SELF SERVICE AUDIT ANALYTICS

AWS BBC Case Study: Challenges Faced by BBC

The BBC Archives Technology and Services division is responsible for preserving over 16 million assets, including films, radio broadcasts, news, sports, and digital material. However, managing this vast repository came with significant challenges: • Fragmentation: Content was dispersed across multiple legacy storage systems, making data retrieval complex and inefficient. • High Costs: Maintaining outdated physical infrastructure demanded heavy financial and resource investments. • Limited Accessibility: Lack of a centralized system led to time-consuming content retrieval processes. Many global enterprises face similar struggles in balancing data accessibility with cost efficiency. To address these challenges, the BBC implemented a five-year plan to consolidate its storage using modern cloud technologies.

Key Benefits of Amazon S3 Glacier Instant Retrieval

Building on years of successful collaboration with Amazon Web Services (AWS), the BBC adopted Amazon S3 Glacier Instant Retrieval to modernize its archival strategy. Key benefits of Amazon S3 solution include: • Cost-effectiveness: Amazon S3 Glacier Instant Retrieval offers some of the lowest-cost storage for petabytes of archival data. • Instant Access: Unlike traditional archival storage, this AWS solution provides rapid retrieval speeds, making it ideal for time-sensitive content. • Scalability: AWS’s robust cloud infrastructure ensures seamless expansion as data volumes grow, future-proofing the BBC’s archives. This transition solved immediate storage challenges and laid the groundwork for a scalable, digitized archive that will serve future generations.

The Migration Journey: 25 PB in 10 Months

Over a span of 10 months, the BBC successfully migrated 25 petabytes (PB) of archival data to AWS. Legacy System Retirement: Enabled decommissioning legacy tape-based storage, freeing up space and IT resources at its London HQ. Enhanced Cost Efficiency: Reduced operational costs by integrating Amazon S3 Glacier Instant Retrieval and Amazon S3 Intelligent-Tiering. Optimized Storage Management: Automated data tiering based on access patterns to balance cost and performance. Improved Data Accessibility: Ensured seamless access to historical media archives for future content innovation.

Building a Future-Ready Data Lake

The BBC’s cloud migration is not just about cost savings and accessibility—it’s about innovation. With its archival content securely stored on AWS, the broadcaster now focuses on developing a comprehensive data lake. This centralized repository will power advanced analytics and machine learning (ML) applications, unlocking new capabilities such as: • Speech-to-text processing for historical broadcasts • Facial recognition for identifying individuals in archival footage • Automated metadata tagging to enhance searchability and categorization By embracing Amazon S3 Glacier Instant Retrieval, the BBC is building an infrastructure that will preserve its media legacy and revolutionize how historical content is accessed and utilized in the digital age.

Amazon S3 Case Study Examples: Lessons for Organizations Everywhere

The BBC’s experience provides a valuable blueprint for organizations looking to modernize their data management strategies. Key takeaways of this BBC Amazon S3 Case Study include: • Modernization is Essential: Cloud-based solutions like Amazon S3 Glacier Instant Retrieval significantly reduce operating costs and enhance data accessibility. • Scalability Matters: AWS storage solutions offer seamless expansion, ensuring long-term sustainability. • Future-Proofing Archives: A centralized data lake paves the way for leveraging machine learning, AI, and advanced analytics, unlocking new insights from historical data. Adopting AWS archive storage solutions can benefit organizations across industries, including media, government, and education, by ensuring efficient, cost-effective, and future-ready data management.

Summing Up

The BBC’s successful migration of 100 years of archival content to Amazon S3 Glacier Instant Retrieval is a testament to the transformative power of cloud-based archival storage. By overcoming high costs, fragmented data storage, and limited accessibility, the BBC has preserved its invaluable media heritage and set a new industry standard for digital archiving.

What is Data Visualization?

Data visualization is the process of representing data in a graphical or spatial format, allowing for easy visual analysis without technical jargon. Unlike raw numerical data, visual representations like charts, graphs, and maps help quickly identify patterns, trends, and anomalies, facilitating faster and more accurate insights.

Benefits of Data Visualization

Understanding raw data can be challenging due to its complexity. Data visualization addresses this by:

1. Simplify Data Interpretation: Converting raw data into charts and graphs makes it easier to understand underlying patterns and relationships.
2. Identify Trends and Anomalies: Visual formats highlight trends and anomalies that might be missed in numerical data.
3. Improve Accessibility: Data visualization makes information accessible to a broader audience, including those without strong analytical skills, thus improving data-driven decision-making across departments.
4. Advanced Data Storytelling: Effective visualization techniques turn data into compelling stories that facilitate better communication and understanding.

What Are Data Visualization Tools?

Data visualization tools provide designers with an efficient way to create visual representations of large data sets. When dealing with data sets that include hundreds of thousands or millions of data points, automating the visualization process simplifies the designer's job considerably.

Key Benefits of Data Visualisation Tools

1. Dashboards: To monitor and analyze key performance indicators (KPIs) and metrics in real-time.
2. Annual Reports: To present data-driven insights to stakeholders in a clear and engaging manner.
3. Sales and Marketing Materials: To showcase trends, performance, and forecasts to potential clients and customers.
4. Investor Slide Decks: To communicate financial health and growth prospects effectively.
5. General Information Interpretation: To make complex data understandable for decision-making processes in virtually any context where quick interpretation of information is necessary.

A Compact List of Top Data Visualization Tools

Here are the top enterprise data visualization tools for creating compelling visualizations:

1. Tableau
2. Google Charts
3. Zoho Analytics
4. Data Wrapper

Tableau

Tableau is a top-tier platform recognized for its user-friendly interface. It adeptly integrates data from multiple sources to create dynamic and visualisations.
Its comprehensive suite of products spans desktop applications, robust server solutions, and flexible web-hosted environments, empowering organizations to drive informed decision-making and achieve actionable insights across their operations.
Connect with us for a free demo: https://www.beinex.com/free-tableau-software

Who Should Use Tableau?

Key Features of Tableau

• User-Friendly Interface: Easy to learn and navigate, making it accessible for all skill levels. • Mobile-Friendly: Create reports and dashboards optimised for mobile devices, allowing you to access and analyse data on the go. • High Performance: Efficiently handles large datasets, ensuring seamless analysis without performance issues. • Interactive Visualizations: Build interactive and dynamic visualisations, allowing deeper data exploration. • Integration Capabilities: Integrates well with various data sources and other business applications, enhancing data connectivity. • Real-Time Data Updates: Provides real-time data updates, ensuring you have the most current insights. • Collaboration Tools: Facilitates easy sharing and collaboration on reports and dashboards within teams. • Customizable Dashboards: Offers highly customisable dashboards to meet specific business needs and preferences. • Advanced Analytics: Supports advanced analytics features, including trend analysis, forecasting, and statistical summaries. • Security: Ensures data security with robust access controls and permissions.

Google Charts

Google Charts is a free tool for creating interactive data visualisations, accessible through most web browsers. It supports various data sources, including spreadsheets and databases.

Who Should Use Google Charts?

Key Features of Google Charts

• User-Friendly Interface: Easy to use with a straightforward setup process. • Wide Range of Chart Types: Supports various chart types, including line, bar, pie, scatter, and more. • Customizable: Offers extensive customization options to tailor charts to specific needs, including colors, fonts, and annotations. • Interactive Charts: Allows for interactive elements such as tooltips, zooming, and panning. • Cross-Platform Compatibility: Ensures charts work seamlessly across different browsers and devices. • Dynamic Data Updates: Supports real-time data updates, keeping charts current with live data feeds. • Integration with Google Services: Easily integrates with other Google services such as Google Sheets and Google Analytics. • Embedding Capabilities: Simple embedding in websites and applications with a few lines of code. • Data Export Options: Provides options to export charts in various formats, including PNG, SVG, and PDF. • Open Source: Free and open source, allowing for extensive customization and community support. • Support for Multiple Data Formats: Works with various data formats, including JSON, CSV, and Google Spreadsheets. • Accessibility Features: These include features to make charts accessible to all users, including screen reader support. • Responsive Design: Ensures charts are responsive and adapt to different screen sizes and resolutions. • Powerful API: Provides a robust API for developers to create complex visualisations and integrate them into applications.

Zoho Analytics

Who Should Use Zoho Analytics?

Key Features of Zoho Analytics

• User-Friendly Interface: Intuitive design that simplifies data analysis and visualisation. • Wide Range of Data Sources: Connects to various data sources, including databases, cloud storage, spreadsheets, and other business applications. • Advanced Analytics: Offers features such as predictive analytics, AI-powered insights, and what-if analysis. • Interactive Dashboards: Create and customise interactive dashboards with drag-and-drop ease. • Collaboration Tools: Facilitate sharing and collaboration on reports and dashboards within teams. • Embedded Analytics: Embed reports and dashboards into websites, applications, and portals. • Automated Data Sync: Schedule data imports and synchronise data regularly. • Customizable Visualizations: Provides a variety of chart types and extensive customisation options for data visualisations. • Data Blending: Combine data from multiple sources for comprehensive analysis. • AI-Driven Insights: Leverages AI to offer advanced analytical insights and pattern detection. • Real-Time Data Access: Supports real-time data integration and live dashboards. • Mobile Access: Access and interact with reports and dashboards on mobile devices. • Data Security: Ensures robust security features, including role-based access control, encryption, and compliance with industry standards. • Report Scheduling: Automate the distribution of reports through scheduled emails. • Integrations with Other Zoho Apps: Seamlessly integrate with other Zoho applications for enhanced functionality. • API Support: Provides APIs for developers to integrate analytics capabilities into custom applications. • Data Preparation Tools: These include tools for data cleaning, transformation, and enrichment.

Data Wrapper

Who Should Use Data Wrapper?

Key Features of Data Wrapper

• User-Friendly Interface: Intuitive and easy to use, requiring no coding skills to create professional charts and maps. • Wide Range of Chart Types: Supports a variety of chart types, including bar, line, pie, scatter plots, and maps. • Customisable Visualizations: Offers extensive customisation options for colors, labels, and annotations to match branding and presentation needs. • Responsive Design: Ensures charts and maps are responsive and adapt to different screen sizes and devices. • Interactive Elements: Allows for adding interactive elements like tooltips, hover effects, and clickable legends. • Real-Time Data Integration: Supports live data updates, enabling real-time visualisation. • Embedding Capabilities: Easily embed charts and maps into websites and blogs with simple code snippets. • Export Options: This option lets users download visualisations in various formats, including PNG, PDF, and SVG. • Accessibility: Committed to creating accessible visualisations with features that support screen readers and keyboard navigation. • Data Security: Ensures data security with robust privacy policies and compliance with industry standards. • Collaboration Tools: Allows for team collaboration with shared projects and editing capabilities. • Support for Multiple Languages: Offers multi-language support for creating visualisations in different languages. • Easy Data Import: Supports importing data from various sources, including CSV files, spreadsheets, and web links. • API Integration: Provides APIs for integrating Data wrapper with other applications and services. • Customizable Templates: Use and modify templates to maintain consistency in visualisations. • Annotation Features: Add rich text annotations directly to charts and maps to provide additional context and information.

Always Go for the Best Tool

By choosing the right data visualisation tool, organizations can discover the full potential of their data, making complex information accessible and actionable for everyone. This empowers businesses to make data-driven decisions that drive growth and innovation.

What is AWS Security Hub?

Features of AWS Security Hub

Top Benefits of AWS Security Hub

What is an SLM?

A Small Language Model (SLM) is tailored to excel in simpler tasks, offering boosted accessibility and user-friendliness for organizations operating with limited resources. Besides, they can be readily fine-tuned to align with specific requirements. Small language models are particularly well-suited for organizations aiming to develop applications capable of operating local devices instead of relying on cloud infrastructure. They are especially beneficial for tasks that do not necessitate extensive reasoning or immediate responses.

Reasons to use SLMs

Given the growing popularity and applicability of SLMs across various domains, particularly in areas like sustainability and the volume of data required for training, there are multiple reasons for employing them.

What is Phi-3?

Microsoft has a suite of small language models (SLMs) known as 'Phi,' demonstrating outstanding performance across various benchmarks. Microsoft's recent release is Phi-3, a series of open AI models. The Phi-3 models represent a prototype of capability and cost-effectiveness among small language models (SLMs), exceeding models of equivalent and larger sizes across the spectrum of coding, language, reasoning, and mathematical standards. This launch broadens the array of high-calibre models accessible to customers, providing them with more practical options as they craft and construct generative AI applications.

Phi-3-mini, a 3.8B language model, is accessible through Microsoft Azure AI Studio, Hugging Face, and Ollama. It is offered in two context-length variations—4K and 128K tokens. Notably, it is the first model within its category to support a context window of up to 128K tokens with minimal impact on quality. Furthermore, it is instruction-tuned, implying that it has been trained to comprehend and adhere to diverse instructions, mirroring natural human communication patterns. This ensures that the model is readily deployable straight out of the box. Phi-3-mini is available on Azure AI to leverage the deploy-eval-finetune toolchain, and it is also accessible on Ollama for developers to execute locally on their laptops.

Features of Phi-3

Phi-3 models exhibit distinctive superiority over language models of comparable and larger dimensions on key benchmarks, showcasing the following features:

Snowflake meets Phi-3: Advantages  

The key pain point about LLMs is the computing required to host and run them. Setting up a dozen GPUs to run models can be expensive and complex. There's where Snowflake steps up. Snowflake's compute pool option enables users to easily and quickly set up and manage compute clusters. Phi-3 comes into the picture because of its cost-effective GPU utilization.

Can you imagine a situation where your language model only requires less than 3GB of GPU memory for inference? Well, now it's possible, all thanks to Phi-3. It's a state-of-the-art SLM that produces excellent results over GP3.5 and Mistral 8x7B, which are much bigger models. This opens the door for more cost-effective solutions to be brought up in the AI space. Add Snowflake for hosting; you have an excellent setup to host, test, and build AI applications. Read below how Beinex managed to run Phi-3 on Day 0 in Snowflake.  Figure 1: DocAI running on Phi-3

Implementing Phi-3 on Snowflake: What Beinex Did and How Beinex Did it?

Beinex has seamlessly integrated Phi-3 into Snowflake to help enterprises unlock their data's full potential through advanced language processing capabilities and enhance decision-making with deeper insights. The integration facilitates Snowflake users to:

Here's a detailed guide on implementing Phi-3 on Snowflake:
Step 1: Create Necessary Objects  -- Run by ACCOUNTADMIN to allow connecting to Hugging Face to download the model  -- Stage to store LLM models  CREATE STAGE <stagename> IF NOT EXISTS models  DIRECTORY = (ENABLE = TRUE)  ENCRYPTION = (TYPE='SNOWFLAKE_SSE');    -- Stage to store YAML specs  CREATE STAGE <stagename> IF NOT EXISTS specs  DIRECTORY = (ENABLE = TRUE)  ENCRYPTION = (TYPE='SNOWFLAKE_SSE');    -- Image repository  CREATE OR REPLACE IMAGE REPOSITORY images;    -- Compute pool to run containers  CREATE COMPUTE POOL GPU_NV_S    MIN_NODES = 1    MAX_NODES = 1    INSTANCE_FAMILY = GPU_NV_S;  Step 2: Docker Image Code - ollama  FROM ollama/ollama    RUN $(ollama serve > output.log 2>&1 &) && sleep 10 && ollama pull phi3 && pkill ollama && rm output.log    ENTRYPOINT ["ollama"]  CMD ["serve"]  Step 3: Tag and Push the Docker Image  docker tag ollama <SNOW_ORG-SNOW_ACCOUNT>.registry.snowflakecomputing.com/db/schema/image respository /ollama  docker push <SNOW_ORG-SNOW_ACCOUNT>.registry.snowflakecomputing.com db/schema/image repository /ollama  Step 4: Docker Image - UDF  FROM python:3.11    WORKDIR /app  ADD ./requirements.txt /app/    RUN pip install --no-cache-dir -r requirements.txt    ADD ./ /app    EXPOSE 5000    ENV FLASK_APP=app  CMD ["flask", "run", "--host=0.0.0.0"]  App.py content is given below :  from flask import Flask, request, Response, jsonify  import logging  import re  import os  from openai import OpenAI    client = OpenAI(      base_url='http://ollama:11434/v1',      api_key="EMPTY",  )    model = "phi3"    app = Flask(__name__)  app.logger.setLevel(logging.ERROR)      def extract_json_from_string(s):      logging.info(f"Extracting JSON from string: {s}")      # Use a regular expression to find a JSON-like string      matches = re.findall(r"\{[^{}]*\}", s)        if matches:          # Return the first match (assuming there's only one JSON object embedded)          return matches[0]        # Return the original string if no JSON object is found      return s      @app.route("/", methods=["POST"])  def udf():      try:          request_data: dict = request.get_json(force=True)  # type: ignore          return_data = []            for index, col1 in request_data["data"]:              completion = client.chat.completions.create(                  model=model,                  messages=[                      {                          "role": "system",                          "content": "You are a bot to help extract data and should give professional responses",                      },                      {"role": "user", "content": col1},                  ],              )              return_data.append(                  [index, extract_json_from_string(completion.choices[0].message.content)]              )            return jsonify({"data": return_data})      except Exception as e:          app.logger.exception(e)          return jsonify(str(e)), 500  Step 6: YAML File  spec:    containers:      - name: ollama        image: <SNOW_ORG-SNOW_ACCOUNT>.registry.snowflakecomputing.com/ db/schema/image respository /Phi3        resources:          requests:            nvidia.com/gpu: 1          limits:            nvidia.com/gpu: 1        env:          NUM_GPU: 1          MAX_GPU_MEMORY: 24Gib        volumeMounts:          - name: llm-workspace            mountPath: /<stage name>      - name: udf        image: <SNOW_ORG-SNOW_ACCOUNT>.registry.snowflakecomputing.com/ db/schema/image respository /ollama_udf    endpoints:      - name: chat        port: 5000        public: false      - name: llm        port: 11434        public: false    volumes:      - name: llm-workspace        source:  "@<llm stage_name>"  Step 7: Upload YAML File and Create Service  Upload the YAML file to the created stage, where the stage name in the YAML file should match the stage created in Step 2.  -- Create service  create service phi3  IN COMPUTE POOL <name of compute pool created>  FROM @dash_stage  SPECIFICATION_FILE = '<name of yaml file uploaded>';  Step 8: Create Service Function  Create a service function on the service (after it starts).  create or replace function phi3chat(prompt text)  returns text  service= phi3  endpoint=chat;  Check Service Status  Use the following command to check the status of the service:  SELECT     v.value:containerName::varchar container_name,    v.value:status::varchar status,    v.value:message::varchar message  FROM (    SELECT parse_json(system$get_service_status('<service name>'))  ) t,   LATERAL FLATTEN(input => t.$1) v; 

Benefits of Running Phi-3 on Snowflake

1. Cost-Effectiveness and Efficiency:

2. Compatibility with Smaller GPUs:

3. Exceptional Performance:

4. Faster Response Times:

SLM vs LLM

The choice between small and large language models hinges on organizational needs, task complexity, and resource availability.

LLMs excel in applications requiring the orchestration of intricate tasks, encompassing advanced reasoning, data analysis, and contextual comprehension.

On the other hand, SLMs present viable options for regulated industries and sectors facing scenarios necessitating top-tier results while maintaining data within their premises.

Both large and small language models possess distinct strengths and applications. While large language models thrive in managing complex workflows, small language models deliver impressive performance despite their compact size.

While some customers may exclusively require small models, others may favour larger models, with many seeking to integrate both types in various configurations. Ultimately, the optimal selection depends on the unique context and objectives of the organization. Besides transitioning from large to small models, the trend is evolving towards a diversified portfolio of models. This means that instead of relying on a single model, customers can choose from various models with different sizes, capabilities, and resource requirements. This empowers customers to decide the best model for their scenario, balancing performance and resource constraints.

Four Ways Alteryx Automation and AI Can Transform Your Marketing Strategy:

While there are numerous objectives marketing teams can achieve with data analytics, this blog highlights four ways Alteryx automation and AI can transform your marketing strategy:

1. Centralize Your Data

As the marketing landscape prepares for a cookie-less future, having a unified view of your data is essential. Staying ahead of customer needs, competition, and campaigns requires gathering all your data in one place.
With analytics automation, you can easily integrate data from various sources—whether cloud or on-premises, first-party data, or marketing applications like web analytics and CRMs—to gain a comprehensive view of your customers. This enables marketing teams to react to market shifts in real time.

Use case:

How Alteryx Helps:

2. Enhance Your Marketing Campaigns

Marketing success depends on speed and agility, especially when it comes to predicting market trends and competitor behavior. Optimizing targeting, pricing, or strategy without the right insights becomes a challenge. Analytics automation helps you find the right combination of offers and tactics to increase conversions and boost revenue.

Use Case:

How Alteryx Helps:

3. Maximize Your Talent and Resources

Many marketing teams struggle to turn data into valuable business insights. According to Gartner, only 53% of marketing decisions are informed by data analytics. Limited staff and time often prevent teams from fully utilizing their data potential.
Analytics automation bridges this gap by enabling teams to achieve more with fewer resources. It automates the time-consuming tasks of data cleaning and preparation, allowing marketing teams to save significant hours and focus on more strategic projects.

Use Case:

How Alteryx Helps:

4. Achieve Immediate Results While Preparing for the Future

Marketing leaders often juggle the challenge of balancing short-term returns with long-term strategic goals. Analytics automation solutions can provide quick wins while also laying a foundation for future success.
By choosing a solution that is user-friendly and easy to implement, you can skip lengthy training sessions and start seeing results quickly. Moreover, the best analytics tools are designed with the future in mind, offering integration with cloud services and AI-driven insights.

Use Case:

How Alteryx Helps:

How Marketing Teams Can Benefit from Alteryx

With Alteryx, marketing teams can benefit from:
• Self-Service Analytics: A user-friendly, drag-and-drop interface, you can easily access and analyze data without technical expertise. • Pre-Built Analytical Tools: Utilize pre-configured tools for market basket analysis, spatial analytics, and more without needing custom development. • Seamless Integration: Integrate Alteryx with your existing marketing tech stack for a cohesive, end-to-end analytics solution.

Alteryx+ Beinex Offerings

Our Premier partnership with Alteryx empowers business users to automate manual data cleansing and transformation tasks in minutes through a simple visual workflow while incorporating the latest technological advancements.
Connect with us for a free demo: https://beinex.com/alteryx-partner/