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.
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.
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.
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.
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.
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.
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!
How do automated data management companies address the perception of DataOps tools being expensive?
How does adopting DataOps impact the time required to deliver analytics solutions?
What specialized knowledge and skills are required for DataOps installation and management?
Why is customization an important factor in the cost of DataOps implementation?
Are there cost-effective ways to manage the complexity of implementing DataOps tools?
What benefits do DataOps tools bring to global industries?
What percentage of time reduction can companies achieve with DataOps adoption?
Are there cost-effective alternatives to commercial DataOps tools?