How do we gain insight into our software so we can make it run better and so we can make better business decisions?

What if you could avoid common problems in your QA and DevOps such as high-defect rates, missed release timelines, and test coverage gaps?

Based on Ventana Research, an estimated of 1 billion people still use Excel spreadsheets for their reporting requirements. Organizations are wasting valuable resources, time and money by spending days every week aggregating data for reports. Most customers have islands of information, with no end-to-end analytics that would, for example, show them how project costs are related to end-user quality.



However, in today’s digital era where customers demand continuous innovations through continuous deliveries, organisations now require a new level of digitalization away from traditional analytic tools that still rely on spreadsheets for reporting, and move towards new BI tools that run at cloud-scale, employ predictive algorithms, and deliver faster insights at the speed of the business.

As software development and testing teams move from waterfall approaches to continuous delivery and continuous testing, QA and DevOps needs monitoring that brings greater visibility across the entire lifecycle, and unifies people, processes, and tools.

By integrating business intelligence tools into the QA and DevOps process , project managers will be able to spend less time copying and pasting data from one system to another and more time managing their projects, and enables them to report accurately to senior stakeholders whether they are getting value for money from their major investment.

Reading through a recent article on www.qa-financial.com about UBS who has created a single global platform for the quality assurance of its apps, you come to realise that most organisations are moving towards a new model where test automation, business intelligence and advanced analytics will be the must. As Kevin Adams global head of quality assurance at UBS, states , the QA teams may not be dealing with regulators directly, but they are the subject matter experts. This means that any technical decisions taken in the QA and DevOps, have an impact on the business decisions of the stakeholders and what better way to improve these decisions than to employ BI and analytics as a standard practice.

In late August 2015, HSBC suffered a system failure that resulted in 275,000 individual payments not being made, leaving many people without pay during a critical holiday weekend.2 Less than a week into 2016, a two-day outage at the bank left millions of customers unable to access their accounts online. Such software failures can be prevented, by leveraging BI and Analytics for better test planning, optimized test execution, early defect detection and defect prediction, which ultimately transforms the QA process.

Same principles can be applied in the DevOps process. DevOps is all about speed, automation and iteration, but when Analytics are added in the process, a competitive advantage is created as teams are able to get insight into meaningful information, ultimately closing the gap, and putting development and operations into better synch. Predictive analytics examines trends and activities along with real-time data, so DevOps can better analyse and predict historical and future events.

Developers can monitor, troubleshoot and improve the performance of production applications, as well as quickly understand the root cause of slow queries, slow loading pages and other application issues.

There is no more flying blind when new features are released. Historically this data was buried in log files or database entries that only developers could access. With Analytics integrated into the process, project managers, test managers, designers and analysts, can easily access real-time data about projects use this information to the benefit of the whole organisation.

Regardless of whether an organization has a head-start in DevOps adoption or is just about to start its DevOps journey, Business Intelligence and  Advanced Analytics can make the difference between failure and success.