Big Data, Cloud & DevOps

A proof of concept (POC) is a popular way for businesses to evaluate the viability of a system, product, or service to ensure it meets specific needs or sets of predefined requirements. What does running a POC mean in practice specifically for data science? When it comes to the evaluation of data science solutions, POCs should prove not just that a solution solves one particular, specific problem, but that a system will provide widespread value to the company: that it’s capable of bringing a data-driven perspective to a range of the business’s strategic objectives.

The impact of surveillance capitalism and this new data economy will be tremendous, but the outlook for the future is still up in the air. if we continue with an unregulated, poorly designed variation of surveillance capitalism, we stand to reap the consequences of a pretty inhumane, prescribed reality. If we take advantage of the brilliant people and institutions that the digital economy has empowered and train them, and the rest of us, to develop, use, and care for a suite of ethics-driven, nuanced, solutions-oriented products, we could easily wind up the benefactors.

P-Values are always a headache to explain even to someone who knows about them let alone someone who doesn’t understand statistics. In statistical hypothesis testing, the p-value or probability value is, for a given statistical model, the probability that, when the null hypothesis is true, the statistical summary such as the sample mean difference between two groups would be equal to, or more extreme than, the actual observed results. This post is about explaining p-values in an easy to understand way without all that pretentiousness of statisticians.

Now the technology cycle has thrown up edge computing but the shift is still in its infancy. The shift was partly created by the Internet of Things — the massive explosion in devices, all with their own processing power. This processing power was often under-utilised. The swing in the technology cycle to edge computing has also been driven by privacy concerns — the ability to store personal data on individuals’ own devices, rather than somewhere in the cloud.

With effective data ownership regulations, governments can ensure that businesses utilize confidential data ethically. However, only creating regulations is not enough. Users like us must be aware of the value of our data and avoid simply giving away our confidential data to businesses. In this manner, we can ensure that we own our data and our data doesn’t own us. Therefore, Governments must regulate data ownership to prevent privacy violations and ensure that businesses use modern technologies ethically.

Businesses now require regular and sustained innovation, but this is problematic for many organizations more used to operating in safe mode. The situation is recoverable when leadership commits to building culture of innovation. This is especially important in IT, because it’s here where innovation now starts and ends. To this end, organizations are rushing to embrace Agile and DevOps – the practices of choice for rapidly delivering high quality software. But when it comes to culture nothing is easy.  Here are five tips to help ensure success.

Data breaches are becoming more frequent and damaging. This failure to solve the growing security crisis is crippling the confidence of large enterprises in their ambition to move to the cloud, which can be a risky, but necessary venture. Why is it necessary? The legacy implications of not moving to the cloud are affecting data. A data-centric approach to security and homomorphic encryption is required to solve this problem and give companies the confidence to move to the cloud.

The Platform business model is clearly today’s economic winner. Rather than seeking to control the means of production, platform companies focus on the means of connection,  connecting and facilitating the interactions between buyers and sellers, suppliers and consumers, or even just friends and families. And they generate revenue by collecting a “toll” from each interaction on their platform. Platforms gather data and use to feed artificial intelligence that helps manage the platform, an essential step due to the scale of due to scale of their organization.

Till now quality management was trying to justify investments in testing via the ROI metric, it is now time for a change. When implementing continuous testing, and running test automation cycles multiple times a day, in different environment, but different personas, ROI becomes an obsolete term since the measures are much different than before. The key term for the next 5-10 years when trying to measure and justify investment in testing should be VALUE.

It is important to understand DevOps is not an island. Enterprises implementing DevOps should be aware that DevOps interoperates with other IT systems and practices. Enterprises are well-advised to put in place governance policies that encourage the selection of tool-agnostic IT partners with solutions that best suit the needs of each unique enterprise and can integrate and evolve DevOps together with all their IT systems. One of the essential best practices areas that make up successful DevOps is continuous testing (CT).

It’s essential that the technology skills development program be aligned in purpose and design with the intended business objective. That may sound obvious, but organizations have a way of accelerating beyond the original scope and intentions. Successfully navigating digital transformation requires a team with well-aligned IT roles and skill sets. People are the make-or-break element of a high-performing IT organization. There is simply no replacement for people with the right skills, attitudes and traits.