How are you managing your data, for the benefit of your customers and organisation?
There’s a constant stream of data coming in from websites, ordering systems, billing systems, customer relationship management (CRM) applications, marketing activity and phones.
Your data is gold. It contains a hidden wealth of information you probably haven’t even considered yet.
Making the data ready to analyse, so you can find out what’s going on in your business, can be a time sink. Your teams can spend hours or days each week extracting portions of data from various applications, so they can wrangle the data in Excel to produce reports. This effort can be duplicated across the organisation, with multiple teams performing the same tasks and often coming up with different results. People then spend time trying to reconcile multiple versions of the truth. It can rapidly spiral out of control.
This is time that could be used instead to productively get on with developing insights.
A data solution does all of that for you. It automates the unproductive prep work, so you can get on with generating useful insights and making better decisions, faster. It’s a system that organises your data so that you get the best value from it – not just learning about your customers and the health of your business, but in many cases also making money from it.
It gives your entire company a single, trusted source of data that you can ask questions of – such as ‘who are my most profitable customers?’ or ‘who could be my most profitable customers?’. Data is the most important asset many organisations have, after their people. Investing in this asset and treating it with care and respect will create a strong return on investment.
However, it’s common for data systems to fail to deliver the benefits I’ve described. Setting one up can be a significant programme of work. There can be an expectation chasm between what you need and what ends up getting delivered.
Systems can go wrong for all sorts of reasons, for example focusing on the wrong outcomes such as machine learning when the source data isn’t of sufficient quality. Or the wrong tools and techniques may be used.
Here are 4 tips for avoiding the pitfalls and getting it right first time, to maximise the value of a data transformation.
We tend to favour Snowflake for the data platform. It can run on AWS, Azure or Google, so basically anywhere – so it’s platform agnostic. It’s also very cost effective. Over the years we have developed a number of tools that accelerate deployment to enable the ingestion, storage, reporting and analysis of data, and we’ve learned how to make things work.
When building out the solution, focus on the requirements. If you understand the business processes and events that generate data, it will really help when you’re building out the context and validity of any data model development.
Bringing all the data together is only part of the approach. The old adage of ‘garbage in – garbage out’ can be easily managed with some simple data governance tools to ensure the right quality data is documented, understood and processed. This is especially so in the regulated financial services sector.
Your data is gold, as it contains a hidden wealth of information you probably haven’t even considered yet.
If anything I’ve discussed here is of interest, do get in touch – our favourite thing is to talk about how data can deliver value in the most elegant, effective way.
Dave Ewles is the Data Engineering Lead at The Data Practice. He has a talent for identifying the real problem, developing the most elegant solution, and delivering it in the most efficient way. Dave has vast knowledge of cloud data solutions and is the safest pair of hands for realising the best possible value. He’s worked across many sectors.
Photo credit: Photo by caeraglo
