power-BI-Consulting.png)
Security monitoring and auditing
You should know how to monitor data access and usage, and how to audit access logs to detect and respond to potential security incidents.
In summary, data security is critical for a successful business intelligence project. It involves protecting sensitive data from unauthorized access, adhering to data privacy regulations, and applying appropriate security controls based on the data classification. By understanding data protection regulations, data classification, access controls, encryption, and security monitoring, you can ensure that sensitive data is protected and that data privacy regulations are adhered to.
So don’t let your business fall behind the competition. Contact us today to learn how our Analytics As A Service solution can help you make data-driven decisions and increase your bottom line.
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Suit 4, 7 Curban Street, Underwood QLD 4119, Australia
Phone
1300 984 835
Email Address
info@technologycue.com.au