Here at Mango, we are often asked to come and help companies who are in a mess with their data. They have huge technical debt, they can’t link all their data sources and the number of reports they have has ballooned beyond control. Everyone has their own version of the truth and business units are involved in ‘data wars’ where their data is right and everyone else has the wrong data. How does this happen? Put quite simply, hires are focused for the ‘shiny’, interesting aspects of data science where it is easy for the business to see how they get value from that hire – business intelligence (BI), management information (MI), or Data Scientists. This ignores the more technical and less exciting but essential pillar of delivering business value: the data management and data engineering pillar which is critical to underpin any data-driven business.
The thing is, you may have the best data team who can programme, model, visualise and report with data but without well-managed, curated data, over the longer term your systems and processes will be thrown into chaos and your data will become unmanageable. This isn’t because these analytical professionals aren’t doing their job, it’s because their job is extracting value from insight, not making sure the machine behind it all is ticking over smoothly. In F1, the driver would be useless without a whole range of engineers and mechanics. If your business only has BI and MI analysts or Data Scientists, you are asking the driver to win an F1 race with a Morris Minor – you need a Data Engineer.
Turning data into wisdom – the role of the Data Engineer
Why does this happen? Quite simply, organisations often might look at the price of hiring a senior experienced head of data/data engineering or a building a data management function and decide they don’t need one and instead hire a significantly cheaper BI resource instead, expecting this person to do it all. As a role, a head of data/data engineering has changed massively since the advent of advanced analytics and now requires both specialist and strategic knowledge to build the reliable systems to collect, transform, store and provision data for analytics or other complex purposes. The right technical infrastructure required to turn the data into wisdom in a repeatable manner bridge the gap between strategy and execution.
From assessing a proliferation of data silos to hard to maintaining “legacy” data processing systems are just common challenges and with modern platforms, data warehouses are a more collaborative affair than ever before, many of the same principles still hold. A data engineer understands data modelling techniques to build data warehouses that can be trusted, maintained, and that deliver exactly what analysts need.
It’s a false economy to overlook the critical engineering needs that a data-driven busines has. There is also cost in fiscal terms. With poorly designed systems that don’t perform, we have seen costs of transformation projects moving to the cloud double purely because of poor data management. Add to that the cost of having to constantly upgrade database servers so they can keep up with the ever increasing workload and lifetime costs get even higher. This ignores the harder to quantify opportunity cost of not being able to leverage your data, or the cultural impact of business units arguing because they have a different data-driven view of the business.
Its essential to look at the investment in an appropriate data function holistically in terms of long term gain through increased opportunities to leverage data and make better decisions, a more efficient cost base for your technology over the long term alongside an easier transformation pathway when you need to evolve as a business. Without taking that long-term view of your business, it can be hard to see how a data management function can add value. However, without one, the opportunity for improved insight and the cultural benefit of happier staff who understand how to leverage data in a way that is sustainable and beneficial to all involved will be lost.
The Key to Extracting Value from your Data
Organisations need a good data engineering function to access the right data, at the right time, and with sufficient quality to empower analytics. But what is the definition of a data engineer’s role and why is this function so crucial to bridging the gap between strategy and execution when it comes to delivering a data science project?
As data experts, we know what companies need to do to become data-driven. If you are struggling to see how a data function fits in your business or don’t know how to move to the data-driven nirvana, we can help guide you on your whole journey, from first steps through to decisions being made from a ‘data first’ mindset.
Author: Dean Wood, Principal Data Scientist