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7 Alternatives to Coherent Spark - Which Is Best?

A visualisation showing digital transformation taking place in the insurance industry

In the fast-paced world of business, having the right tools to streamline operations, enhance decision-making, and reduce time to market is paramount. 

Coherent Spark offers seamless conversion of simple spreadsheet logic into executable code, a feature particularly beneficial for businesses looking to automate simple spreadsheet use cases without extensive redevelopment. However, as companies grow and their requirements diversify, exploring alternatives that provide similar or superior functionality aligning with their emerging needs becomes essential.

This article delves into the features, benefits, use cases, and limitations of alternatives to Coherent Spark, equipping you with the insights needed to make informed choices about the tools that will drive your strategic decisions forward.

We will explore the following Coherent Spark competitors:

1. Schematiq

Best All-rounder for Analysis, Functionality & Integration

Schematiq logo

Schematiq redefines data analysis and spreadsheet optimisation and has succeeded in many different sectors with its cutting-edge solution for modern enterprises. It provides a platform of capability to solve a variety of Excel use cases, helping businesses transition from traditional spreadsheets to agile, strategic decision-driving tools as part of a digital transformation.

Its approach enhances data operations and analysis and integrates seamlessly with existing business workflows and applications. Schematiq is an essential tool for businesses aiming to leverage their data and proven spreadsheet-based IP for a competitive edge.

Main Features of Schematiq:

  • Excel Logic Conversion: Transforms complex Excel logic into scalable APIs, significantly enhancing performance without sacrificing the familiarity and ease of use associated with Excel.

  • Supercharged Spreadsheets: By optimising and scaling spreadsheets, Schematiq ensures operations are up to thousands of times faster than standard Excel.

  • Advanced Data Integration and ETL Operations with Excel: With proprietary datalinks, Schematiq allows direct access to data at its source, e.g. SQL, Oracle, and data platforms such as Snowflake, in addition to API-based sources. This eliminates the constraints of row/column limits and computing power typically associated with Excel and provides a data viewer/debugger to allow users to trace through data operations that are being performed.

  • Version Control and Auditability: Provides robust tools for version control, testing, analysis, and usage tracking, ensuring that spreadsheets are efficient, secure and compliant with regulatory standards.

  • Custom Deployment: Offers flexibility in deployment, either on-premises or in a private cloud, addressing concerns around data residency and access while allowing businesses to control update schedules.

Benefits of Using Schematiq:

  • Enhanced Performance and Availability: Schematiq's Workbench simplifies complex spreadsheets for better performance, accuracy and management.

  • Scalability and Operational Risk Reduction: Tailored for businesses that model large volumes of data and complex logic in spreadsheets.

  • Intuitive Platform: An Integrated Development Environment (IDE) for Excel, Schematiq empowers users to remediate and reduce operational risks associated with Excel-related challenges.

  • Fewer Excel EUCs: by transforming spreadsheets into APIs, users can interact with proven Excel logic directly from strategic systems such as CRM, Finance or browser-based low-code apps, meaning a significant reduction in spreadsheet EUCs / ‘Shadow IT’ and no re-keying between systems.

Limitations of Schematiq:

  • Learning Curve: Transitioning from traditional Excel to Schematiq’s advanced functionalities may require some training. However, it is a shallow learning curve—a user proficient in Excel will already have 80% of the knowledge required to be proficient in Schematiq.

  • Cost: Advanced features and custom deployment options might come at a higher price point than simpler tools.

Schematiq Use Cases:

  • Accelerating Digital Transformation: Transforms complex Excel spreadsheets into scalable, cloud-based APIs, crucial for rapidly digitising business processes and reducing the number of spreadsheets.

  • Migrating Legacy Systems: Facilitates the smooth transition of outdated systems to modern platforms, preserving and enhancing business logic – ideal for organisations looking to update their tech without losing data integrity or proven IP.

  • Reducing Operational Risk: Minimises manual data processes, spreadsheet errors, and operational risks – especially valuable in highly regulated industries where auditability is key.

  • Improving Efficiency and Reducing Costs: Schematiq's ability to supercharge spreadsheets and manage large, complex models more efficiently translates into significant cost savings and operational efficiencies across the board.

Schematiq vs. Coherent Spark:

Schematiq extends beyond spreadsheet-to-API conversions, offering a workbench for spreadsheet simplification and proprietary datalinks for direct data access, which Coherent Spark does not provide. Additionally, Schematiq’s flexibility in deployment offers various advantages over Coherent Spark’s principally SaaS model.

2. Convert to Code (e.g., Python) 

Best for Businesses Looking To Use In-House Programming

Python logo

Converting spreadsheet logic to programming languages like Python offers flexibility and power for data analysis and application integration. This approach involves translating the formulas and functions of Excel spreadsheets into a programming language that is then integrated into broader software applications.

Key Aspects of Convert to Code:

  • Flexibility and Power: Python offers a high degree of flexibility and is powerful for data analysis, capable of handling complex calculations and large datasets.

  • Integration Capabilities: Code-based solutions can be easily integrated with other software applications, providing a seamless flow of data across systems.

Limitations of Convert to Code:

  • Technical Expertise Required: Demands significant programming knowledge, making it less accessible to non-technical business users.

  • Time and Resource Intensive: Developing and maintaining custom code is time-consuming and expensive, requiring the availability of often limited IT resources.

  • Potential for Miscommunication: This issue arises when translating business requirements into fixed IT code, potentially leading to errors or misalignments with ever-changing business objectives.

  • Locked business requirements: for companies operating in markets with rapidly evolving business requirements, locking these in code creates an inflexible solution with a protracted time-to-market.

  • Lack of Transparency: For non-technical stakeholders, code-based solutions can become "black boxes," making it difficult to understand, audit, and debug the underlying logic.

Convert to Code vs. Coherent Spark: 

This approach provides a more flexible and powerful alternative for data analysis and integration but requires more technical expertise compared to Coherent Spark’s more accessible platform.

3. Basic Excel-to-API Converters

Best for Simple Automation & Integration

These tools transform Excel spreadsheets into APIs for easier data integration, enabling programmatic access to the spreadsheet logic.

Key Aspects of Basic Excel-to-API Converters:

  • Automation and Integration: Facilitates data-process automation and integration with other software systems, enhancing operational efficiency.

  • Simplicity: Offers a straightforward approach to converting simple Excel logic into more adaptable formats for web applications and services.

Limitations of Basic Excel-to-API Converters:

  • Limited Functionality: Unable to support VBA macros, advanced formulas, and data connectors that are integral to spreadsheet's functionality.

  • Scalability Issues: May not effectively manage large or complex models, leading to performance bottlenecks and scalability challenges.

  • Operational Risks: Without comprehensive version control, testing, and audit trails, there's an increased risk of errors and compliance issues.

  • Data Handling Limitations: Basic converters typically require data to be within the Excel sheet, limiting the ability to access and manipulate large datasets efficiently.

Basic Converters vs. Coherent Spark:

While offering simplicity, basic converters lack the robust features and scalability that Coherent Spark provides, making them suitable for simpler tasks only.

4. Microsoft Graph API

Best for Businesses Deeply Integrated in the Microsoft Ecosystem

Microsoft graph logo

Microsoft Graph API facilitates integration across Microsoft 365 services, including Excel Online, enhancing productivity and collaboration. It allows developers to access, manipulate, and integrate Excel workbook data with other Microsoft services, facilitating a wide range of automation and data management tasks.

Key Aspects of Microsoft Graph API:

  • Integration with Microsoft Ecosystem: Offers seamless integration across Microsoft 365 services, enhancing productivity and collaboration.

  • Programmatic Access: Enables developers to access Excel files stored in OneDrive, manipulate them programmatically, and integrate them with other applications.

Limitations of Microsoft Graph API:

  • Complexity for Non-Developers: Requires programming knowledge, making it less accessible for non-technical users or decision-makers.

  • Limited by Excel Online Capabilities: The functionality is restricted to what is available in Excel Online, which may not support all features of desktop Excel, including certain complex formulas and macros.

  • Scalability and Performance Issues: While suitable for integration tasks, handling large volumes of data or complex calculations might lead to performance issues, especially for businesses with extensive data analysis needs.

  • Data Residency Concerns: Cloud-based services may pose compliance issues for enterprises with strict data residency and security requirements.

Microsoft Graph API vs. Coherent Spark: 

Microsoft Graph API offers deeper integration within the Microsoft ecosystem but may present a steeper learning curve and potential data residency issues not encountered with Coherent Spark.

5. Optalitix

Focused on the Insurance industry

Optalitix logo

Optalitix offers a suite of products designed to transform insurers’ Excel-based models into cloud-based systems accessible through APIs. This solution is particularly tailored for the insurance industry, providing tools for underwriting, pricing models, and data validation.

Key Aspects of Optalitix:

  • Industry-Specific Solutions: Optalitix specialises in insurance, offering products like Optalitix Models for converting pricing models, Optalitix Quote for underwriting, and Optalitix Validate for data analysis.

  • Cloud-Based Systems: Facilitates the transition of Excel-based models to the cloud, enhancing accessibility and collaboration.

Limitations of Optalitix:

  • Niche Focus: Primarily caters to the insurance sector, so it may not cater to the broader needs of businesses outside this industry e.g. finance, banking, energy.

  • Complexity and Integration: Transitioning to Optalitix’s cloud-based systems might require significant changes to existing workflows, potentially leading to integration challenges for businesses with diverse IT ecosystems.

  • Scalability Concerns: For organisations with highly complex or large-scale Excel models, Optalitix’s ability to handle these without compromising performance or functionality could be a concern.

Optalitix vs. Coherent Spark: 

Optalitix provides industry-specific solutions not offered by Coherent Spark, making it an attractive option for insurance companies but less so for businesses outside this sector.

6. Running Excel on Server (e.g., EASA)

Best for Collaborating (Without the Need for Enhanced Functionality)

EASA logo

Allows users to access and interact with Excel models via a web browser.

Running Excel on a server, for example, using EASA, lets users access and interact with centralised Excel models via a web browser. This approach aims to retain the power of Excel for simpler use cases while overcoming some of its collaboration and accessibility limitations, e.g., scaling a simple pricing model for a sales team.

Key Aspects of Running Excel on Server:

  • Web-Enabled Excel Models: Enables users to access Excel models from anywhere, facilitating collaboration without requiring local Excel installations.

  • Centralised Control: Provides a centralised platform for managing Excel models, enhancing security and version control.

Limitations of Running Excel on Server:

  • Complexity and Scalability: While effective for simpler models, running complex or large Excel files on a server may encounter performance issues, limiting scalability for businesses with extensive data analysis needs and/or VBA tasks.

  • Maintenance and Updates: Requires ongoing server upkeep to ensure compatibility with the latest Excel versions and security standards, potentially increasing IT overheads.

  • Integration Challenges: This option may not offer seamless integration with other business systems or the flexibility to easily adapt Excel models for web use without significant modifications.

  • Limited Functionality Enhancement: This does not significantly enhance Excel's capabilities – it primarily focuses on making Excel accessible online.

Excel on Server vs. Coherent Spark: 

This solution improves accessibility and collaboration but doesn’t offer the performance enhancements or advanced features of Coherent Spark.

7. Beacon

Best for Financial Systems Reliant on Python

beacon logo

Beacon lets users convert Excel models into Python, offering a stable platform for financial modelling and analysis. 

Key Aspects of Beacon:

  • Model Conversion to Python: Transforms Excel models into Python code to enhance performance and scalability.

  • Integrated Development Environment: Provides tools for developing, testing, and deploying financial models and applications within a unified platform.

Limitations of Beacon:

  • Technical Barrier: Requires programming skills, which may not be feasible for all business analysts and decision-makers.

  • Adaptation Time: Transitioning models to a new platform and language can disrupt workflows and be time-consuming.

  • Stability vs. Flexibility: While Beacon's conversion process aims for stability in financial models, it may limit the flexibility Excel offers, especially for ad-hoc changes and analyses.

  • Price / Full Product Adoption: Utilising Beacon effectively might require adopting its entire ecosystem, which could represent a significant change in how a company approaches its data analysis and financial modelling tasks in addition to being very expensive.

Beacon vs. Coherent Spark: 

Beacon leverages Python for scalability and performance, a significant shift from Excel-based operations that Coherent Spark facilitates. This makes it suited for businesses ready to invest in programming capabilities.

Making the Right Choice for Your Business

Choosing the right tool depends on your business’s needs, technical capabilities, and strategic goals. While Schematiq emerges as a strong all-rounder, the best choice varies based on your requirements for integration, industry-specific functionality, and your team's technical expertise.

For An All-Round Standout Solution, Make the Switch to Schematiq Today

Embrace the future of data analysis with Schematiq, the ultimate tool for managing large Excel datasets and complex Excel logic with ease and precision. Say goodbye to the limitations of traditional methods and hello to seamless scalability, integration, and efficiency. Make the smart choice for your business and switch to Schematiq today. Discover more about Schematiq here


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