Optimising data circulation
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 of sufficient, correct & timely data through the systems of your organisation.
When optimised, healthy, performant data flows are vital for:
- provisioning data optimally to edge operations;
- managing the data life-cycle & avoiding corporate arteriosclerosis;
- supporting the quality of management decision-making through reports that are timely & accurate;
- enabling accurate analytics or pipelines for data-science/BI/MI/AI initiatives.
Sufficient & relevant data
But data engineering isn’t just about speed & timeliness. It’s also about making sure you have sufficient & relevant data, so services may also include data calculation, reconciliation, consolidation, aggregation & synchronisation:
- calculation, such as OLAP or ROLAP, which need to happen ‘outside’ main data processing;
- reconciliation, making sure it all adds up;
- consolidation, marrying (and in some cases, ignoring) different data sources and structures with all the inherent issues that come with;
- aggregation, the use of subtotal pre-calculations to reduce server load and ‘noise’;
- synchronisation, making sure all parts of an enterprise and its ecosystem are working off the same information – and so is the customer.
Data engineering principles
‘Enginers’ of early military machines were regarded as creative and ‘ingenious’ inventors, essential to the success of any campaign. Data engineering principles are also centred on strategic advantage:
- design for change – an evolutionary constant;
- build for resilience – against long term tests such as scale and performance; and
- simplify – minimise digital plumbing & inherent complexity.
Interdisciplinary data engineering expertise
Based on our experience, we take a broad view of this service.
Data engineering requires not just technical, but interdisciplinary competencies, to ensure that the technology really addresses the business use, financial or regulatory case. We have that rare combination of skills technical and interdisciplinary expertise. This was used to good effect in the hospitality & leisure industry where meshing a sequence of multi-dimensional, mutable data flows, dependencies, entities & jurisdictions was the thorny issue troubling a global brand.
Organisations planning AI or ML projects would benefit from securing their data infrastructure first so that the AI & data science experts can devote their time to AI & ML, not the data management, engineering or data quality which underpins it. If you don’t, your AI may disappoint.