For such a case, the data analyst examining the problem can use traces with triggers based on processing time. But there must be an easier way to spot anomalies.
Traditional monitoring involves recording the performance of the infrastructure and applications. Data observability allows you to track your data flows and find faults in them (may even extend to business processes). While traditional tools analyze infrastructure and applications using metrics, logs, and traces, data observability uses data analysis in a broader sense.

So, how do we tackle the case of no new account creation in 24 hours?

The data analyst could use traces with time-based triggers. There has to be an easier way of detecting potential anomalies on site.

A machine learning model is used to predict future events, such as the volume of future sales, by utilizing regularly updated historical data. However, because the input data may not always be of perfect quality, the model can sometimes produce inaccurate forecasts. These inaccuracies can lead to either excess inventory for the retailer or, worse, out-of-stock situations when there is consumer demand.

Classifying and Addressing Unplanned Events

The point of Data Observability is to identify so-called data downtime. Data Downtime refers to a sudden unplanned event in your business/infrastructure/code that leads to a sudden change in the data. In other words, it is the process of finding anomalies in data.

How can you classify these events?

• Exceeding a given metric value or an abnormal jump in a given metric. This type is the simplest. Imagine that you add 80-120 clients every day (confidence interval with some probability), and in one day, only 20. Perhaps something caused it to drop suddenly, and it’s worth looking into.
• Abrupt change in data structure. Let’s take a past example with clients. Everything was fine, but one day, the contact information field began to receive empty values. Perhaps something has broken in your data pipeline, and it’s better to check.
• The occurrence of a certain condition or deviation from it. Just as GPS coordinates should not show a truck in the ocean, banking transactions should not suddenly appear in unexpected locations or in unusual amounts that deviate significantly from the norm.
• Statistical anomalies. During a routine check, the bank’s analysts notice that on a particular day, the average ATM withdrawal per customer spiked to $500, which is significantly higher than the historical average.

On the one hand, it seems that there is nothing new in this approach of classifying abnormal events and taking the necessary remedial action. But on the other hand, previously there were no comprehensive and specialized tools for these tasks.

Data Observability is Essential for Ensuring Fresh, Accurate, and Smooth Data Flow

Data observability serves as a checkup for your systems. It lets you ensure your data is fresh, accurate, and flowing smoothly, helping you catch potential problems early on.

Tiger Analytics’ Continuous Observability Solution

Continuous monitoring and alerting of potential issues (gathered from various sources) before a customer/operations reports an issue. Consists of Set of tools, patterns and practices to build Data Observability components for your big data workloads in Cloud platform to reduce DATA DOWNTIME.

Select examples of our experience in Data observability and Quality

Tools and Technology

Tiger Analytics Data Observability is set of tools, patterns and best practices to:

• Ingest MELT(Metrics, Events, Logs, Traces) data
• Enrich, Store MELT for getting insights on Event & Log Correlations, Data Anomalies, Pipeline Failures, Performance Metrics
• Configure Data Quality rules using a Self Service UI
• Monitor Operational Metrics like Data quality, Pipeline health, SLAs
• Alert Business team when there is Data Downtime
• Perform Root cause analysis
• Fix broken pipelines and data quality issues

Which will help:

• Minimize data downtime using automated data quality checks
• Discover data problems before they impact the business KPIs
• Accelerate Troubleshooting and Root Cause Analysis
• Boost productivity and reduce operational cost
• Improve Operational Excellence, QoS, Uptime

Data observability and Generative AI (GenAI) can play crucial roles in enhancing data-driven decision-making and machine learning (ML) model performance.

The combination of data observability primes the pump by instilling confidence with smooth sailing, high-quality and always available data which forms a foundation for any data-driven initiative while GenAI enables to realize what is achievable through it, opening up new avenues into how we can simulate, generate or even go beyond innovate. Organizations can use both to improve their data capabilities, decision-making processes, and innovation with different areas.

Thus, Monte Carlo, a company that produces a tool for data monitoring, raised $135 million, Observe – $112 million, Acceldata – $100 million have an excellent technology medium in the Data Observability space.

To summarize

Data Observability is an approach to identifying anomalies in business processes and the operation of applications and infrastructure, allowing users to quickly respond to emerging incidents.It lets you ensure your data is fresh, accurate, and flowing smoothly, helping you catch potential problems early on.

And if there is no particular novelty in technology, there is certainly novelty in the approach, tools and new terms that make it possible to better convince investors and clients. The next few years will show how successful new players will be in the market.

References

https://www.oreilly.com/library/view/data-observability-for/9781804616024/
https://www.oreilly.com/library/view/data-quality-fundamentals/9781098112035/

The post What is Data Observability Used For? appeared first on Tiger Analytics.

“This approach could change everything,” her CEO remarked during her presentation. “How do we implement it across our entire operation?”

E’s answer lay in a comprehensive framework designed to unlock the full potential of their data. In this article, we’ll explore Tiger Analytics’ innovative 5-point Graph Value framework – a roadmap that guides businesses from establishing a foundational Knowledge Graph to leveraging advanced AI capabilities for deeper insights.

The 5-Point Graph Value

At Tiger Analytics, we have identified a connected 5-point Graph Value framework that enables businesses to unlock the true potential of their data through a phased approach, leading to transformative insights and decision-making. The 5-point Graph Value framework consists of three distinct phases, each building upon the previous one to create a comprehensive and powerful solution for data-driven insights.

Phase 1: Knowledge Graph (Base)

The first phase focuses on establishing a solid foundation with the Knowledge Graph. This graph serves as the base, connecting all the relevant data points and creating a unified view of the business ecosystem. By integrating data from various sources and establishing relationships between entities, the Knowledge Graph enables businesses to gain a holistic understanding of their operations.

In this phase, two key scenarios demonstrate the power of the Knowledge Graph:

1. Connect All Dots
Role-based Universal View: Gaining a Holistic Understanding of the Business
A business user needs to see a connected view of Product, Plant, Material, Quantity, Inspection, Results, Vendor, PO, and Customer complaints. With a Knowledge Graph, this becomes a reality. By integrating data from various sources and establishing relationships between entities, the Knowledge Graph provides a comprehensive, unified view of the product ecosystem. This enables business users to gain a holistic understanding of the factors influencing product performance and customer satisfaction, leading to context-based insights for unbiased actions.

2. Trace & Traverse
Trace ‘Where Things’: Context-based Insights for R&D Lead
An R&D Lead wants to check Package material types and their headspace combination patterns with dry chicken batches processed in the last 3 months. With a Knowledge Graph, this information can be easily traced and traversed. The graph structure allows for efficient navigation and exploration of the interconnected data, enabling the R&D Lead to identify patterns and insights that would otherwise be hidden in traditional data silos. This trace and traverse capability empowers the R&D Lead to make informed decisions based on a comprehensive understanding of the data landscape.

Phase 2: Connected Context Graph

Building upon the Knowledge Graph, the second phase introduces the Connected Context Graph. This graph incorporates the temporal aspect of data, allowing businesses to discover patterns, track changes over time, and identify influential entities within their network.

Two scenarios showcase the value of the Connected Context Graph

3. Discover more Paths & Patterns
Uncover Patterns: Change History and its weighted impacts for an Audit
An auditor wants to see all the changes that happened for a given product between 2021 and 2023. With a Connected Context Graph, this becomes possible. The graph captures the temporal aspect of data, allowing for the discovery of patterns and changes over time. This enables the auditor to identify significant modifications, track the evolution of the product, and uncover potential areas of concern. The Connected Context Graph provides valuable insights into the change history and its weighted impacts, empowering the auditor to make informed decisions and take necessary actions.

4. Community Network
Network Community: Identifying Influencers and Optimizing Processes
A business user wants to perform self-discovery on the Manufacturer and Vendor network for a specific Plant, Products, and Material categories within a specific time window. The Connected Context Graph enables the identification of community networks, revealing the relationships and interdependencies between various entities. This allows the business user to identify key influencers, critical suppliers, and potential risk factors within the network. By understanding the influential entities and their impact on the supply chain, businesses can optimize their processes and make strategic decisions to mitigate risks and improve overall performance.

Phase 3: Auto-Answers with AI

The final phase of the 5-point Graph Value framework takes the insights derived from the Knowledge Graph and Connected Context Graph to the next level by augmenting them with AI capabilities. This phase focuses on leveraging AI algorithms to identify critical paths, optimize supply chain efficiency, and provide automated answers to complex business questions.

The scenario in this phase illustrates the power of AI integration:

5. Augment with AI
Optimizing Supply Chain Critical Paths and Efficiency
A Transformation Lead wants to identify all the critical paths across the supply chain to improve green scores and avoid unplanned plant shutdowns. By augmenting the Knowledge Graph with AI capabilities, this becomes achievable. AI algorithms can analyze the graph structure, identify critical paths, and provide recommendations for optimization. This enables the Transformation Lead to make data-driven decisions, minimize risks, and improve overall operational efficiency. The integration of AI with the Knowledge Graph opens up new possibilities for business process optimization, workflow streamlining, and value creation, empowering organizations to stay ahead in today’s competitive landscape.

A 360-Degree View of Your Product with Context Graphs

By leveraging Knowledge Graphs, businesses can unlock a complete 360-degree view of their products, encompassing every aspect from raw materials to customer feedback. Graph capabilities enable organizations to explore the intricate relationships between entities, uncover hidden patterns, and gain a deeper understanding of the factors influencing product performance. From context-based search using natural language to visual outlier detection and link prediction, graph capabilities empower businesses to ask complex questions, simulate scenarios, and make data-driven decisions with confidence. In the table below, we will delve into the various graph capabilities that can enhace the way you manage and optimize your products.

Use Cases of Context Graphs Across Your Product

Graph Implementation Roadmap

The adoption of Context Graphs follows a structured roadmap, encompassing various levels of data integration and analysis:

• Connected View (Level 1): The foundational step involves creating a Knowledge Graph (KG) that links disparate enterprise data sources, enabling traceability from customer complaints to specific deviations in materials or processes.
• Deep View (Level 2): This level delves deeper into the data, uncovering hidden insights and implicit relationships through pattern matching and sequence analysis.
• Global View (Level 3): The focus expands to a global perspective, identifying overarching patterns and predictive insights across the entire network structure.
• ML View (Level 4): Leveraging machine learning, this level enhances predictive capabilities by identifying key features and relationships that may not be immediately apparent.
• AI View (Level 5): The pinnacle of the roadmap integrates AI for unbiased, explainable insights, using natural language processing to facilitate self-discovery and proactive decision-making.

Leveraging LLMs and KGs

A significant advancement in Context Graphs is the integration of Large Language Models (LLMs) with Knowledge Graphs (KGs), addressing challenges such as knowledge cutoffs, data privacy, and the need for domain-specific insights. This synergy enhances the accuracy of insights generated, enabling more intelligent search capabilities, self-service analytics, and the construction of KGs from unstructured data.

Context Graph queries are revolutionizing our machine learning and AI systems. They are enabling these systems to make informed and nuanced decisions swiftly. With these tools, we can preemptively identify and analyze similar patterns or paths in raw materials lots even before they commence the manufacturing process.

This need to understand the connections between disparate data points is reshaping how we store, connect, and interpret data, equipping us with the context needed for more proactive and real-time decision-making. The evolution in how we handle data is paving the way for a future where immediate, context-aware decision-making becomes a practical reality.

The post Implementing Context Graphs: A 5-Point Framework for Transformative Business Insights appeared first on Tiger Analytics.

The company’s traditional methods of analysis, rooted in linear data-crunching, were proving to be insufficient. They pointed to various potential causes: a shipment of substandard raw materials, a series of human errors, unexpected deviations in manufacturing processes, mismatches in component ratios, and even inconsistent additives in packaging materials. The list was exhaustive, but the connections were elusive.

The issue was complex-no single factor was the culprit. E needed to trace and compare the key influencers and their patterns, not just within a single time frame but across the tumultuous period between 2021 and 2022. The domino effect of one small issue escalating into a full-blown crisis was becoming a daunting reality.

To trace the key influencers and their patterns across the tumultuous period between 2021 and 2022, E needed a tool that could capture and analyze the intricate relationships within the data. At Tiger Analytics, we recognized the limitations of conventional approaches and introduced the concept of the Product Knowledge Graph, powered by Neo4j. The concept of the Context Graph, a term we coined to describe a specialized graph-based data structure. This specialized sub-graph from the Master Graph emphasized the contextual information and intricate connections specific to the issue at hand. It provided a visual and analytical representation that weighted different factors and their interrelations.

The Context Graph illuminated the crucial 20% of factors that were contributing to 80% of the problems—the Pareto Principle in action. By mapping out the entire journey from raw material to customer feedback, the Context Graph enabled E to pinpoint the specific combinations of factors that were causing the majority of the complaints. With this clarity, E implemented targeted solutions to the most impactful issues.

What is a Context Graph and Why we need it?

In today’s complex business landscape, traditional databases often fall short in revealing crucial relationships within data. Context Graphs address this limitation by connecting diverse data points, offering a comprehensive view of your business ecosystem.

“The term Context Graph refers to a graph-based data structure (sub-graph from Master Graph) used to represent the contextual information, relationships, or connections between data entities, events, and processes at specific points at the time. It might be used in various applications, such as enhancing natural language understanding, recommendation systems, or improving the contextual awareness of artificial intelligence.”

At Tiger Analytics, we combine graph technology with Large Language Models to build Product Knowledge Graphs, unifying various data silos like Customer, Batch, Material, and more. The power of Context Graphs lies in their ability to facilitate efficient search and analysis from any starting point. Users can easily query the graph to uncover hidden insights, enhance predictive analytics, and improve decision-making across various business functions.

By embracing Context Graphs, businesses gain a deeper understanding of their operations and customer interactions, paving the way for more informed strategies and improved outcomes.

This comprehensive approach is set to redefine the landscape of data-driven decision-making, paving the way for enhanced predictive analytics, risk management, and customer experience.

6 Ways Graphs Enhance Data Analytics

1. Making Connections Clear: If data is like a bunch of dots, by itself, each dot doesn’t tell you much. A Context Graph connects these dots to show how they’re related. This is like drawing lines between the dots to make a clear picture.

2. Understanding the Big Picture: In complex situations, just knowing the facts (like numbers and dates) isn’t enough. You need to understand how these facts affect each other. Context Graphs show these relationships, helping you see the whole story.

3. Finding Hidden Patterns: Sometimes, important insights are hidden in the way different pieces of data are connected. Context Graphs can reveal these patterns. For example, in a business, you might discover that when more people visit your website (one piece of data), sales in a certain region go up (another piece of data). Without seeing the connection, you might miss this insight.

4. Quick Problem-Solving: When something goes wrong, like a drop in product quality, a Context Graph can quickly show where the problem might be coming from. It connects data from different parts of the process (like raw material quality, production dates, and supplier information) to help find the source of the issue.

5. Better Predictions and Decisions: By understanding how different pieces of data are connected, businesses can make smarter predictions and decisions. For example, they can forecast which product combo will be popular in the future or decide where to invest their resources for the best results.

6. Enhancing Artificial Intelligence and Machine Learning: Context Graphs feed AI and machine learning systems with rich, connected data. This helps these systems make more accurate and context-aware decisions, like identifying fraud in financial transactions or personalizing recommendations for customers.

The power of Context Graphs in solving complex business problems is clear. By illuminating hidden connections and patterns in data, these graph-based structures offer a new approach to decision-making and problem-solving. From E’s product quality crisis to broader applications in predictive analytics and AI, Context Graphs are changing how businesses understand and utilize their data.

In Part 2 of this series, we’ll delve deeper into the practical aspects, exploring a framework approach to implementing these powerful graph structures in your organization.

The post Connected Context: Introducing Product Knowledge Graphs for Smarter Business Decisions appeared first on Tiger Analytics.

With advancements in artificial intelligence (AI) and machine learning (ML), the demand for real-time and accurate data ingestion has reached new heights. AI and ML models, require a constant feed of high-quality data to train, adapt, and deliver accurate insights and predictions. Consequently, organizations must prioritize robust data ingestion strategies to harness the full potential of their data assets and stay competitive in the AI-driven era.

Challenges with Existing Data Ingestion Mechanisms

While platforms like Snowflake offer powerful data warehousing capabilities, the native data ingestion methods provided by Snowflake, such as Snowpipe and the COPY command, often face limitations that hinder scalability, flexibility, and efficiency.

Limitations of the COPY Method

• Data Transformation Overhead: Extensive transformation during the COPY process can introduce overhead, which is better performed post-loading.
• Limited Horizontal Scalability: COPY struggles to scale efficiently with large data volumes, underutilizing warehouse resources.
• File Format Compatibility: Complex formats like Excel require preprocessing for compatibility with Snowflake’s COPY INTO operation.
• Data Validation and Error Handling: Snowflake’s validation during COPY is limited; additional checks can burden performance.
• Manual Optimization: Achieving optimal performance with COPY demands meticulous file size and concurrency management, adding complexity.

Limitations of Snowpipe

• Lack of Upsert Support: Snowpipe lacks direct upsert functionality, necessitating complex workarounds.
• Limited Real-Time Capabilities: While near-real-time, Snowpipe may not meet the needs for instant data availability or complex streaming transformations.
• Scheduling Flexibility: Continuous operation limits precise control over data loading times.
• Data Quality and Consistency: Snowpipe offers limited support for data validation and transformation, requiring additional checks.
• Limited Flexibility: Snowpipe is optimized for streaming data into Snowflake, limiting custom processing and external integrations.
• Support for Specific Data Formats: Snowpipe supports delimited text, JSON, Avro, Parquet, ORC, and XML (using Snowflake XML format), necessitating conversion for unsupported formats.

Tiger’s Snowpark-Based Framework – Transforming Data Ingestion

To address these challenges and unlock the full potential of data ingestion, organizations are turning to innovative solutions that leverage advanced technologies and frameworks. One such solution we’ve built, is Tiger’s Snowpark-based framework for Snowflake.

Our solution transforms data ingestion by offering a highly customizable framework driven by metadata tables. Users can efficiently tailor ingestion processes to various data sources and business rules. Advanced auditing and reconciliation ensure thorough tracking and resolution of data integrity issues. Additionally, built-in data quality checks and observability features enable real-time monitoring and proactive alerting. Overall, the Tiger framework provides a robust, adaptable, and efficient solution for managing data ingestion challenges within the Snowflake ecosystem.

Key features of Tiger’s Snowpark-based framework include:

Configurability and Metadata-Driven Approach:

• Flexible Configuration: Users can tailor the framework to their needs, accommodating diverse data sources, formats, and business rules.
• Metadata-Driven Processes: The framework utilizes metadata tables and configuration files to drive every aspect of the ingestion process, promoting consistency and ease of management.

Advanced Auditing and Reconciliation:

• Detailed Logging: The framework provides comprehensive auditing and logging capabilities, ensuring traceability, compliance, and data lineage visibility.
• Automated Reconciliation: Built-in reconciliation mechanisms identify and resolve discrepancies, minimizing errors and ensuring data integrity.

Enhanced Data Quality and Observability:

• Real-Time Monitoring: The framework offers real-time data quality checks and observability features, enabling users to detect anomalies and deviations promptly.
• Custom Alerts and Notifications: Users can set up custom thresholds and receive alerts for data quality issues, facilitating proactive monitoring and intervention.

Seamless Transformation and Schema Evolution:

• Sophisticated Transformations: Leveraging Snowpark’s capabilities, users can perform complex data transformations and manage schema evolution seamlessly.
• Adaptability to Changes: The framework automatically adapts to schema changes, ensuring compatibility with downstream systems and minimizing disruption.

Data continues to be the seminal building block that determines the accuracy of the output. As businesses race through this data-driven era, investing in robust and future-proof data ingestion frameworks will be key to translating data into real-world insights.

The post Tiger’s Snowpark-Based Framework for Snowflake: Illuminating the Path to Efficient Data Ingestion appeared first on Tiger Analytics.

Their workday is consumed by the arduous task of migrating complex Excel data from legacy systems to Snowflake. They spend hours grappling with detailed Excel files, trying to navigate through multiple headers, footers, subtotals, formulas, macros, and custom formatting. The manual process is time-consuming, and error-prone, and hinders their ability to focus on deriving valuable insights from the data.

To streamline their workday, the data analyst can leverage Snowpark Python’s capabilities to streamline the process. They can effortlessly access and process Excel files directly within Snowflake, eliminating the need for external ETL tools or complex migration scripts. With just a few lines of code, they can automate the extraction of data from Excel files, regardless of their complexity. Formulas, conditional formatting, and macros are handled seamlessly, ensuring data accuracy and consistency.

Many businesses today grapple with the complexities of Excel data migration. Traditional ETL scripts may suffice for straightforward data migration, but heavily customized processes pose significant challenges. That’s where Snowpark Python comes into the picture.

Snowpark Python: Simplifying Excel Data Migration

Snowpark Python presents itself as a versatile tool that simplifies the process of migrating Excel data to Snowflake. By leveraging Snowpark’s file access capabilities, users can directly access and process Excel files within Snowflake, eliminating the need for external ETL tools or complex migration scripts. This approach not only streamlines the migration process but also ensures data accuracy and consistency.

With Snowpark Python, businesses can efficiently extract data from Excel files, regardless of their complexity. Python’s rich ecosystem of libraries enables users to handle formulas, conditional formatting, and macros in Excel files. By integrating Python scripts seamlessly into Snowflake pipelines, the migration process can be automated, maintaining data quality throughout. This approach not only simplifies the migration process but also enhances scalability and performance.

Tiger Analytics’ Approach to Excel Data Migration using Snowpark Python

At Tiger Analytics, we‘ve worked with several Fortune 500 clients on data migration projects. In doing so, we’ve found a robust solution: using Snowpark Python to tackle this problem head-on. Here’s how it works.

We crafted Snowpark code that seamlessly integrates Excel libraries to facilitate data loading into Snowflake. Our approach involves configuring a metadata table within Snowflake to store essential details such as Excel file names, sheet names, and cell information. By utilizing Snowpark Python and standard stored procedures, we have implemented a streamlined process that extracts configurations from the metadata table and dynamically loads Excel files into Snowflake based on these parameters. This approach ensures data integrity and accuracy throughout the migration process, empowering businesses to unlock the full potential of their data analytics workflows within Snowflake. So we’re able to not only accelerate the migration process but also future-proof data operations, enabling organizations to focus on deriving valuable insights from their data.

The advantage of using Snowpark Python is that it enables new use cases for Snowflake customers, allowing them to ingest data from specialized file formats without the need to build and maintain external file ingestion processes. This results in faster development lifecycles, reduced time spent managing various cloud provider services, lower costs, and more time spent adding business value.

For organizations looking to modernize data operations and migrate Excel data from legacy systems into Snowflake, Snowpark Python offers a useful solution. With the right partners and supporting tech, a seamless data migration will pave the way for enhanced data-driven decision-making.

The post Migrating from Legacy Systems to Snowflake: Simplifying Excel Data Migration with Snowpark Python appeared first on Tiger Analytics.

Three Roadblocks on the Journey from Data-to-Insights

In our work with several Fortune 500 clients across domains, we’ve observed the path to actionable insights extracted from data is hindered by a trifecta of formidable bottlenecks that often prolong time to value for businesses.

• The pressing need for personalized insights tailored to each user’s role
• The escalating complexities of dashboard development, and
• The constant stream of updates and modifications required to keep pace with evolving business needs

As companies navigate this challenging landscape, the integration of Generative AI (GenAI) into BI processes presents a promising solution, empowering businesses to unlock the true potential of their data and stay ahead in an increasingly competitive market.

Challenge 1: Lack of persona-based insights

Every user persona within an organization has different insight requirements based on their roles and responsibilities. Let’s look at real-world examples of such personas for a CPG firm:

• CEOs seek insights into operational efficiency and revenue, focusing on potential risks and losses
• Supply Chain Managers prioritize information about missed Service Level Agreements (SLAs) or high-priority orders that might face delays
• Plant Managers are interested in understanding unplanned downtime and its impact on production

Hence, the ability to slice and dice data for ad-hoc queries is crucial for gaining technical know-how. However, the challenge lies in catering to these diverse needs while ensuring each user gets relevant insights tailored to their roles. Manual data analysis and reporting may not pass the litmus test, as it can be too time-consuming and may not be able to provide granularity as desired by the key stakeholders.

Challenge 2: Growing complexities of dashboard development

Creating multiple dashboards to meet the diverse needs of users requires a lot of time and effort. It typically involves extensive stakeholder discussions to understand their requirements, leading to extended development cycles. The process becomes more intricate as organizations strive to strike the right balance between customization and scalability. With each additional dashboard, the complexity grows, potentially leading to data silos and inconsistencies. Dependency on analysts for ad-hoc analysis also causes more delays in generating actionable insights. The backlog of ad-hoc requests can overwhelm the BI team, diverting their focus from strategic analytics.

Managing various dashboard versions, data sources, and user access permissions adds another layer of complexity, making it difficult to ensure consistency and accuracy.

Challenge 3: Too many updates and modifications

The relentless need to update and modify the dashboard landscape puts immense pressure on the BI teams, stretching their resources and capabilities. Rapidly shifting priorities and data demands can lead to a struggle to align with the latest strategic objectives. Also, constant disruptions to existing dashboards can create user reluctance and hinder the adoption of data-driven decision-making across the organization.

Plus, as businesses grow and evolve, their data requirements change. It leads to constant updates/modifications– triggering delays in delivering insights, especially when relying on traditional development approaches. As a result, the BI team is often overwhelmed with frequent requests.

Empowering BI through GenAI

What if anyone within the organization could effortlessly derive ad-hoc insights through simple natural language queries, eliminating the need for running complex queries or dependence on IT for assistance? This is where the integration of GenAI and NLP proves invaluable, streamlining information access for all key users with unparalleled ease and speed.

At Tiger Analytics we developed Insights Pro, a proprietary GenAI platform to overcome these challenges and deliver faster and more efficient data-to-insights conversions.

In a nutshell, by generating insights and streamlining BI workflows, Insights Pro takes on a new approach. Rather than contextualizing data using data dictionary, it leverages the power of LLMs for data dictionary analysis and prompt engineering, thus offering:

• Versatility – Ensures superior data and domain-agnostic performance
• Contextuality – Comes with an advanced data dictionary that understands column definitions and contexts based on session conversations
• Scalability – Spans across different user and verticals

This democratizes access to data-driven insights, reducing the dependency on dedicated analysts. Whether it’s the CEO, Supply Chain Manager, or Plant Manager, they can directly interact with the platform to get the relevant insights on time and as needed.

Empowering Data-Driven Decision-Making | Applications across various industries

Logistics and Warehousing: AI powered BI solutions can assist in optimizing warehouse operations by analyzing shipment punctuality, fill rates, and comparing warehouse locations. It identifies areas for improvement, determines average rates, and pinpoints critical influencing factors to enhance efficiency and streamline processes.

Transportation: Transportation companies can evaluate carrier performance, identify reasons for performance disparities, and assess overall carrier efficiency. It provides insights into performance gaps, uncovers the causes of delays, and supports informed decision-making to optimize transportation networks.

Supply Chain Management: AI powered BI solution empowers supply chain leaders to identify bottlenecks, such as plants with the longest loading times, compare location efficiency, and uncover factors impacting efficiency. It guides leaders towards clarity and success in navigating the complexities of supply chain operations, facilitating data-driven optimization strategies.

Business Intelligence and Analytics: Analysts are equipped with a comprehensive view of key metrics across various domains, such as shipments across carriers, order-to-delivery times, and modeling to understand influencing factors. It bridges data gaps, simplifies complexities, and offers clarity in data analysis, enabling analysts to derive actionable insights and drive data-informed decision-making.

Undeniably, empowering BI through AI can only be achieved by knocking off time-consuming bottlenecks that hinder data-to-insights conversion.

Tiger Analytics’ Insights Pro also goes a long way to combat other challenges that Generative AI has been associated with at an enterprise level. For instance, it ensures that data Security concerns are uploaded to the GPT server as data dictionaries. It also delivers an up-to-date data dictionary so that new business terms shouldn’t be manually defined in the current session.

Looking ahead, NLP and GenAI-powered solutions will break down barriers to data accessibility, automate the discovery of hidden patterns empowering users across organizations to leverage data insights through natural language interactions. By embracing solutions like Insights Pro, businesses can unlock the value of their data, drive innovation, and shape a future where data-driven insights are accessible to all.

The post Empowering BI through GenAI: How to address data-to-insights’ biggest bottlenecks appeared first on Tiger Analytics.

State of the Private Equity (PE) industry

For starters, Private Equity (PE) firms have to work twice as hard to make sense of their data before turning them into actionable insights. This is because their client portfolios are often diverse, as is the data – spread across different industries and geographies, which limits the reusability of frameworks and processes. Furthermore, each client may have its own unique reporting format, which leads to information overflow.

Other data analytics-related challenges that PE firms have to overcome include:

• No reliable sources and poor understanding of non-traditional data
• Archaic and ineffective data management strategy
• Inability to make optimal use of various data assets
• Absence of analytics-focused functions, resources, and tools

These challenges offer a clear indication of why the adoption of data analytics in the PE industry has been low – compared to others. According to a recent study conducted by KPMG, only a few PE firms are currently exploring big data and analytics as a viable strategy, with “70% of surveyed firms still in the awareness-raising stage.”

Why PE firms need to incubate a data-first mindset

So, considering these herculean challenges, why is a data analytics strategy the need of the hour for Private Equity firms? After all, according to Gartner, “By 2025, more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics.”

First, it’s important to understand that as technology continues to skyrocket, a tremendous amount of relevant data is generated and gathered around the clock. And without leveraging data to unearth correlations and trends, they can only rely on half-truths and gut instincts. For instance, such outdated strategies can mislead firms regarding where their portfolio companies can reduce operating costs. Hence, the lack of a data analytics strategy means they can no longer remain competitive in today’s dynamic investment world.

Plus, stakeholders expect more transparency and visibility into the valuation processes. So, Private Equity firms are already under pressure to break down innovation barriers and enable seamless access and utilization of their data assets to build a decision-making culture based on actionable insights. They can also proactively identify good investment opportunities, which can significantly help grow revenue while optimizing the bandwidth of their teams by focusing on the right opportunities.

Some of the other benefits for PE firms are:

• Enriched company valuation models
• Enhanced portfolio monitoring
• Reduced dependency on financial data
• Pipeline monitoring and timely access for key event triggers
• Stronger due diligence processes

Final thoughts

The emergence of data analytics as a game-changer for Private Equity firms has caused some to adopt piecemeal solutions – hoping that it could reap low-hanging fruits. However, this could prove to be hugely ineffective because it would further decentralize the availability of data, which has been this industry’s biggest problem in the first place.

In reality, the key is for Private Equity firms to rethink how they collect data and what they can do with it – from the ground up. There’s no doubt that only by building a data-led master strategy can they make a difference in how they make key investment decisions and successfully navigate a hyper-competitive landscape.

We hope that we helped you understand the current data challenges Private Equity firms face while adopting a data analytics strategy and why it’s still a competitive differentiator. Stay tuned for the next blog in the series, in which we will shed light on how Private Equity firms can overcome these challenges.

The post From Awareness to Action: Private Equity’s Quest for Data-Driven Growth appeared first on Tiger Analytics.

Come 2021, and the three major factors that are challenging traditional insurance value chain are Insurtech, climate change, and the ongoing COVID pandemic. Amid lockdowns, floods, forest fires, and a digital-first world, the industry is having its watershed moment (we briefly touched upon this in our earlier blog). On the one hand, risks that need to be insured are becoming complex. On the other hand, data and technology are enabling insurers to better understand and influence customer behavior.

Given the disruptors are here to stay, insurers are being forced to evolve and be creative every step of the way. Insurance companies now have the opportunity to become something much more than just financial protection providers. A deep dive into the challenges within the insurance value chain sheds light on the innovations being made to not only assess the risks to be covered, but also to mitigate that risk for the customer and the insurer.

Product Design and Distribution

In the current environment, products such as home or property insurance must be designed to mitigate the long-term effects of climate change. Companies, for example, are launching efforts to help clients strengthen their insured assets against extreme weather conditions. Offering climate-proofing of homes and catastrophe-specific coverage for customers living in a risk-prone area are examples of ways in which insurers are moving towards offering more sustainable policies. Adopting geospatial solutions can play a vital role in helping insurers understand environmental risks with precision.

The pandemic has also, undoubtedly, increased the interest in life and health insurance. This has forced insurers to design products that ensure long-term financial and health benefits, all while following a digital-first module. Here, incentives such as virtual gym memberships or a free teleconsultation with a nutritionist integrated into the policy, are being used to attract customers.

Usage-based insurance products are also growing in popularity in 2021, wherein premiums are payable based on the extent to which an activity is performed. The best real-world example of this is motor insurance, where a customer is charged based on the number of miles they drive, rather than paying fixed premiums over a certain period of time. This confronts the COVID and climate conundrum all at once — lockdowns have forced customers to stay indoors and use their cars less, and insurance companies are able to promote themselves as being sustainable, adjustable, and allies in the war against rising carbon emissions.

While the demand for insurance is going up, the pandemic has imposed restrictions on traditional distribution channels for insurance. Insurers and agents, who were accustomed to in-person interactions, had to quickly adopt digital tools such as video chats, chatbots, and self-service websites to sell insurance. The industry is also moving away from captive agents to independent agents and digital insurance exchanges are accelerating this trend. Platforms like Semsee, Bolttech, Bold Penguin, and Uncharted pull data from many insurers, allowing agents to see multiple quotes for policies, similar to how travel agents see competing airfares.

Pricing and Underwriting

Historic data, which forms the basis of pricing and underwriting, needs to be re-examined in the COVID era, and beyond. With the threat of future pandemics and increasing climate change-related disasters, assessing the risk level of customers, whose needs are getting more varied, is going to get more challenging.

One of the ways insurers are tackling this is by moving to a continuous underwriting model from a one-time pricing model. This involves using regularly updated or real-time policyholder data to continuously assess the risk and update the policy terms and premiums accordingly. In addition to providing a better estimation of risk, it helps insurers to adapt to evolving customer needs and influences customers to reduce any risky behavior.

The pandemic is also forcing companies to reduce the physical interactions needed for underwriting. Take life insurance, for example. Given the constraints on in-person medical tests, concepts such as ‘fluidless (no lab tests) processing’ are gaining traction.

Loss and Claims Management

The initial innovations in these last steps of the insurance value chain were primarily focused on post-loss scenarios with the objective of increasing the efficiency of the claim assessment process. Across property and auto insurance, image analytics enables insurance companies to remotely and more effectively assess the loss and identify possible fraudulent claims.

However, the focus is now shifting to loss prevention. Across all types of insurance products, the aim is to track risky behavior and intervene at the right moment to prevent a loss event. This strategy has existed in Health insurance for a while where insurers have tried to ensure medication adherence and a healthy lifestyle through data-driven interventions. Now, thanks to IoT, it is finding its place across other areas like property insurance (detecting water leak or low pressure in a water sprinkler), workers compensations (identifying workers without adequate equipment or unsafe lifting by an employee), and auto insurance (sensing erratic driving behavior). An interesting example from auto insurance is the BlaBlaCar Coach, an app-based service that comes with certain car insurance products and offers drivers personalized tips for safer driving.

This focus on loss prevention is also having an impact on other parts of the insurance value chain like product design and pricing. It is allowing insurers to provide expanded coverage at affordable rates, even for previously uninsurable risks.

What the Future Holds

Risk analysis and crisis aversion were always at the core of the insurance industry. However, what the industry was not prepared for, much like the rest of us, was a global pandemic and coming face-to-face with the effects of climate change. On the other hand, these disruptions may have proven to be the catalyst for innovation in the industry, which was long overdue. One thing is clear – a successful transformation cannot be achieved by technology alone. A data-driven approach will be crucial in effectively leveraging this technology.

Stay tuned for more details on the role of data and how innovative data science solutions are driving value for both insurance companies and their customers.

The post Rethinking Insurance Dynamics in a Changing World appeared first on Tiger Analytics.

Of late though, I have been making conscious efforts to better my game, and seem to be doing fairly well now. This, however, was no easy task. My path became clearer and my efforts more fruitful when I was able to take data analytics along with me into this fantasy world.

So, from one enthusiast to the other, here are my two cents on what can help you create the right team based on numbers, the power of observation, and a little bit of luck.

Consistency is Key

The first and foremost point to keep in mind is finding consistent performers, and there are some tools that can help you determine who is on top of their game that season. Here are some of my obvious picks:

Suryakumar Yadav: My top pick will always be Yadav of the Mumbai Indians. In 2020, he collected a total of 400 runs in just 16 matches. He was consistent in 2018 and 2019 as well by amassing 400+ runs. Yadav made a name for himself from the very beginning of his cricket career, which can be further proved by his consistent performance for Team India as well.

Bhuvaneshwar Kumar: Kumar is a sure-shot player, and, as Michael Vaughn pointed out, he is also possibly the “smartest player” his generation has to offer. Even in ODI and T20 matches in the pre-IPL era, he was always able to out-smart his opponents. From the 151 matches he has played in the IPL, he has maintained an average of 24, and one can always expect wickets from him. Economically too, Kumar is a man to watch out for, as he is in the top five.

David Warner: No matter how the Sunrisers Hyderabad perform, Warner remains consistent, and has managed to remain the highest run-scorer for his team.

KL Rahul: Rahul, with an average of 44, is a consistent player who is also great at stitching together partnerships.

Playing by Numbers

After some years of bungling up, I realized that variables such as economy, average, past performance, etc. can be best understood using data analysis. I have found the platforms Kaggle and Howstat to be useful resources.

Kaggle, which serves as a home for a lot of challenging data science competitions, has an IPL dataset that has ball-by-ball details of all the IPL matches till 2020. That’s 12 years of granular data you can aggregate to get the metrics of your choice. You can get the dataset at this link: https://www.kaggle.com/patrickb1912/ipl-complete-dataset-20082020.

Howstat, on the other hand, has the most frequently used metrics laid out beautifully. Thanks to the folks with whom I play cricket, I came to know about this wonderful website.

Let’s talk about venues specifically, which, as you may know by now, play a critical role in the IPL fantasy world too, and can directly impact the kind of players you pick. In the early days (pre-2010), when Dada would walk onto the pitch at Eden Gardens, the crowd would roar because it was given that he would put up a good show on his home turf. But why take the risk and rely on gut and cheers where numbers can lead you to assured results? Especially when the stakes are so high and the competition so fierce.

Here is where I would look to data analysis to help me make a more informed decision. For example, if one were to refer to KL Rahul’s scores on Howstat, you can see that despite having played the most matches at Chinnaswamy Stadium (15 in total), Rahul’s average at theIS Bindra Stadium is much better. His average is 49.78 at IS Bindra, while at Chinnaswamy Stadium, his average is 38.22.

(Pro-tip: One player who comes to mind not just for his batting skills but also for his ability to perform well across pitches is Shikhar Dhawan. I would definitely include him in my team. He also secures tons of catches which adds to the points tally).

Now some of you may not have the time to sort through the many IPL datasets available on a platform such as Kaggle, which is understandable as even the best of us can be intimidated by numbers. One tip I have for you folk is to merely look at what the numbers point to on your app of choice. By looking at the percentage of people choosing a particular player on the Dream11 app, for example, you can understand which players are on top of their game that season.

This is best determined somewhere around the middle of the season, after around five-six matches, as that is when you will know who is at his peak and whom you can skip from your team.

The Non-Data Way

If you are struggling to make your way through all the numbers, I have some non-statistical tips too, which I learned to include in my game only after my many trials and tribulations.

1. It’s human nature to compare ourselves to others – you know how it goes, the grass is always greener and all that jazz. This leads to mimicry, and while at times it helps to follow in the footsteps of those you aspire to be (on the leaderboard in this case), unfortunately, in 20-20 fantasy, this doesn’t work. The best route is to work out your own strategy and make your own mistakes.

2. Make sure to use boosts and player-transfer options wisely in the initial stages. It’s only normal to want a good leaderboard score while starting out, but this could lead you to exhaust your transfer list very early on, leaving you with the same players through the season. This can also significantly bring down your score. Using sufficient transfers and boosts towards the business end of things (post 20 matches or so) can go a long way.

3. Using the uncapped player-transfer option is also worth exploring. This can reveal a whole range of players and talent from different countries, who haven’t played for Team India, but who are extremely skilled.

4. Coming to all-rounders – my tip would be to have three in your team. This is especially important while selecting your captain and vice-captain. For example, Chris Woakes is one all-rounder who has worked well for me this season before he left.

Use your gut, use your mind

What I can say for certain through this blog, is that nothing is certain in IPL fantasy cricket. Yet, while this may seem like the most unsatisfactory take-away, I can vouch for one thing – data analysis can definitely change your game for the better.

Of course, certain factors are out of our control. Injuries, fouls, poor weather, etc. are an inevitable part of any sport and could significantly change the outcome of a game. But if one dataset or one number-crunch can change how you view a match and give you better insight, wouldn’t that be something worth exploring? In Dhoni’s own words, ‘Bas dimaag laga ke khel’!

The post Achieving IPL Fantasy Glory with Data-Backed Insights and Strategies appeared first on Tiger Analytics.

Importance of commercial waste management

The commercial waste management lifecycle comprises collection, transportation, and disposal. Ensuring that all the waste materials are properly handled throughout the process is a matter of compliance. After all, multiple environmental regulations dictate how waste management protocols should be implemented and monitored. Instituting the right waste management guidelines also helps companies fulfill their ethical and legal responsibility of maintaining proper health and safety standards at the workplace.

For instance, all the waste materials generated in commercial buildings are stored in bins placed at strategic locations. If companies do not utilize them effectively, it will lead to bin overflows causing severe financial, reputational, and legal repercussions.

Impact of data analytics on commercial waste management

Data analytics eliminates compliance issues that stem from overflowing bins by bridging any operational gaps. In addition, it provides the precise know-how for creating intelligent waste management workflows. With high-quality video cameras integrated into the chassis of their waste collection trucks, image-based analytics can be captured and shared through a cloud-hosted platform for real-time visual detection. From these, data insights can be extracted to evaluate when the trash bins are getting filled and schedule the collection to minimize transportation, fuel, and labor expenses. They can also determine the right collection frequency, streamline collection routes, and optimize vehicle loads.

By monitoring real-time data from the video cameras, the flow of waste in each bin can be managed promptly to avoid compliance-related repercussions. The trucks also receive real-time data on the location of empty bins, which helps them chart optimal routes and be more fuel-conscious.

Ultimately, leveraging sophisticated data analytics helps build a leaner and greener waste management system. In addition, it can improve operational efficiency while taking an uncompromising stance on environmental and health sustainability.

Tiger Analytics’ waste management modeling use case for a large manufacturer

Overflowing bins are a severe impediment in the waste management process as they increase the time required to process the waste. Waste collection trucks will have to spend more time than budgeted for ensuring that they handle overflowing bins effectively – without any spillage in and around the premises. It is also difficult for them to complete their trips on time. When dealing with commercial bins, the situation is even more complicated. The size and contents of the commercial bins vary based on the unique waste disposal requirements of businesses.

Recently, Tiger Analytics worked with a leading waste management company to harness advanced data analytics to improve compliance concerning commercial waste management.

Previously, the client had to record videos of the waste pick up process and send them for manual review. The videos were used to identify the commercial establishments that did not follow the prescribed norms on how much waste could be stored in a bin. However, their video review process was inefficient and tedious.

When the pick up takes place, the manual reviewer is expected to watch hours of video clips and images captured by each truck to determine violators. Thus, there was an uncompromising need for accuracy since overflowing bins led to compliance violations and potential penalties.

Tiger Analytics developed a solution that leveraged video analytics to help determine whether a particular bin in an image was overflowing or not. Using cutting-edge deep learning algorithms, the solution enabled a high level of accuracy and eliminated all activities related to the manual video review and the associated costs.

Tiger Analytics’ solution was based on a new data classification algorithm that increased the efficiency of the waste collection trucks. Based on the sensor data collected from the chassis, we empowered the client to predict the collection time when the truck was five seconds away from being in the vicinity of a bin. Furthermore, with advanced monitoring analytics, we reduced the duration of the review process from 10 hours to 1.5 hours, which boosted workforce efficiency too.

As a result, the client could effortlessly de-risk their waste management approach and prevent overflow in commercial bins. Some of the business benefits of our solution were:

• More operational efficiency by streamlining how pickups are scheduled
• Smarter asset management through increased fuel efficiency and reduced vehicle running costs
• Improved workforce productivity – with accelerated critical processes like reviewing videos to confirm the pickup
• Quick risk mitigation of any overflow negligence that leads to compliance violations

Conclusion

New avenues of leveraging advanced analytics continue to pave the way for eco-conscious and sustainable business practices. Especially in a highly regulated sector like commercial waste management, it provides the much-needed accuracy, convenience, and speed to strengthen day-to-day operations and prevent compliance issues.

Day by day, commercial waste management is growing into a more significant catalyst for societal progress. As mentioned earlier, more companies are becoming mindful of their impact on the environment. In addition, the extent of infrastructure development has taken its toll – thereby exponentially increasing the need to optimize waste disposal and collection methods. Either way, data provides a valuable understanding of how it should be done. 

This article was first published in Analytics India Magazine.

The post Waste No More: Making a Difference with Tiger Analytics’ Data-Driven Solution for a Greener Tomorrow appeared first on Tiger Analytics.