In brief, modern data architecture is the structure of a system’s data assets and a management of the workflow that data will pass through. It is the foundation by which data is sourced, cleaned, stored and then analysed for later use.
Within this context the data is being moulded, so how does this happen?
Fundamental to this are the applications that are used. The data arrives in its ‘raw’ form, which has been collected from various sources. One context in which this might take place is within high performance sport: Opta is a company that record data on English Premier League football matches. The data is subsequently ‘ingested’, usually with the help of an API (application programming interface), to be imported or stored for immediate use. Applications such as Alteryx are extremely powerful tools which allow the user to do this. For the sake of continuity, in a footballing context, the data is then ‘cleaned’. Again, with the use of Alteryx, the user is able to breakdown and manipulate the data in order to make what was once a large collection of information, now concise and usable. Many other applications similar to Alteryx do exist. However, based upon its processing and machine learning capabilities, in what is now a saturated market of analytical products, Alteryx is unique not only its broad functionality but also it intuitive and user friendly interface. Now the data has been prepared it needs to be stored. Depending on the how the data is being used and how sensitive it may be it is a choice between cloud, physical or a hybrid storage structures. In the advent of technological advances over the past few decades, cloud computing structures have become more normalised. Regardless of which storage method one uses, the data is ready to be analysed.
As far as Alteryx is concerned, its purpose has been fulfilled and other analytical tools will takes its place. Tableau is an analytical software that visualises the data from this stage onwards. Again, this is an extremely powerful tool and one which has seemingly limitless capabilities in terms of how one wishes to view their data.
While this is a very concise overview defining modern data architecture you may now ask, why is this important? From the perspective of a business this data could represent one’s sales. Visually, this would demonstrate where best to allocate resources in order to improve the productiveness and efficiency for the business model without having to manually trawl through spreadsheet upon spreadsheet. Equally, the potential to automate this process could reduce the human error associated to carrying out these repetitive tasks. If it was not already clear already, data analytics is an extremely effective answer to the question: how can I reduce costs, save time and make marginal gains on my business? Therefore, approaching the subject of modern data architecture serves to be a fruitful enterprise now, and in the future.
