With properly prepared data to fuel information production, the next task is an engineering one: to ensure that information production systems operates efficiently & effectively. Data processing is an intensive, resource-hungry task where the application of engineering principles can help optimise the production of information so that it is efficient, effective, scalable and performant.
The digital expectations of management and end users or consumers, means that data circulation should not be constrained at any point within the domain, especially where cloud and bandwidth is concerned. Cloud is a commoditised service and thus subject to change. Networks are prone to service provision bottlenecks. So data migration, workflow modelling & master information concepts (consistent with strategic information requirements) are important aspects of the successful engineering & management of data-centric cloud (or hybrid) platforms ,
Platforming is a common way forward for digital but platforms must produce reliable information at scale, at speed across an evolving ecosystem if they are to meet digital expectations. To achieve that with piecemeal systems is a huge challenge if information architecture remains the ad hoc organic environment which is usually the case. F1 performance requires holistic design, smart engineering, innovating with state-of-the-art interoperable components, to produce winning thrust for skilled drivers.
Data engineering is the application of engineering principles to ensure optimal flow, collection, availability, processing & protection of data as a business-critical resource. The smooth, consistent, routing & flow of data throughout its lifecycle is an essential aspect of a healthy, data-centric ecosystem.
With data as key organisational asset in the digital age, data management has a strategic role in the pursuit of value delivery. Overseeing data from raw source to value-add output must echo corporate strategy whilst fulfilling its traditional remit.
The performance of Cloud is adversely impacted by unusable or unnecessary data which attracts equally unnecessary costs, particularly upon exit. Clogging Cloud with poor or unnecessary data will also exacerbate any bandwidth choke points on your network. Is your shiny new Cloud choking?
As data volumes grow exponentially, modelling for conceptual & semantic integrity is a vital if you want to turn data into information, into knowledge & subsequently value for the business. Is your metadata equal to this task? Before you model, does the business have shared understanding?
Master Data Management focused on the creation of a 'golden record' within a data domain; a 'single version of the truth'. But does your 'truth' must have consistent meaning across applications, across the business & its entire ecosystem, virtually in real-time, with meaning and identity common to all stakeholders?