Recent years have seen many companies consolidate all their data into a data lake/warehouse of some sort. Once it’s all consolidated, what next?
Many companies consolidate data with a field of dreams mindset – “build it and they will come”, however a comprehensive data strategy is needed if the ultimate goals of an organisation are to be realised: monetisation through Machine Learning and AI is an oft-cited goal. Unfortunately, before one rushes into the enticing world of machine learning, one should lay more mundane foundations. Indeed, in data science, estimates vary between 50% to 80% of the time taken is devoted to so-called data-wrangling. Further, Google estimates ML projects produce 5% ML code and 95% “glue code”. If this is the reality we face, what foundations are required before one can dive headlong into ML?
Machine Learning, alongside a mature Data Science, will help to bring IT and business closer together. By leveraging data for actionable insights, IT will increasingly drive business value. Agile and DevOps practices enable the continuous delivery of business value through productionised machine learning models and software delivery.
On previous blog posts we have provided examples of different types of acceptance tests coverage, UI, API and Performance. One area where automation is often lacking is around validating the security of the application under test. This has been discussed in the post on non functional testing You Are Ignoring Non-functional Testing. With this post we will enhance the automation framework to quickly check for some common security flaws.
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