How can you prove your data engineering impact to your organization?

Measure and quantify the outcomes of data engineering initiatives using KPIs. These metrics can encompass data processing speed, data quality enhancements, or cost savings. Demonstrate how the data infrastructure and pipelines you've established directly contribute to data-driven decision-making within the organization. Showcase how your data engineering initiatives have elevated data quality, resulting in accurate reporting, reduced errors, and better-informed decision-making across the organization. Emphasize how your data engineering solutions have accelerated the transformation of raw data into actionable insights. This acceleration enables the organization to respond promptly to market changes and capitalize on emerging opportunities.

An example I've seen in my current job is the transparancy over the architecure. In other words, envolving business owners in both roadmap definition, continuous delivery and goal definition. In the KPI and measurement spectrum, we understood that the priority on business impact comes from the feedbacks of business users. Usually the metric should envolve both data dissemination and development of an impactfull data ecossystem. We did it by defining which Analytics Cubes were more important and which business metrics were more impactfull, with the help of each business leader. Those were the guidelenes for goal setting that really latched itself towards the business. Without getting lost in technical definitions.

The first step to prove your impact in data engineering is to define your goals and metrics, align them with the organization's objectives, and communicate them effectively to stakeholders, managers, and peers. This ensures that our work is understood and appreciated in the context of improving data quality or optimizing data pipelines

Defining goals (more abstract) and metrics (more concrete) ensures that you and your stakeholders are aligned. If you do a great job on something that nobody cares about, then you are wasting your time. Hence the need for goals to make sure you are working to wards something useful. Similarly, it's difficult to determine consistently and with method what a 'good job' looks like, hence the need for metrics to make sure there is a method to determine success. Crucially, metrics also help identify areas where improvement is needed, which can otherwise be surprisingly difficult to pinpoint.

Start by defining clear KPIs that align with organizational goals and establish baseline metrics. Continuously monitor and track progress using real-time dashboards, and document success stories showcasing the benefits of your work, such as improved decision-making and cost savings. Conduct cost-benefit analyses to quantify financial impact and ROI. Additionally, gather feedback from data users to highlight the practical advantages of your data engineering solutions. By presenting a combination of quantitative metrics and qualitative evidence, you can effectively demonstrate the value you bring to the organization, making it easier to secure support and resources for future projects.

Before we as data engineers prove the impact of our work to the organizations, it's important to quantify our work, that can help to demonstrate our accomplishments to the stakeholders. Inorder to achieve this, documenting the quality of work along with the performance impact, cost effectiveness and data quality that has given productive outcomes can really help. It's very important to focus on the impact of the work done, by sharing right volume of data with a smashing story that represents your progress productively.

Whether it's in the form of reports, dashboards, or updates, documentation will serve as evidence of your work's effectiveness. Over time, this can help paint a picture of your contributions, making it easier for stakeholders to understand and appreciate your role and efforts

"Practice what you preach"! It's essential to implement a robust measurement framework that not only tracks technical accomplishments but also gauges business outcomes. Begin by identifying leading KPIs like improvements in data quality, reduction in time-to-serve, and other operational metrics. These KPIs will serve as early indicators of your initiative's success and direction. However, leading KPIs alone won't paint the full picture. Bridge these to lagging KPIs, which are the ultimate measures of business impact—be it enhanced customer satisfaction, elevated profitability, or improved business performance. Creating this linkage between leading and lagging KPIs enables you to articulate a compelling narrative.

Collecting and analyzing data isn't just some boring chore; it's your chance to spotlight your impact. Think of it like documenting your journey from newbie to ninja. It's proof of your hustle, your grind, your relentless pursuit of excellence! But hold on, don't just throw numbers around like confetti. Be thoughtful. Understand the narrative behind the data. How do your improvements in data quality translate into better insights? How do your pipeline optimizations speed up decision-making? It's not just about the WHAT but also the WHY.

We can achieve good understanding and showcase the impact with clear and good storytelling in our dashboards, be also patient with non data people and try to explain as much as possible (how you measure, what are the benefits, what’s the challenges achieving improvements etc)

When you've got something to celebrate, don't hold back! Be the loudest advocate for your data engineering wins. Your passion is infectious, and it'll rally your team and stakeholders like nothing else.

When sharing success there are a few things I'd suggest: 1. Celebrate success in a public setting by voicing it, emails are nice but do not carry tone. 2. Make sure to include all participants including stakeholders, it take a team to accomplish major initiatives. 3. For small individual accomplishments, try to make success tangible - whether by spot bonus, small token of appreciation or even a silly rotating plush toy for "top contributor of the month". 4. Keep awards & celebrations sensible - completing tasks is why people get hired for. Some things to take into account - considerable challenge (timeline/complexity), was this assigned work or self initiative & the impact of this accomplishment. 5. Be fair - be consistent and transparent.

As a woman, celebrating small successes and wins can be all the encouragement for that young lady who has doubts if she can do it needs to continue to push forward. No milestone is too small to celebrate, share your processes including tools, codes, and notebooks it goes a long way to motivate others.

Yes, it’s vital to effectively communicate our data engineering achievements and insights. Using technical blogs and demos, we can share innovative solutions and detailed knowledge we’ve acquired, highlighting the advancements in our data methodologies. Clarifying our technical contributions and acknowledging team efforts is crucial, as it promotes a harmonious and enriching environment, aligning technical development with organizational objectives.

Easy-Peasy.ai. It's another AI chatbot, similar to ChatGPT. It's designed to be a conversational AI that can answer questions, provide information, and chat with users in a natural and engaging way. One of the things that sets Easy-Peasy.ai apart from other chatbots is its focus on providing accurate, up-to-date information. It uses a combination of machine learning and natural language processing to understand and respond to user queries, and it's constantly learning and improving.

Don't just wait for feedback to come to you; seek it out. But remember, feedback is a two-way street. Don't just collect it; embrace it. Be open to diverse perspectives. It's not about defending your work; it's about growing together. Now, here's the real kicker. It's not enough to collect feedback; you've got to act on it! Show, don't just tell. Demonstrate how you've taken those valuable suggestions and made tangible improvements in your data engineering processes, methods, or standards.

If you can spare the time, interviews are one of the best ways to collect feedback. After that are the ways to just instantly just free text feedback. Also, through interviews, it's much more efficient to make an effort to understand the workflow of the stakeholders than to just follow a questionnaire. Lastly, collecting feedback without acting on it is a waste of time. If there are results from the feedback which are not sensible to follow-up on, this should be communicated back transparently. To the stakeholders but also to the management.

Proactively canvassing for feedback is more than a mere tick in the box; it's a conduit for growth. Engage with stakeholders, invite critiques, and truly tune into the resonances from every corner of the organization. The canvas of feedback is vast, ranging from meticulous surveys to candid chats. But collecting insights is just half the journey. The crux lies in the art of weaving these insights into your data engineering tapestry, showing a clear trajectory from suggestion to tangible action, thus underscoring your commitment to collective progress.

Feedback is your compass to navigate the data engineering journey. Actively seeking input from stakeholders and peers, whether through surveys or casual chats, is like tuning your instrument for a symphony. What makes it truly impactful is not just gathering feedback but turning it into action, showing how you're shaping your data engineering processes based on these insights. It's the key to continuous improvement and building trust in your data-driven endeavors.

Collecting feedback and suggestions be a part of the data engineering process from start to finish. Collecting feedback directly from stakeholders and end users are often the most impactful for future initiatives. One of the hardest parts of collecting feedback is acting upon criticisms and doubling down on positives. Being able to take critical feedback and improve a process or product is one of the biggest differences between successful and mediocre data professionals. Additionally, some professionals often become complacent after receiving positive feedback - the goal is to not stop improving once things work, but to extend what works to other initiatives.

Failure is the best teacher. If you're not failing, you're not trying hard enough. In data engineering, failure is not an option. It's a guarantee. But that's not a bad thing. Failure is how we learn and grow. It's how we become better data engineers. So when you fail, don't beat yourself up. Embrace it. Learn from it. And come back stronger.

Every assignment in Data Engineering comes with a set of new learnings, hence Data Engineering is all out learning from the Past, use in the Present and Improve in the Future.

Looking at every assignment as an opportunity to explore and deliver an end to end data product can help you have fun while learning from challenges. Keeping an open mindset to new ideas and being flexible to trying out different approaches gets you an invaluable experience as a data Engineer.

Data engineering is a journey through a maze of challenges, but think of them as thrilling puzzles rather than roadblocks. When things go sideways, dive into the adventure of post-mortems and root cause analyses, like a detective cracking a case. And when you conquer those challenges, it's like leveling up in a video game, unlocking new skills through courses and books. The best part? You get to share these epic quests and newfound powers with your data engineering comrades, making the journey all the more rewarding and exciting.

In the intricate world of data engineering, every setback is merely a stepping stone, a catalyst that propels us towards mastery. The challenges and pitfalls we face aren't barriers; they're lessons waiting to be deciphered. Through practices like retrospectives and deep dives into root causes, we unravel these lessons. And as we absorb new skills, be it through courses or literature, we fortify our arsenal. So, as we navigate the terrain of data engineering, every stumble becomes a testament to our resilience, every learning a badge of our evolution. Wear those badges with pride and let them be the evidence of a journey marked by growth and determination.

Having a network outside of the organization often goes ignored. Do not underestimate having a network of data professionals in your space. Besides making new friends, data professionals help each other to keep up with trends and news in the data domain. It's a great way to look out for each other. Giving back to the community helps you build credibility in your industry. While it may not be formal, leaders care when teammates upskill and engage with their craft after work. Word travels fast, and positive mentions will reach your peers eventually. Hence, attend events, write blogs, and contribute in meaningful ways. Adding value to the community will pave the way for personal and professional growth.

A really great way to get involved is through open source projects. When you leave a company, you unfortunately leave all the work you’ve accumulated for years. Contributing to open source is a great way to network with likeminded individuals, showcase your abilities in a public domain, and keep a public record of these skill sets for employers. Setting up sample data flows is interesting, but showcasing work in open source will make you incredibly marketable. These communities also can act as a safety net by connecting you with a lot of companies using the technology you’re contributing to.

The data engineering community is growing rapidly, and it seems that these communities aren't taking that for granted. Free resources, wonderful Q&A's, and incredibly helpful individuals can be the best assets as someone develops and grows their engineering career. Engaging with other engineers on platforms such as LinkedIn allows both parties to learn and build relationships outside of their organization. This provides perspectives that would otherwise be impossible to build without a thriving community.

In every profession, individuals leverage their skills to make an impact and deliver value. However, "value" is a nuanced term, influenced by factors like team size, budget, financial health, and the specific challenge faced. For an engineer at a startup, building a pipeline with open-source tools offers greater value due to budgetary constraints, unlike splurging on expensive cloud solutions. Conversely, at a billion-user enterprise, the priority is crafting a resilient, scalable system, even if it's pricier. Key metrics to gauge impact include: - User feedback from data consumers. - Innovation, evident in advanced analytics and real-time decision-making capabilities. - Data Quality - Cost Savings - Turn Around Time etc - Time Saved etc

Data Engineering is a vast field if you want to excel in this field you have to be proficient in understanding Data not only writing hundreds of lines of code understanding the data gives you an edge over others not just process data by a predefined logic but how you can process, clean data in a efficient way and help your organisation like Data doctor ????.

I've found it very valuable to cultivate a strong relationship with product owners and if necessary, take the time to educate them on data engineering in general. Often to POs, the data part of the product they are defining is seen not as schema but as fields. Everything below basic validation and data typing can seem like a big black box. Concepts like cardinality, domain crosswalks, and data entropy over time are usually not well understood all the way out to the requirements level. Having your PO understand that the database is much more than a storage engine can be helpful. Investing real time in explaining how the back end works with those writing the specs can help a lot and change the way they look at defining the product.

One of the most important aspects of Data Engineering is communication. Nobody will use the data on your platform or the dashboards they drive without having familiarity. One of the more modern methods of communication is done through data discovery platforms like DataHub or Amundsen that provides a user friendly interface to your platform that enables smart searching to find the datasets in your company that provide value, which team owns that dataset if there are any issues, and how the datasets relate to eachother. This fills in the mental model and centralizes how users will actually find and interact with the data you work so hard to curate. It provides an almost Google-like search over data. This boosts your value to the company.

I believe there are 2 undervalued skilled in the data engineering field: communication, and creativity. Communication is necessary when working within your organization and with stakeholders. Gathering requirements, organizing tasks, and meeting with your team are all important parts of the development lifecycle for a data engineer. Creativity is necessary for solving the majority of problems. Sure - some problems might be similar to one you might find in a textbook, but the real world has many variables and are often influenced by humans. This volatile mix of demands, expectations, and non-standard data means that engineers will need to solve problems that have never been solved before.