VC5 Consulting’s Post

Note that there are additional opportunities mentioned in the comments section.

This post made me smile this morning! Most business leaders understand sales and marketing, since they usually have that background. With everything becoming “data-driven” a resource grab for data talent is ongoing. However, most business leaders have no idea how nuanced the data field actually is and “data scientist” is the sexiest title most people think of. (Well maybe not this week, AI engineer is taking the stage….which is still data dependent, btw). Hence, the painfully truthful post below. If you have data that is not where it needs to be or the shape it needs to be in, you need to start with data engineers. When the data starts to flow and has meaning, then a data scientist can work their magic. Foundation first, data application second, insights later. #digitaltransformation #datastrategy #dataengineering #digitalbusiness

The 1st Data Scientist. It's the hardest hire. They have to bring so much more to the table than a regular Data Science hire in an established team. Consider what the 1st Data Scientist needs to take on: - Build great relationships with colleagues and Execs that might not have worked with DS before - Get access to the right Data - Consider data Infrastructure & tooling they have to work with - Prioritise use cases & projects, based on their potential value + the data available to work with. A complex commercial equation. - Present insights to leaders - And ultimately they’ll need to develop prototypes and tools which actually deliver value. Oh, and you’ll probably also need to be a Data Engineer a lot of the time as well 😊 I’m telling you though, for the few able to pull it off over a 12 – 18 month period, you’ll be worth your wait in gold. Because most in the space can’t deal with this level of autonomy & blank canvas. #datascience #dataengineering #datastrategy

This is a solid piece of advice. Read it and heed it. #dataengineering #datateam #datastrategy #datarogue

How simple and correct this is! To leverage on the importance of having the data engineer first: 📌Foundation of Data Infrastructure: Data engineers build and maintain the foundational infrastructure required for collecting, storing, and processing data. This is critical for any AI initiatives as high-quality, well-organized data is essential for training accurate machine learning models. 📌Data Quality and Consistency: Data engineers ensure that data is clean, consistent, and reliable. Without this, any insights or models developed from the data could be flawed, leading to inaccurate conclusions or ineffective AI solutions. 📌Scalability: As organizations grow, the data they handle also scales. Data engineers are instrumental in designing systems that can scale efficiently, ensuring that as more data is collected, it can be handled without performance bottlenecks. 📌Cross-Departmental Collaboration: Data engineers often serve as a bridge between various departments within an organization, facilitating data integration and flow, which is crucial for a holistic approach to AI. 📌Regulatory Compliance: With the increasing importance of data privacy and protection, data engineers help organizations comply with regulations like GDPR or CCPA by implementing proper data governance. 📌Innovation and Competitive Edge: Having a well-structured data engineering process allows organizations to quickly experiment with new AI technologies and stay ahead in the market. 📌Cost Efficiency: Proper data engineering can help avoid costly mistakes and inefficiencies by ensuring that data systems are optimized and well-maintained. 📌Data-Driven Culture: Data engineers are at the heart of cultivating a data-driven culture within an organization, enabling other roles to leverage data effectively for decision-making. #dataengineering

If you hire a data scientist before you've hired a data engineer, congratulations, you've just hired an entry-level data engineer!* *Unless they've had the same thing happen to them multiple times, in which case, congratulations, you've hired a seasoned data engineer. #datascience #dataengineering

After you establish the right data leadership (full-time or fractional), your first hire should be a data engineer. 👨💻 If you hire a data analyst first, they'll need to be a data engineer first to build the data infrastructure to support analytics. 👷♀️ If you hire a data architect first, they'll need to understand all the company's systems, determine what platform/tools to use, and likely be a data engineer first to build the data infrastructure and then refine it accordingly. 👩🔬 If you hire a data scientist first, you can speak to any of the 90% of data scientists turned engineers over the last 5 years because they couldn't build appropriate models with the trash (or non-existent) data infrastructure they were handed. Find a data engineer with a solid background in designing and architecting data platforms, and then you can hire the architects, analysts, and scientists afterward. #EGDataGuy #datateam #dataengineering

I can't agree enough with this... A lot of Data Analysts/Scientists are becoming Data Engineers, not because they originally wanted to, but because the infrastructure they work with is so bad that they just have to try to get the skills to build one. Secondly, I think this is another good reason we now have ELT and Analytics Engineering as against ETL and Data Engineering. #dataengineering #analyticsengineering #etltools #elt

I think this is mistake. Start with business/data analyst with domain expertise and focus on driving value for the business. Start with most important and impactful use cases and go from there. Do a proper modelling. Climb the data maturity ladder as you become more able to kick off advanced use cases. Get tech specialists afterwards when you need to super optimize particular aspects. There are several platforms on the market that can help any company to leapfrog this initial buildup period that is just sunken costs and let you focus on the results. I have seen too make teams just digging into building own stuff for the sake of building with intangible business value but significant costs incurred.