What are the best ways to build a successful data warehousing team?

I partially disagree with defining clear goals before hiring for data warehouse projects. Data warehouses, or data architectures in general, are inherently complex, with every department having a stake in data, influenced by the ever-evolving four V's of Big Data. Establishing a data warehouse is a long-term goal with milestones, feedback loops, and shifting priorities. Presenting a new team with a rigid 10-year plan can hinder team buy-in. Achieving a sustainable data warehouse solution is optimally facilitated by presenting the team the actual problems of the stakeholders, allowing the team the autonomy to create a vision how to approach these. Empowering a new team to shape their vision results in a data warehouse built on passion.

Data warehousing teams are like every other team I have put together or had foisted upon me. My top 3 keys to building any successful team in 750 characters or less are, 1. Build a team with the same goals and drives as the person leading it, not just identifying key roles and filling them up. 2. Invest in your team, including investing your time and energy in your team's success and not just your own. Your success is reflected in their success. 3. It is better to have a hole in your team, rather than an a$$hole, do not hire in an a$$hole. When there is a hole in the team, acknowledge it and reward your team for filling in the gaps. (Thanks to Mr Branson and Mr Pausch). These have nuances & are not an exhaustive treaty on the subject.

Data warehousing is a complex task, and it's important to have a team that can work together effectively. Build a diverse team as it will bring different perspectives, which can lead to better decision-making and more creative solutions. Invest in training which will help them to be more effective in their roles. If you don't have the in-house expertise, partner with experienced data warehousing consultants to build and manage a DW. Create a positive work environment where your team members feel valued and supported which will help them to be more productive and engaged. Focus on soft skills as well as hard skills. It's also important to hire people who have good communication, teamwork, and problem-solving skills.

Having a well-defined idea of the tech stack and the fundamental problem you are trying to solve is the crucial starting point. Business requirements change rapidly which means it is more important to have the right building blocks in place to accommodate that need. Hire skills which can help quickly establish and build your Data warehouse fundamentals which will be used to build future processes and pipelines.

Building a successful data warehousing team necessitates a strategic blend of skill sets and practices. Start by recruiting professionals with a diverse array of skills, including data architects, ETL developers, data analysts, and project managers. Ensure the team is proficient in SQL, data modeling, ETL processes, and cloud computing technologies. Pair this technical acumen with strong business understanding to align the team’s efforts with organizational objectives. Implement robust project management and agile methodologies to streamline workflows, foster collaboration, and ensure timely delivery of projects. Lastly, Invest in continuous training and certifications to keep the team updated on the latest industry trends and technologies.

I made a mistake once in pushing on Snowflake instead of RDS. In a way, it's because of promised high loads of data and a need for scaling. Ultimately, it was less than 0.5TB of data, which would have worked smoothly and cheaper on RDS with more frequent updates and live dashboards. I'd like you to learn from my mistakes and go for some fancier solution than a good old relational DB, only if it's higher than 1TB.

Drawing upon my own experience, I’d like to offer a slightly different perspective. While selecting the right architecture and tools is undoubtedly crucial, the essence of data warehousing is based on principles and logic rather than the tools themselves. In the age of data lakes, if a robust data lake infrastructure is already in place, there may be no need to look elsewhere or invest in another database to establish a data warehouse. It’s pivotal to remember that tools and technologies will evolve, but the core principles of data warehousing will remain steadfast. It’s these principles that drive effective data management, not merely the tools employed.

in my experience it’s not normally the tool choice that makes you succeed or fail as must of them all have same capabilities always about the implementation for me.

The velocity of data matters these days - like Velocidata provides super speed data transfer, like latest meta data management tools - check my post on my linkedin to see latest data trends

Building a successful data warehousing team involves hiring or training individuals with specific skills for roles like data modeling and analysis. It's essential to bring in diverse talents from different backgrounds, as this diversity enriches the team's overall capabilities and enhances the quality of data and insights they can provide.

For Building a successful data warehousing team , you always need to account for the changes that are happening in the technology segment, with new open source tools coming in, hence it is essential to bring in talent and also provide them some bandwidth to learn and up skill based on the latest data skills. This would bring in diversity and also provide great future proofing while building solution.

I believe that building a team of diverse and skilled professionals, whether through hiring or training, is essential. It not only enhances the team’s capability to handle complex data challenges but also ensures that the insights generated are well-rounded and innovative. Investing in your team is an investment in the future success and competitiveness of your business.

At many places, the sales/managers do not understand that "datawarehousing" does not end with somebody knowing to write database queries. It is utmost imperative to have an individual part of the DW team to atleast understand the difference between data engineer/data analysis/data visualization/data modelling and to be if not expert but atleast proficient in 3 of the 6 skills required. It would be worth much more if , instead of 5 experts in 5 different skills , there is a blend , awareness and understanding so as to bring the perspective. Datwarehousing is about analysing,modelling engineering,transforming, visualizing and communicating. The team members would be able to complement each other only if they understand what the other require.

Continuous improvement is often facilitated by a clear and agile approach. It enables a team to discover areas for improvement and make required adjustments by analysing and reflecting on the workflow on a regular basis. This iterative method guarantees that the workflow is always optimised and in line with the project's objectives.Throughout my career, I have witnessed personally the advantages of a clear and agile workflow. It has allowed my teams to manage projects more efficiently, generate higher-quality results, and adapt to the data industry's changing nature. Creating such a system is not without difficulties, but the benefits are well worth the effort.

From my experience, the dynamic nature of a datawarehouse necessitates the involvement of diverse teams, from architects, data engineers, and business analysts to reporting engineers, security experts, and data scientists. Agile methodologies shine in this setting, as they foster a collaborative environment and ensure that all teams are aligned on the objectives, approach, and expected outcomes. Iterative sprints, daily stand-ups, and retrospective meetings not only provide visibility into each team’s progress but also address bottlenecks or misalignments promptly. By adopting agile, we are essentially ensuring a harmonious symphony among various teams, each contributing its expertise to unified goal – a robust efficient datawarehouse

The best teams where I've worked had a very good culture of caring what they ship, and they actively discussed related matters to the project. Sometimes not all people are comfortable in sharing, so usually it's the team leader's job to tackle situations delicately and involve knowledge sharing. If needed he/she would ask to elaborate on some solution or usage of the new framework during team meetings. This allow team to feel more comfortable in sharing their findings.

New tools and architecture appear in the field of data every other day the best way to create a good team is to focus on the culture. We need a good balance of tech and business skills to create a good dw team. Insights are only adopted if we can convince the business through good brainstorming sessions and showcases. If we have a good culture then we can solve any business problem through collaboration and positive energy. Skills are always teachable through training and good hires who can cross-train.

Actually, I have never seen a team with good KPIs, not even sure if there are such. Data teams in general are doing some features they got pushed from management, and then when s**t hits the fan they're out there putting it out. Query-driven development also pushes everyone to quickly deliver some results while creating tons of debt. If someone has some good KPIs to put on a team I'm all ears. Maybe it's just my experience talking...

Adoption metrics are very important to measure the success of any business campaign and that also applies to the success of Dw team. We should see how much adoption our insights and dashboard are getting so that we can keep meeting the business to improve the adoption rate. It doesn't matter how skillful the team is if we can't create a good relationship with the business in the end we are to serve and help them.

Try establishing a defect rate goal for the team. If your deliveries are buggy, you might not have good quality controls, adequate source profiling, complete requirements or solid unit test plans. Our data engineering team goal was Zero defects reports from QA.

In my experience, building a successful data warehousing team hinges on adaptability to the organization's context. For IT service providers, a structured approach involving defined goals, architecture, and a skilled team is effective. However, in business-focused contexts, it's essential to understand the organization's financial landscape and adapt to evolving data needs. Once a solid infrastructure is in place, justifying the team's expense becomes crucial. This involves demonstrating alignment with business goals, quantifying benefits, and showcasing how the team contributes to better decision-making and revenue growth.

You have to define standards right away together with the newly formed team - Naming conventions, data quality checks, etc. This will allow everyone to voice their opinions and get everyone on the same page before this snowballs into big problems later. This will also make them owners and advocates of defined practices that will snowball on newcomers or other data practitioners in the company.

Some institutions do not have a solid understanding of the data, data sources, data modelling, data quality, data engineering, data warehousing interlinkage and complexity spawned within this data mixbowl. The vision is to have instant reporting solutions from whatever available data remnants lying about. On top of that, any able bodied individual capable of keyboard usage or alternatively, fogging a mirror, is also thrown into the mix. That's where the disparity between the strategy and tactical teams engulfs an unforeseeable void, ripe for failure. It would thus be more appropriate to understand all competencies and roles required. Assessing and acknowledging team strengths and weaknesses coupled with experience is the first step.

Focus on Interpersonal Skills along with technical skills as it is the deciding factor for creating meaningful stories. Also, have a mix of different personalities in a team that compliment each other so they grow and push each other in their respective area of strengths.