How zinzu helps businesses

How zinzu helps businesses.pdf

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zinzu’s Vision: Uncovering Patterns in Sequential Data

Zinzu helps you connect the dots in your data, like piecing together a story. It takes all your scattered data points, like different events or actions over time and puts them in order, so you can see patterns and understand the 'why' behind them.

At zinzu, we view every piece of data as an event on a timeline, regardless of how it was created or where it’s stored, and believe in making raw data accessible without the need for complex queries, empowering businesses to gain deeper insights with ease.

Events are correlated by entities; such as users, sessions, customers, products, and more. Each entity’s data forms a unique sequence on a timeline. In businesses with hundreds of millions or even billions of entities, this generates as many sequences.

To uncover deeper insights, it's essential to re-order data in the sequence it was generated. This approach enables a clearer understanding of context and the discovery of underlying behavioral patterns.

Once sequenced, these events function like characters in a text, where patterns can be identified in much the same way you would search for substrings using regular expressions or substring searches.

Check out these two short videos to learn more about Zinzu and its sequencing:

• https://youtu.be/kR16kmyrw8I

• https://youtu.be/_3g8LEQSCOg

No Need to Move Your Data:

zinzu connects to various cloud-based datasets (AWS, GCP, Azure) through its configuration, eliminating the need for users to move their data into Zinzu’s data store. During processing, Zinzu standardizes these datasets into a common schema, treating each record as an event. As a no-code platform, Zinzu allows users to create queries through a simple drag-and-drop interface. Zinzu writes computed results back into customers' accounts in the cloud.

Expressing queries in Zinzu:

At zinzu, we believe users should be empowered to articulate their data needs in a straightforward, linear manner, just as they naturally think, without worrying about underlying data structures. This approach offers a much simpler, no-code drag-and-drop querying interface. SQL's widespread adoption has ingrained a tabular and relational mindset for data retrieval, influencing the design of many big data technologies. Zinzu challenges this philosophy, offering a more intuitive and flexible alternative.

No Vendor Lock-In: Use When Needed and Pay for What You Use:

zinzu operates across all three major cloud platforms, allowing customers to utilize it as needed and only pay for the duration of each query run. At Zinzu, we’re not aiming to replace existing systems but rather to complement them by addressing gaps in current analytics models.

AI integration in Zinzu query engine:

zinzu seamlessly integrates with Gen AI, allowing users to express query filters in natural language, making the process more intuitive and accessible.

Sample Use Cases (Not Limited To):

At zinzu, we believe that every vertical or business can benefit from sequential and pattern analysis. Sequential analysis is a common use case across industries, providing valuable insights that drive better decision-making.

Here are some key use cases:

• Tracking Campaign Performance: Analyze the sequence of customer interactions to optimize marketing efforts.

  
  

• Monitoring Customer Journeys: Understand user behavior on websites or apps by tracking their navigation paths.

  
  

• Observability of Complex Services: Identify sequences of log records for sessions with prolonged response times, enabling faster issue resolution.

  
  

• Error Path Detection: Discover the app usage paths that lead to errors in complex systems.

  
  

• Supply Chain Optimization: Identify and resolve bottlenecks by analyzing patterns in the supply chain.

  
  

• Patient Treatment Patterns: Uncover trends in patient care to improve treatment outcomes.

  
  

• Fraud Detection: Track transactional sequences to identify and prevent fraudulent activities.

  
  

• Manufacturing Process Improvement: Analyze production line sequences to detect inefficiencies and reduce downtime.

  
  

• Financial Market Analysis: Discover trading patterns and trends for better investment strategies.

Author: Arif Khan

Founder | Zinzu.io

Contact: team@zinzu.io

LinkedIn: Arif Khan

Passionate about solving complex problems in data using innovative solutions.

Data Wrangling

Data Wrangling.pdf

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Data: Wrangling Challenges, Opportunities, and a Herd of Cats

Businesses today use various data processes; like ETL pipelines, observability tools, and marketing platforms, to ensure smooth operations and gain insights. But are these processes truly effective?

Dashboards are commonly used to monitor performance, but they often provide only partial information when detailed insights are needed. Most processes confirm that everything is functioning as expected, but when systems break or new opportunities arise, it’s usually for NEW reasons – leaving us with stale insights and unanswered questions. This results in time-consuming requests to engineering teams, slowing decision-making and disrupting planned deliverables.

What Do We Need?

We need access to raw, well-organized data that's easily queryable. This enables us to explore and uncover the root causes of new issues and opportunities.

With data lakes and elastic cloud computing, accessing raw data isn’t a problem. The real challenge isn’t size or compute cost, data can be filtered, and costs managed with spot instances and minimal storage fees. However, several challenges remain:

• Disparate Systems: Data spread across different systems complicates access and analysis.

  
  

• Inconsistent Schemas and Serializations: Varying schemas and serialization formats add complexity.

  
  

• Complex Queries: Analyzing raw data often requires time-consuming and intricate queries.

  
  

• Technical Expertise: Effective querying demands specialized skills.

The most challenging aspect of opening up raw data is writing complex queries on disparate datasets with varying schemas. If the querying process can be simplified through no-code tools or natural language interfaces, the vast potential of raw data can be unlocked for a broader range of users.

Democratizing access to raw data is key to unlocking its full potential.

Is Latency an Issue?

Latency isn’t usually a concern in exploratory analysis. The real issue is the time spent searching for data in index-based systems through complex queries, often yielding incomplete results. These systems are also costly if we store all datasets and inefficient for occasional use.

Is Vendor Lock-In the Right Solution?

Businesses often rely on vendors for observability or marketing funnels, but this can limit flexibility. For example, extracting detailed insights, like sequenced log records for long sessions, can be challenging due to vendor data aggregation. To maintain control, consider storing raw data in the cloud with on-demand querying for deeper analysis.

The Constraints of SQL-Style Joins and Tabular Visualization

RDBMS systems and SQL enforce a relational, fixed-schema approach that limits flexibility. This approach is particularly unsuitable when trying to visualize data in the sequence of its generation, especially with varying schemas. As data structures evolve and become more complex, traditional tabular formats fail to effectively represent such sequences, highlighting the need for more dynamic and flexible visualization methods.

SQL's widespread influence has ingrained a row-and-column mindset in how we query data, often overshadowing more natural, linear approaches that could better represent the true sequence of events.

What’s the Way Forward?

Anticipating all possible issues in advance and preprocessing data isn’t feasible. In addition to pre-processing, it's more effective to offer an easy-to-use query mechanism that allows users to access pre-aggregated data. This way, users can leverage elastic compute to extract insights as needed.

Have you faced similar challenges? How have you overcome them?

Author: Arif Khan

Founder | Zinzu.io

Contact: team@zinzu.io

LinkedIn: Arif Khan

Passionate about solving complex problems in data using innovative solutions.