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Assess gaps in your organization’s data capabilities and identify areas of improvement with our data maturity assessment services.
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Establish and enforce policies, procedures and controls for management and safeguard your data assets. With our data maturity assessment service, we help organizations develop robust frameworks that cater to their specific data needs. , build, launch, and test your product idea.
Maintain data quality for informed business decision-making. We focus on assessing and improving data quality management practices within organizations, starting with evaluating the data quality processes. Once evaluated, we move towards cleansing and validation techniques to identify areas for enhancement.
Evaluate data integration across the organization, assess gaps, and benchmark against best practices. Get recommendations on optimizing data flows, pipelines, and integration platforms. Streamline access to data across numerous applications and systems and facilitate real-time insights through improved integration.
Artificial Intelligence (AI) and Machine Learning (ML) are heavily influencing how organizations utilize and leverage data. VentureDive assists companies by testing their readiness and maturity in adopting AI and ML in their workflows and processes. We do this by evaluating the availability of the current data, the model development processes, and the algorithm selection.
Our Process
In the initial/ad hoc stage, practices for data management could be structured better since there is a general lack of understanding of the value of data as a strategic asset. Data is often siloed and inconsistent and needs proper governance. This means that the organization needs standardized processes and tools for data management.
In the reactive/fragmented stage, the organization begins to understand better data management requirements. While some existing good data management practices might exist in some departments, there may need to be an overall cohesive data strategy that governs all.
In the proactive stage, the organization establishes standardized data management practices and processes, such as data governance frameworks and policies, to ensure data compliance, security, and quality. The organization tries to integrate data sources and establish data standards while defining the role and responsibilities of data management. This helps in understanding the importance of data management well.
In the scoping phase, we meticulously define project objectives, outline features, and establish constraints, ensuring a clear project scope. Our estimation methodology employs industry best practices, providing accurate timelines.
The organization reaches the Manage/Integrated stage as data management practices become more mature and integrated. Data governance processes become well-defined, and there is a focus on interoperability, data integration, and creating a central data repository or a data warehouse. The organization has started to leverage analytics and business intelligence tools to gain insights from data.
In the initial/ad hoc stage, practices for data management could be structured better since there is a general lack of understanding of the value of data as a strategic asset. Data is often siloed and inconsistent and needs proper governance. This means that the organization needs standardized processes and tools for data management.
In the reactive/fragmented stage, the organization begins to understand better data management requirements. While some existing good data management practices might exist in some departments, there may need to be an overall cohesive data strategy that governs all.
In the proactive stage, the organization establishes standardized data management practices and processes, such as data governance frameworks and policies, to ensure data compliance, security, and quality. The organization tries to integrate data sources and establish data standards while defining the role and responsibilities of data management. This helps in understanding the importance of data management well.
In the scoping phase, we meticulously define project objectives, outline features, and establish constraints, ensuring a clear project scope. Our estimation methodology employs industry best practices, providing accurate timelines.
The organization reaches the Manage/Integrated stage as data management practices become more mature and integrated. Data governance processes become well-defined, and there is a focus on interoperability, data integration, and creating a central data repository or a data warehouse. The organization has started to leverage analytics and business intelligence tools to gain insights from data.
Technologies We Use
Our Work
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OUR WORKING MODEL
We are agile in our processes, and our departments are well-coordinated when it comes to working on any given project. Following the agile framework helps with incremental progress, continuous feedback, and flexibility to adjust the maturity assessment based on evolving needs.
Following mutually agreed-on security protocols, identification of risks & implementation of strategies to protect confidentiality, integrity, access control.
Data and tech are rapidly changing industries that require constant upskilling for the people involved. The engineers, developers, and data scientists at codevative are consistently working on updating their knowledge and expertise to match the industry standards.
After thorough analysis, we identify the gaps that require improvement and provide recommendations based on the organization’s data goals, existing data landscape, and flexibility in terms of data literacy. These recommendations are customized for every organization as per their assessments.
We are partnered with leading financial and cloud platforms, such as AWS, Azure, MifosX, NVIDIA, Oracle, and SAP. This helps us in digital transformation and maturity processes and gives us a competitive edge as well.
The recommendations outlined in the data maturity assessments, Codevative also offers support in change management and implementation of recommended improvements. This includes pieces of training and workshops and sessions for increasing data literacy wherever needed.
FREQUENTLY ASKED QUESTIONS
A data maturity assessment is a process of evaluating and analyzing an organization’s current state of data management practices, capabilities, and processes. It involves assessing various aspects of data governance, data quality, data integration, data security, data analytics, and other relevant areas to determine the organization’s level of maturity in managing and leveraging data effectively.
A data maturity model is a framework or methodology that defines different stages or levels of an organization’s data management capabilities. It provides a roadmap for progressing from lower maturity levels to higher ones. It helps organizations assess their current state of data management practices, identify areas for improvement, and define the steps needed to get to their target state.
The benefits of data governance maturity model include:
INSIGHT THAT MATTER
We’re a goal-oriented Consulting, Technology & Outsourcing Firm, helping global customers to accelerate business transformation with innovative, secure and scalable business solutions.
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