As Steve Miller, formerly at Gresham Tech pointed out: “One fundamental barrier is that most banks and large enterprises rely on architectures that have evolved over time. In fact, ‘architecture’ may not even be the right term, as that implies a level of intent and design that can often be wholly absent. In a sense these…
Once you have clean data, why stint your investment? Curation isn’t just about certain datasets (as many would have it) nor just about metadata, but about sustaining the health & value of your entire data-fuelled corporate cardiovascular system. Just like the human body, a blockage anywhere in the system can prove fatal. Think again about…
In an enterprise application environment, Master Data Management (MDM) used to refer to the desire to achieve a ‘golden record’ within a data domain. It was the holy grail of enterprise computing – a single version of the truth in a single repository, indicating mastery of organisational data – but then reality set in. MDM…
Modelling techniques are vital in digital, where the distinction between logical and physical dataflows has been overtaken by the need to ensure the business has the data to deliver on its intentions – from the centre to the edge and all points in between. This is such a complex environment that visual presentation is essential.…
Given that data sources may include the following, the chances of your migration (to cloud) working first time round, are very small: Each cloud has its own characteristics and may be informed by: Different data sources require different discovery and preparation: If the source data is in a packaged application, this does not necessarily guarantee…
If you have massive amounts of data which needs to be available across the extent of your organisation, even your ecosystem, you will need data engineering. Data engineering could be regarded as the antidote to (prevalent) organic (as opposed to purposefully architected) information flows in application-centric organisations. You need to design & deliver optimal circulation…
Data validation tests the accuracy, consistency (of format and standards), quality and integrity of the data (& associated metadata) for onward information engineering, data provisioning and curation. Note that ‘reliable’ data still needs to be validated. Even though this is not its primary purpose, data anomalies revealed by validation techniques can reveal hitherto unnoticed business…
It seems obvious, but migrating to cloud can be a frustrating process if your data is not suitably transformed before you try the ETL process. As data volumes get bigger; formats more varied and growth, exponential, data transformation techniques are vital. Routine transformations ensure harmony of data format and target destination. But more importantly, data…
Poor data quality, taking a reactive, rather than proactive, stance to data errors is a massive own goal. DQ negatively impacts the business so that: Financial data, trades & transactions, are highly regulated and subject to time stamping. Delays and mistakes are not only corrosive, they have much more immediate and damaging implications. The European…