With 2018 now live and kicking, I decided to take a look back at what subjects proved most popular in 2017 based on our blog. With GDPR coming up fast in May, interest in Data Regulations has unsurprisingly jumped up the list, but read on to take a look at what other subjects piqued your curiosity and delve into some of the best articles from last year.
If you are a data quality professional then you have more than likely heard the terms Data Lake, Data Swamp, Data Ocean and even Data Pond and Data Puddle. In fact, stick the word ‘data’ in front of any word used to name a body of water and you’ve more than likely found a commonly used term in the industry (although I have yet to hear of a Data Paddling Pool’). As the gatekeeper of our ever-growing Glossary section, I have picked out some of the most commonly mistaken terms – and with help from our team of experts, I’ve explained how we define them.
Many of us are aware of the benefits that high-quality data can bring to an organisation including improvements in operational efficiencies, better decision making and avoidance of risk. It’s getting started that can often be the biggest road block. By that I mean, if you can’t quantify the tangible returns that investment in data quality can bring, how do you get buy-in for investment in it?
Traditionally, organisations have tackled their SCV requirement through the deployment of an MDM platform. And yet, as Philip discusses in his paper, ‘MDM has always been complex, costly and time-consuming to implement’ and so not necessarily, therefore, in tune with modern business requirements. Layer in an increase in regulation and we have a perfect storm of reasons for organisations to seek an alternative route.
So, what options are there for organisations looking to keep costs to a minimum or take a more agile approach to developing an SCV?
The proliferation of data provides us with both challenges, and, if managed correctly, great opportunities. So where to start? It’s all about being able to get a full and complete view of your data. Without the ability to bring relevant data elements together into a single view it’s simply impossible to ‘see the wood for the trees’.
You can finally ignore the assumptions, anecdotes and accusations about your data; data migration will expose the whole truth (and nothing but the truth!) about your data.
Most free tools will only profile and analyse a sample of your data – but not ours. Profiling 25,000 records of 1 million will not identify all data issues, you need to profile all 1 million.
From experience, I find that most companies struggle with making their data quality assessment results compelling because they take a data-centric viewpoint. They show stats and metrics that, whilst valuable to the data community, can be dull as dishwater to the business leader who needs to make tough decisions on where to focus limited staff and financial resources.
In this two-part series I want to share some practical, actionable techniques to help you create a more business focused data quality assessment.
One of the mistakes I made early in my career was to create exhaustive data quality assessments that failed to motivate and engage the business community. It was only when I tweaked my approach to tools, strategy and storytelling did the business finally sit up and in most cases, take action.
In this blog post I want to share some background to the data quality assessment process so that you can understand the fundamentals.
With love in the air there has never been a better time to get more intimate with your customer. Using this romantic season as a muse I’d like to show you a more data-driven solution to customer care.