Global industries have experienced a tremendous impact from DataOps. DataOps tools have streamlined data management and analytics processes, improved underwriting, and enhanced customer experience, resulting in numerous benefits. But how do DataOps tools achieve this? The numbers hold the answer.
Dresner Advisory Services‘ survey indicates that companies that adopt DataOps can reduce the time required to deliver analytics solutions by up to 50%. This means businesses can obtain insights faster, enabling them to make more informed decisions that, in turn, can increase their analytics teams’ efficiency by 25%. DataOps frees analysts to focus on higher-value activities by automating many of the repetitive tasks involved in data management.
Insurance executives believe that DataOps can help improve data quality, according to a survey by Deloitte. The success of DataOps is not an industry secret, with numerous benefits arising from its use. There is a common misconception that DataOps tools are far too expensive for SMBs, but in today’s article, we will deeply dive into how DataOps tools are priced and why they are within the budget of every company looking to scale their data management.
Decoding the Pricing Structures of DataOps Tools
There is no single price range for most commercial DataOps tools. The cost of implementing a solution depends on several factors, such as the complexity of the data sources, the volume of processed data, and the project’s specific requirements.
Let’s look at a few factors that influence the cost of DataOps implementation.
Also Read- How Businesses Manage Data Intelligently With Cloud Data Warehouse
Licensing costs
The principal reason why DataOps is often associated with being expensive is that many commercial DataOps tools have high licensing fees.
Licensing fees are based on the number of users, the number of processors, or the level of functionality required. Very few DataOps solutions have a catalog price. They offer a quote after an initial consultation, which varies widely depending on the tool and vendor.
There are also open-source and low-cost DataOps tools available, which can be more cost-effective in the long run. However, the lack of licensing fees is offset by the cost of hiring a team of developers, which leads to higher costs and manual data implementation.
Customization
DataOps tools often require thorough customization to meet an organization’s specific needs.
Developers need to create custom scripts and set up integrations with existing systems. Configuring a DataOps tool to meet the project’s unique requirements is challenging.
DataOps solutions often require integration with other systems, such as databases, data warehouses, and reporting tools. This level of customization can be time-consuming and expensive. The larger or more complex the projects, the greater the cost associated with customizing.
Complexity
Implementing a DataOps tool can be complex and lengthy, particularly for organizations with numerous data sources.
It is often time-consuming to extract data from multiple systems and transform it to meet business needs; the task often does not end there. The final step is loading it into a target system. DataOps installation and management often prove challenging and require specialized knowledge and skills.
This involves updating code, troubleshooting errors, and performing upgrades. All of these can be time-consuming and need a dedicated team of developers.
Why DataOps Tools Are Deemed to be Expensive
A study by IBM found that data quality issues, such as incomplete or inaccurate data, can result in a loss of up to 20% of revenue for insurance companies. This translates to billions of dollars lost over improper data management and inefficient automation. To address this problem, companies are compelled to hire additional developers, which adversely affects their profit margins and operational efficiency. Furthermore, over-reliance on developers and data engineers results in business owners losing data ownership, as they cannot rely on the timely and accurate data provided by automated DataOps tools. However, these automated tools are often considered too expensive. So, how can the industry balance budget and effectiveness?
Automated data management companies offer pricing structures that provide the answer. Most tools charge an upfront license fee and bill insurance companies for features they may not require. This often includes charging companies for a large number of sources or transformations that they may never need.
Such a pricing structure increases the budget insurance companies require to shift to automated DataOps tools and often acts as a deterrent. As a result, many companies opt for low-cost manual DataOps tools that are clunky and complex to use, which ultimately affects their revenue. The best solution for insurance companies is to choose an automated DataOps tool with entry-level pricing and charges based on the number of features required by the company.
Also Read- Generative AI For Insurance: Use Cases And Applications
FLIP – Transform Your Data With Ease and Affordability
FLIP is a zero-code DataOps tool designed to meet the unique needs of global industries. It helps companies manage large and complex datasets effectively while retaining important data trends to improve customer satisfaction and operational efficiency. Kanerika’s FLIP offers a cost-effective pricing structure with highly customized templates built for sectors such as insurance, healthcare, and retail.
Here are some distinct advantages of using FLIP:
- FLIP is up to 60% more cost-effective than similar DataOps tools. FLIP achieves this by providing flexible pricing plans that charge the user based on the number of features they opt for. Clients pay for what they use – nothing more!
- FLIP can save companies up to 75% of their overall DataOps costs by completely automating their data management needs and reducing dependency on third-party developers and data engineers.
- FLIP is a zero-code solution and enables business owners to own their data. With highly customized templates tailored for the insurance industry, FLIP is operational without any lengthy implementation process.
Here is a case study showcasing the impact of FLIP.
Sign up for FLIP today and start harnessing the power of data like never before!
Frequently Asked Questions
What is a DataOps platform?
A DataOps platform is like a "one-stop shop" for managing your data journey, from collection to analysis and everything in between. It combines tools and processes to automate data workflows, improve collaboration, and ensure the quality and reliability of your data. Think of it as the foundation for building a strong and efficient data-driven organization.
What is DataOps vs DevOps?
DataOps and DevOps share a focus on automation and collaboration, but target different areas. DevOps streamlines software development and delivery, while DataOps focuses specifically on data pipelines and analytics. Think of DevOps as building the car and DataOps as building the engine that powers it, both critical for a smooth ride.
What is the difference between DataOps and MLOps?
DataOps focuses on streamlining the entire data lifecycle, from ingestion to analysis, emphasizing automation and collaboration to improve data quality and accessibility. MLOps, on the other hand, specializes in the deployment, monitoring, and management of machine learning models, ensuring consistent performance and reliability in production. While both aim for operational efficiency, DataOps focuses on data itself, while MLOps centers around AI models.
What is the difference between DataOps and data pipeline?
DataOps is a broader approach to managing the entire data lifecycle, from ingestion to analysis. It emphasizes automation, collaboration, and continuous improvement. A data pipeline, on the other hand, is a specific sequence of steps used to move and transform data. DataOps provides the framework and tools for building and managing data pipelines, ensuring they operate efficiently and effectively.
What are the three pipelines of DataOps?
DataOps pipelines are like assembly lines for your data, ensuring smooth flow from raw data to insightful outputs. The three main pipelines are: Data Ingestion, where raw data is collected and prepared for processing; Data Transformation, where data is cleaned, structured, and enriched; and Data Delivery, where insights are presented to consumers in a timely and accessible manner.
What is the role of DataOps?
DataOps is like a bridge between data science and IT operations. It helps organizations streamline the entire data lifecycle, from data collection to analysis and deployment, by focusing on automation, collaboration, and continuous improvement. This ensures that data is readily available, accurate, and reliable for making informed decisions and driving business value.
How to implement DataOps?
DataOps is all about bringing Agile principles to your data pipelines. Think of it like building a factory for your data, but instead of cars, you're making insights. It involves automating processes, integrating tools, and establishing collaboration between data engineers, scientists, and business users. The goal is to get data into the right hands, in the right format, at the right time for faster, more reliable decision-making.
How is DataOps different from DevOps?
While both DataOps and DevOps aim for efficiency, DataOps specifically focuses on data-related processes. It emphasizes the seamless flow of data from collection to analysis, incorporating practices like automation, collaboration, and data quality monitoring. DevOps, on the other hand, focuses on streamlining software development and deployment, with a primary focus on code and applications.
What is DataOps vs MLOps?
DataOps focuses on optimizing the entire data lifecycle, from ingestion to analysis, to deliver reliable, high-quality data for decision-making. MLOps, on the other hand, focuses on streamlining the development and deployment of machine learning models, ensuring they are reliable, scalable, and maintainable. While both are about automating processes and improving efficiency, DataOps focuses on data quality and reliability, while MLOps prioritizes model performance and deployment.
