In the face of a challenging macroeconomic climate, the UK digital advertising market remains remarkably strong, expected to reach $35.43bn by the end of this year. With advertisers increasingly relying on digital channels for driving brand awareness and sales, platforms like Connected TV (CTV), digital audio and digital out-of-home (DOOH) are picking up a larger slice of the ad spend pie. In contrast to just a few years ago, this investment would have traditionally been allocated to offline media. This is not to say that the industry is without its problems however. The economic situation, amongst other geopolitical pressures, is having an adverse effect on the sector, forcing media planners to think more short-term and reactively.

Comparing data to oil, has become almost a cliché, and although this image correctly translates the potential value of data it doesn't translate the challenges when it comes to enable that value. Comparing data to water may be a more accurate way to describe the challenges most organizations face when trying to get actionable insights from their data - Despite being in a sea of data, they can't benefit from it. Just like potable water, actionable data is also a scarce resource. We've witnessed, in these last few years, to organizations investing heavily on new IT infrastructure, on new digital channels, leading to volumes of data flowing into their systems increasing exponentially. And now business leaders want to see value being drawn from all this data – and started investing breaking down the data silos or creating data lakes – to enable them to retrieve those insights and generate business value from their data.

This article was published as a part of the Data Science Blogathon. In this article, we shall discuss the upcoming innovations in the field of artificial intelligence, big data, machine learning and overall, Data Science Trends in 2022. Times change, technology improves and our lives get better. Deep learning, natural language processing, and computer vision are examples of technologies that have emerged as a result of the rise of Data Science as a field of research and practical application throughout the previous century. In general, it has aided the development of machine learning (ML) as a means of achieving artificial intelligence (AI), a field of technology that is fast changing the way we work and live.

Capturing big data is easy. What's difficult is to corral, tag, govern, and utilize it. NetApp, a hybrid cloud provider, sees cloud automation as a practice that enables IT, developers, and teams to develop, modify, and disassemble resources automatically on the cloud. Cloud computing provides services whenever it is required. Yet, you need support to utilize these resources to further test, identify, and take them down when the requirement is no longer needed. Completing the process requires a lot of manual effort and is time-consuming. This is when cloud automation intervenes.

Uncovering insights is hard because we depend on data to answer questions. The problem is that most companies are not collecting "the right" (well-selected) data, they don't have the right processes in place for analyzing that data, and the insights that come from this analysis aren't being applied back into the business. Even reports generated by humans are most of the time a one-way street and outdated once presented. As a result, they're holding back from making critical decisions on how to improve their business. The solution to these struggles is the strategic adoption of smart systems that enable natural human-machine interaction.

Codelitt uses technology and user centric design to solve corporate problems with start-up speed, technology, and innovation. They focus on the build side to develop scalable solutions in platforms such as Web Mobile, AR/VR, AI/ML, Robotics and IoT for large enterprises and offer a full stack of services from idea validation/ ideation, design, and development. Codelitt partners with Box to create new opportunities in enterprise digital content management and has developed Ada, a custom application which utilizes machine learning and Box Skills to intelligently extract actionable data from documents. Manual processes for data retrieval/data entry, are still prevalent in many large enterprises across a variety of industries. For most, a vast amount of data and information remains unharnessed because it lives inside unstructured documents.

Quickly shifting to remote work has enterprises looking to meet the ops needs of a suddenly distributed team, and there are open source options to get them there. The recent mad rush to scale to remote work may prove to be a key chapter in DevOps and AIOps evolution. This need for rapid, widescale change is creating a real conundrum concerning AIOps, DevOps, and ITSM, as organizations seek the best monitoring and incident response solution for their now distributed enterprises. The key question both the DevOps and IT service management (ITSM) communities need to answer is how quickly they can pivot and adapt to increasing demands for operational intelligence. Artificial intelligence for IT Operations (AIOps) brings together artificial intelligence (AI), analytics, and machine learning (ML) to automate the identification and remediation of IT operations issues.

The term is thrown around in marketing tool ad copy, by marketing gurus and hyped by the media. For many, AI is an enigma surrounded by buzzwords. But the irony is, as much as the hype has overstated what AI might do in the next years, the reality of how AI is already used today in marketing is often under-recognized. Your life is already machine-assisted, and your marketing can be, too. The best way to understand artificial intelligence is as an umbrella term.

The term is thrown around in marketing tool ad copy, by marketing gurus and hyped by the media. Yet, a concrete definition is elusive. For many, AI is an enigma surrounded by buzzwords. But the irony is, as much as the hype has overstated what AI might do in the next years, the reality of how AI is already used today in marketing is often under-recognized. Your life is already machine-assisted, and your marketing can be, too.

Antonio Piraino is Chief Technology Officer at ScienceLogic, where he guides the company's IT management vision and product strategy. Recently, Gartner announced its top 10 strategic technology trends for 2019. It is a nice list, touching on digital transformation trends that range from empowered edge computing to artificial intelligence-driven autonomous things. But while Gartner's trends sound great in annual reports and Forbes articles, operationally, most enterprises aren't properly (or digitally) prepared to adopt these trends. Today's pace of business and the disorderly data that's needed to make sense of it all.