Here's how you can showcase your worth when asking for a raise in Data Architecture.

Action: Regularly document your achievements in metadata management, master data management, data quality management, data security, and ILM. Track metrics such as improved data accuracy, reduced data retrieval time, and compliance adherence. Example: "Implemented a metadata management tool that reduced data retrieval time by 30%.

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Keep a spreadsheet of your initiatives and impacts during the year. I do this throughout the year to keep track of both the initiatives I worked on and the impact I made on the organization. This will make it easy to provide input to the mid-year and year-end review processes and save you time while producing higher quality success metrics.

Documenting your successes is essential for a raise. Keep a record of significant projects, like developing data models that increased efficiency, implementing cost-saving measures, or enhancing data governance. By systematically documenting these successes, you create a compelling narrative of your contributions and their impact, providing a strong foundation for negotiating a raise.

Taking control through the metrics that Power Bi offers helps prioritize projects and track ongoing projects. Quality control in data acquisition allows errors and opportunities for improvement to be identified in a timely manner.

In Data Architecture, ongoing education and skill improvement are crucial. Keep abreast of the latest industry standards and technologies such as SQL, NoSQL, ETL processes, and cloud services. Showcasing mastery of new tools or earning certifications demonstrates your commitment to staying current and contributing value to your team.

The automation of data capture, processing and the generation of automated clinical reports reduces the possibility of generating errors and reduces the time required to perform routine tasks in the operation.

Action: Continuously enhance your skills in core areas like data profiling, data cleansing, and data validation. Obtain certifications in relevant tools and technologies. Example: "Certified in Talend for data quality management and IBM InfoSphere for master data management."

Work on skills in that architecture that are necessary for your role and also in emerging areas. For example if you work in Hadoop, keep sharp in Spark and new versions of Delta, but also learn cloud technologies such as Snowflake, Iceberg, Snowpark, and AWS. This helps demonstrate your worth to your team but also new teams or employeers looking for hot skills.

Learning data architecture skill first and foremost think enthusiasts should know how the data layers participate and how the tool deeply helps to understand the data. Following point to cover the tool perspective 1. You should understand tool capability and limitation 2. Tool use case aspect 3. Integrate various tools to achieve various data archestration 4. Performance aspect of the tool

I have been leading the data design that there were a lot of challenges to improve the report performance, after migrate the data to the new data warehouse, it was possible get the reports faster. The implementation give some benefit for the business such as: - The marketing team can now adjust their strategies promptly based on the latest data, leading to a 15% increase in campaign effectiveness. -Timely adjustments and optimizations have reduced marketing spend by 10% while achieving the same or better results.

The architecture need to be flexible to accomodate business strategy so we can measure growth and value proposition based on the report build. Another example to see tangible result is to count man hours reduces when automate the report/insight.

Every effort counts. For example, if you fine-tuned a query that significantly reduces load time, you are directly contributing to the organization's vision. It's crucial to effectively communicate your contributions to relevant stakeholders so they understand the business value you've created. At some point, you will need to market the impact you've made towards achieving common objectives.

Action: Showcase how your data architecture work has positively impacted business operations. Highlight cost savings, improved decision-making, and enhanced data security. Example: "Optimized ETL processes, saving the company $50,000 annually in operational costs."

Understand the business objective of your organization. If you are not sure, ask your manager during regular check-ins. Then work with your teams to explain how the architecute you (and your team) is developing is impacting the goals and objectives of your organization with external stakeholders. The work you do is important, the way it's communicated to stakeholders around business impact is just, if not more important. This will help connect the value of your (and your team's work) to the value for the organization.

Action: Clearly articulate the value of your work to stakeholders. Use data to demonstrate how your contributions have led to business improvements. Example: "Improved data quality metrics led to a 15% increase in sales conversion rates."

Just as it's important to understand the business impact, it's more important for the architect to connect the value of the technical work to the business value or impact on the org. The data models and reports that are generated need to solve a business problem that would not happen without them. Understand the business challenges, and communicate how your solutions have helped use data and technology to create business for the organization. This will help connect the value of your (and your team's work) to the value for the organization.

Find ways to optimize the data architecture to save the organization on infrastructure, processing, and performance costs. For example, optimizing ETL processes can lead to significant reductions in operational expenses.

Action: Regularly seek feedback from peers and supervisors to identify areas for improvement. Use this feedback to continuously refine your skills. Example: "Received positive feedback from the data governance committee on the implementation of new data security protocols."

Work with stakeholders with whom you frequently engage for feedback. Every time you work on a , hold retros, and figure out how you can engage differently moving forward. Also, develop and solicit feedback through engagement surveys. Data Architects typically cross multiple teams, including downstream and upstream systems which have multiple friction points. Determine how you can make the process of obtaining and providing data more seamless in the org. This will help you build relationships across the org and be seen as a leader within the organization.

Action: Choose the right time to ask for a raise, ideally after a significant project completion or when your contributions have led to noticeable business benefits. Example: "Presented the case for a raise following the successful migration to a new data warehouse system that improved data processing speed by 40%."

Action: Stay updated with the latest industry trends and continuously align your skills with the evolving needs of the business. Participate in relevant training and development programs. Example: "Completed advanced training in cloud data solutions, keeping up with the industry shift towards cloud-based data management."

Start by quantifying specific achievements, such as successful project completions, improvements in data quality, or cost savings through optimised architectures. Provide concrete examples of how your technical expertise and leadership have directly contributed to achieving business goals and enhancing operational efficiency. Demonstrate your ability to solve complex data challenges, innovate with new technologies, and drive initiatives that align with strategic objectives. Additionally, emphasise any certifications, trainings, or professional development that have expanded your skills and added value to your role. Align your request with industry benchmarks and the value you bring relative to market standards.