Developers today have a fantastic assortment of tools and technology available to them, which they use to construct the digital world around us. However, the sheer number of choices in the DevOps and CICD toolchains introduces a vast amount of complexity, which leads to multiple inefficiencies. Now a new discipline called developer productivity engineering (DPE) is emerging to tackle this problem, and advanced analytics and AI play big roles. While the advent of DevOps and continuous integration, continuous deployment (CICD) methods has made developers lives simpler in many respects, they have also unleashed new forces that hinder developer productivity, according to Hans Dockter, the CEO of Gradle, the for-profit company behind the leading open source build tool of the same name. For starters, testing is critical to ensure software is bug free and doesn't pose a security risk.

Artificial Intelligence (AI) is built on data. Yet, many organizations are still finding it hard to implement AI properly to make the most out of their data. There are concerns that the AI is not able to comprehend the data the way they want it to, especially with more businesses having their data stored across the multi-cloud and even on-premise. When it comes to data analytics, SAS has been a household vendor in the industry for years. The data analytics leader continues to pioneer new innovations when it comes to providing businesses with the insights they need in the best way possible.

Ten years ago, the authors posited that being a data scientist was the “sexiest job of the 21st century.” A decade later, does the claim stand up? The job has grown in popularity and is generally well-paid, and the field is projected to experience more growth than almost any other by 2029. But the job has changed, in both large and small ways. It’s become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown. How it operates in companies — and how executives need to think about managing data science efforts — has changed, too, as businesses now need to create and oversee diverse data science teams rather than searching for data scientist unicorns. Finally, companies need to think about what comes next, and how they can begin to think about democratizing data science.

Despite a massive increase in online activity during the COVID-19 pandemic, fraud on the Visa payment network is at an all time low, the company says. One of the chief reasons for that success is a big investment in advanced analytics and AI. The world changed in March 2020, when many countries went into lockdowns to prevent the spread of the virus that causes COVID-19. As retail shops and other physical locations closed, people's attention turned to the Internet for school, work, and play. Since late 2019, the volume of e-commerce traffic has grown by 50%, according to Visa.

Life sciences manufacturing is on the cusp of a seismic shift. Companies are beginning to understand that their focus needs to move from documents to data because data leads to insights and competitive advantages. The benefits of and initiatives that lead to a data-centric mindset dovetail nicely into those related to a quality mindset. Fortunately, these are synergistic goals. Making data-based decisions improves quality.

To help the company and its customers gain value from this data deluge, the Dell IT organization manages a massive data lake and a world-class set of tools for data analytics, machine learning, deep learning and artificial intelligence. At the heart of this data environment is a Greenplum database, a massively parallel data platform for structured data analytics, machine learning and AI. In a typical use case, this raw data gets parsed in Hadoop into a structured format, and then that structured data gets pumped into the Greenplum database, so business and IT users can consume it in analytics applications. The data is used by Dell Technologies employees and customers in the Americas, Europe, the Middle East, Asia and other geographic regions, according to Darryl Smith, chief data platform architect and distinguished engineer at Dell Technologies. For the full story, see the Dell Technologies case study "Analytics and AI in a massive data lake."

Recent years have brought a stream of exciting developments in the field of Business Process Management (BPM). We have seen a breathtaking uptake of business process automation technology, such as Robotic Process Automation (RPA). We have witnessed the rise of process mining, and promising evolutions in the areas of predictive process analytics and digital process twins. In the eyes of a business analyst, each of these technologies offers compelling opportunities to enhance operational excellence. However, if we look at these technologies in isolation, it is easy to miss the bigger picture and the wider space of opportunities that these technologies open when used jointly rather than applied in individual projects or silos.

The Defense Department's Chief Digital and Artificial Intelligence Office, a new hub to align disparate AI-centered pursuits across the vast enterprise, officially reached initial operating capacity this week--but much must still be puzzled out before it's totally realized this summer. John Sherman, DOD's recently Senate-confirmed chief information officer, will play a major role in seeing it through. He's taking the office's lead as acting chief digital and AI officer until the department completes its search for the right person to fill this first-of-a-kind position. "In addition to getting the OCDAO up and running for [full operational capacity], rest assured we'll remain laser-focused on our CIO duties--cybersecurity, digital modernization and other areas the department relies on us for," Sherman told reporters during a press call on Wednesday. He and two other senior defense officials shared fresh details about the new unit's establishment and what it's ultimately meant to accomplish.

SAS, the leader in analytics, asked its experts in health care, retail, government, fraud, data ethics and more. "Curiosity helps businesses address critical challenges – from improving job satisfaction to creating more innovative workplaces. Curiosity will be the most sought-after job skill in 2022 because curious employees help improve overall retention, even during the Great Resignation." "The pandemic upended expected business trajectories and exposed weaknesses in machine learning systems dependent on historical data and reasonably predictable patterns. This identified an acute need to bolster investments in traditional analytics teams and techniques for rapid data discovery and hypothesizing. Synthetic data generation will play a major role in helping businesses respond to continued dynamic markets and uncertainty in 2022."

As we have reached the end of 2021, my inbox has become stuffed with the now customary batch of emails, from tech companies and their PR agencies, sharing management's thoughts on what next year will hold for us, in the world of data, analytics and AI. As ever, the annual exercise of compiling sage predictions about the upcoming year, from executives around the industry, was a big effort. In fact, once all the prediction emails were consolidated, a 50-page document resulted. As with any big data exercise, my goal was to aggregate the data into groupings I could organize it by, both to tame the volume of the data and because the groupings are themselves instructive. Testing at home can provide peace of mind, and it doesn't have to take a long time or be terribly expensive.