Analytics is essential for organizations to gain meaningful and actionable insights from their data. As data volumes grow exponentially, in-house analytics requires massive infrastructure investments, skills, and effort. This is where adopting Analytics-as-a-Service (AaaS) solutions from the cloud makes strategic sense.
What is Analytics-as-a-Service?
Analytics-as-a-Service (AaaS) refers to cloud-based analytics solutions and services provided by a third-party service provider via a subscription model. With AaaS, organizations can leverage advanced analytics capabilities like predictive modeling, machine learning, AI, and complex event processing without building them in-house.
Microsoft Azure provides a robust set of Cognitive Services that can be leveraged to build intelligent AaaS solutions. Azure Cognitive Services provides pre-built machine learning models for vision, speech, language, search, and decision support. By tapping into these cloud-hosted models using standard programmatic interfaces, new categories of analytics solutions can be quickly enabled.
The advantages of an AaaS model based on Azure Cognitive Services include quick time-to-value, reduced costs, flexibility, advanced analytics powered by AI/ML, scalability, and no need for deep data science skills. Organizations can focus more on delivering business value than model development and infrastructure management.
This article covers examples and benefits of building AaaS solutions leveraging the broad range of Azure Cognitive Services capabilities. The power of AI and advanced analytics can be democratized for organizations to unlock hidden insights in data that drive innovation.
Microsoft Azure Cognitive Services for AaaS
Azure Cognitive Services are cloud-based AI models for vision, speech, language, search, and decision support. These pre-built capabilities allow adding intelligent analytics functionality without building custom machine learning models.
Some of the primary Azure Cognitive Service categories relevant to analytics solutions include:
Computer Vision - Advanced image classification and recognition capabilities using machine learning for scenarios like object detection, optical character recognition, etc.
Custom Vision - To quickly build and deploy customized computer vision models tailored to niche image recognition needs like products, logos, defects, etc.
Text Analytics - Sophisticated natural language processing models out of the box for sentiment analysis, key phrase extraction, language detection, etc, applied to text content.
Anomaly Detector - Time series anomaly detection, i.e., uncovering abnormal spikes or drops in temporal data. Applicable across manufacturing, energy, IoT, etc.
By leveraging these mature Cognitive Services for specific AI-driven analytical tasks, the specialized models and infrastructure are fully managed by Microsoft Azure. This enables accelerated development of AaaS solutions for actionable and predictive insights across use cases.
Key benefits include ready-to-consume pre-built AI, encryption of data at rest and in transit, redundancy and backup for reliability, and availability in data centers globally for low latency. Security, privacy, compliance, transparency, and responsible AI safeguards are also ensured.
Use Cases and Industry Examples
The pre-built AI capabilities that Azure Cognitive Services offer can be leveraged to transform processes and deliver analytical insights across sectors:
Retail
- Analyze in-store video feeds using Computer Vision to track customer demographics, foot traffic, dwell times, queue lengths, etc.
- Custom Vision is for automated visual inspection in manufacturing - identify defects, misalignments, etc.
- Text Analytics provides sentiment analysis - monitor social media and review sites for brand perception.
- Form Recognizer's optical character recognition (OCR) extracts text and data from images of documents like receipts, invoices, etc.
Receipt Understanding Use Case
- Capture images of retail customer receipts.
- Leverage Form Recognizer's pre-built receipt model to automatically extract critical fields of data, including:
- Store name
- Transaction date/time
- List of items purchased
- Taxes
- Total amount paid
- Aggregate and analyze receipts data to gain shopper insights:
- basket sizes
- top selling items
- seasonal purchase patterns
- price sensitivity
- promotions effectiveness
OCR is an essential Azure Cognitive Service that unlocks image text data for analysis. Combined with AI document model understanding, entire classes of forms and documents like receipts, invoices, and contracts become an automated input for more innovative analytics!
Healthcare
- Leverage Form Recognizer to ingest and extract structured information from medical claims and paperwork to expedite processing
- Use natural language processing to analyze doctor-patient conversations and medical history reports to surface relevant symptoms and conditions.
Financial Services
- Apply Anomaly Detector on time series transaction data to quickly detect potential fraud.
- Audio transcription and speech analytics for customer service call monitoring
Power and Utilities
- Forecast energy demand fluctuations using automated machine learning to model multi-seasonal time series data.
- Predict renewable output volatility using weather data correlation
The above covers a small sample of impactful analytics application areas where Azure Cognitive Services delivers the AI muscle. Hundreds of rich machine learning models pre-developed to kickstart the most ambitious analytics innovation.
Benefits of a Microsoft AaaS Approach
They are choosing Azure Cognitive Services as the engine for analytics as a service (AaaS) solutions, providing tremendous benefits over traditional self-managed business intelligence and significant data approaches.
The massive amount of data available today from diverse sources and formats poses a challenge for organizations to store, process, and analyze data cost-effectively. With AaaS on Azure, pre-built AI models tackle the complexity while cloud scale handles the data volumes - without needing expertise or infrastructure.
Key advantages include:
Rapid Deployment: Near zero setup time. Instantly leverage advanced AI functionality for predictive analytics and data visualization using simple APIs connected to data sources—no need for data warehouse modeling and management.
Agile Data Exploration: Query different datasets with ease to uncover insights. Dynamic integration capabilities keep pace with new emerging data sources. Try experiments rapidly without prolonged data analytics software development.
Improved Decisions: Consume the latest AI innovations in vision, speech, language, and predictive modeling. Empowers more accurate forecasting and informed decisions aligned with business objectives.
Enhanced Focus: Instead of tooling and infrastructure management, concentrate analytics personnel exclusively on extracting meaning from ever-growing data. Democratizes cutting-edge analytics use cases.
The breadth of Azure Cognitive Services spans typical data analysis needs for most enterprises today. As AaaS solutions mature, benefits become abundantly clear for sectors to rethink approaches.
Pros and cons of analytics-as-a-service, such as Microsoft Cognitive Services
Pros:
- Microsoft takes care of the details. This makes it easier to create an application and, thus, to try out new ideas.
- Microsoft will (hopefully) update the algorithms based on the latest research, so you don't have to!
- As with any public cloud-based solution, MSCS enables flexible use of resources.
Cons:
- With the ease of use gained through APIs, some expressivity is lost. For applications such as trading, which are so fundamental to the value added by an organization that relative superiority to other users is critical, analytics-as-a-service may not be ideal if it does not provide adequate support for fine-tuning the algorithms.
- Because this client is a Microsoft shop using .NET, Azure, and VSTS (Visual Studio Team Service), it made sense to go with the Microsoft product for this project.
Implementation of AaaS solutions with Azure Cognitive Services
The pre-built AI models for vision, speech, language, and decision-making provided by Azure Cognitive Services allow for the rapid implementation of analytics-as-a-service solutions. Integration with Azure Cognitive Services typically involves:
Data Collection
- We are ingesting relevant datasets from diverse sources - IoT sensors, databases, apps, social media, etc.
- Azure provides extensive data pipeline capabilities, including batch, streaming, and hybrid.
- Serverless options like Azure Functions greatly simplify building data ingestion triggers.
Processing
- Step functions to orchestrate running different analytics tasks on ingested data.
- Azure Databricks, HDInsight for distributed computing of large analytics workflows
- Azure Synapse Cosmos DB to host processed analytic datasets
Analytics Execution
- Calling desired Cognitive Services containers for target capabilities using standard REST APIs
- Containers run on Azure Kubernetes Service clusters for easy scale-out
- Integrate into apps, PowerBI dashboards, or custom portals
Monitoring
- Application Insights provides distributed tracing live metrics monitoring to tune and enhance AaaS solutions continually
- Log Analytics is for deeper infrastructure analytics when debugging or using usage analysis.
The above represents typical analytics-as-a-service architecture on Azure. Fully managed infrastructure, security, and reliability ensure that the focus stays on driving value from AI-powered insights rather than overhead.
Other AaaS Providers
Microsoft is not the only game in town providing analytics services for financial and other applications. There are various advanced analytics tools like IBM. Their Cloud offering leverages Watson (yes, the Jeopardy player) to provide analytics in domains including investment management. Bottlenose, another, uses sources from various data streams to offer analytical insights in areas including finance, competitive intelligence, and risk estimation. Then there is Domo, which provides a platform by which organizations can access analytics not via APIs (at least not yet) but via numerous 3rd party apps.
PoC: The Need
One of our clients manages prepaid cards for their clients, making it possible for them to track their expenses. Often, corporate customers wish to track the costs of their employees and make data-driven decisions. In the old days, traveling employees laid out funds for their expenses and submitted expense reports for review and reimbursement. Our client allows these expenses to be prepaid by the company, removing the need for employees to use their funds and sift through old receipts. But how do you validate these expenses when employees might, through their error or malice, submit receipts that should not qualify for spending through the card? Most immediately, how do you even pull information from them?
PoC: The Solution
For this problem, the balance tipped in favor of using analytics-as-a-service, particularly Microsoft Cognitive Services.
The corporate customer's employee scans and uploads a picture of their receipt, taken with their smartphone. Softjourn's proof of concept (POC) sends it to MSCS's Computer Vision API for optical character recognition (OCR). This pulls out editable lines of text from the receipt image, which are returned to the POC, along with indications of Microsoft's confidence level in this result. Text with a low confidence level must be sent to be read by a human; other text contains errors that Softjourn can correct automatically. Next, templates encoding standard receipt formats are selected and applied to extract the various vital pieces of information from the receipt, including transaction total, amount of tax, card charged, and establishment address. This allows for the initial validation of transaction totals. Receipts from designated vendors may already have an approval code ΜΆΜΆ for others; there may be more work to do for proof. To this end, MSCS also returns a "raciness" indicator (checking for a situation where the expense ought to be rejected for lack of relevance/appropriateness).
The Benefits
- Money. Reduced costs due to the automation of the transaction validation process.
- Reliability. Minimized human error.
The Benefits
- Money. Reduced costs due to the automation of the transaction validation process.
- Reliability. We minimized human error.
Conclusion
The growing power of data analytics is undeniable across industries today. Organizations strive to transform extensive, complex data sets into data-driven insights to guide strategic decisions and fuel business growth. However, robust analytics capabilities require significant investments in infrastructure and teams of data scientists and engineers - resources better spent on core business needs for most companies.
This is where adopting AaaS pays dividends. AaaS provides various analytics services on an agile, managed cloud platform, from descriptive to prescriptive techniques. Leading examples include Salesforce Einstein Analytics, Google Analytics 360, and more.
Benefits of AaaS include:
- Access to advanced analytics tools without significant capital investments
- Ability to process large data volumes and transform raw data into trends
- Pre-built connectors to quickly analyze in-house data alongside external sources
- Security and privacy certifications to ensure data protection regulations are met
- Intuitive dashboards empowering business users to self-serve insights
- Augmented analytics and machine learning to enhance data quality and analysis
- Pay-as-you-go pricing to scale analytics needs up or down
By leveraging AaaS, teams spend less time cleaning data and maintaining infrastructure. Instead, the focus shifts to higher-value initiatives - interpreting insights, identifying new data sources, and ultimately driving decisions through analytics embedded across the business. Democratizing access removes barriers, so more staff base decisions on data.
While in-house analytics talent focuses on specialized modeling, AaaS securely opens analytics accessibility for fact-based growth. The platforms, technology, and methods will continue advancing. Organizations should evaluate providers based on total capability now and in the future to maintain a competitive edge with analytics-fueled initiatives.
