This article was published as a part of the Data Science Blogathon. The distribution of computer services through the internet is known as cloud computing. Businesses can adopt the cloud computing paradigm, where they can rent IT equipment and services instead of purchasing and operating their data centers. These services cover everything from basic infrastructures like networking, servers, storage, databases, and software to cutting-edge tools like artificial intelligence (AI) and machine learning systems. As less equipment needs to be rented and maintained, the importance of cloud analytics translates into businesses cutting expenses while boosting productivity.

Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process. Professional Services' Revenue Challenges Are Complex Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers' life cycles include the following: Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage.

Qlik, a data integration and analytics cloud platform announced yesterday that it has upgraded its AI assistant, Insight Advisor to provide customers with even more robust and targeted cloud analytics solutions. With Insight Advisor, users can expect search-based visual analysis (NLP-driven), conversational analytics (chat), and associative insights. These augmented analytics aim to expose hidden data relationships, assist with creation and data preparation, and integrate advanced calculations for customers. "Analytics users want to do more with their data, but often struggle with where to look or what next steps to take. Insight Advisor gives these users a complete and powerful AI assistant, built directly into Qlik Sense, to help guide them along every step of their data exploration and analysis journey," said James Fisher, chief product officer at Qlik. "Qlik Sense users are only a click or question away from the assistance needed to derive more insights and value from data. And, with every interaction, Insight Advisor learns alongside them, creating a virtuous cycle where they become smarter together, increasing users' data literacy and data usage."

Hybrid Computing, and thus Hybrid Analytics are concepts which are undergoing accelerated mutations, with the introduction of Edge and Fog Computing, in the wake of new mobility and IoT communication protocols, technologies and practices being phased in the Industry on a daily basis, 5G being its latest illustration. Our objective will be to shed some light on the various impacts, both positive and challenging, that these transformations impose on Cloud Analytics. This session will first address what these changes spell out for Cloud Analytics and in particular, what are the new considerations, key assets and enabling paradigms being introduced, both in terms of functional architectures and underlying infrastructures supporting the ingestion, distributed treatment and produced insights, in the cloud, in the fog, and at the edge, along with the unlocked potentials but also the pitfalls associated to them. As a part in these considerations, the session will address the intrinsic security, information privacy and data protection concerns, and the specific hybrid specificities which allow for new ways to compartment privacy and protect anonymity while maintaining the same descriptive and predictive capabilities. Unfortunately, we'll see that these new hybrid architectures can also harbor new combinations of vulnerabilities.

Findings show that data practitioners spend a majority (up to 80%1) of their time on data wrangling instead of mining data for analytics and machine learning projects. Organizations want to find trusted datasets so they gain visibility into workloads across data sources as well as their upstream and downstream impact. Take the first step towards successful cloud modernization with Databricks and Informatica. The partnership provides an end-to-end data discovery and lineage enabled by Informatica's AI-powered Enterprise Data Catalog that helps enterprises be highly strategic about data engineering with complete visibility into their data stack. Register now to see an in-depth demo of the Databricks and Informatica joint solution for data lineage.

Google Cloud Platform (GCP) is considered to be one of the Big 3 cloud platforms among Microsoft Azure and AWS. GCP is widely used cloud solutions supporting AI capabilities to design and develop smart models to turn your data into insights at a cheap, affordable cost. The following excerpt is taken from the book'Cloud Analytics with Google Cloud Platform' authored by Sanket Thodge. GCP offers many machine learning APIs, among which we take a look at the 3 most popular APIs: A powerful API from GCP! This enables the user to convert speech to text by using a neural network model.

On the sidelines of Money20/20, held in Amsterdam this week, bobsguide caught up with Nick Cook, head of regtech and advanced analytics at the UK's Financial Conduct Authority (FCA). All of the external facing regtech sits under my department. Internally, I'm leading our more advanced analytics, both the technology side - cloud analytics - as well as building out our human side - our data science capability. Over time, through training and development we can start to build it out and expand in an osmotic fashion across the wider organisation - effectively enabling us to leverage machine learning. We're also running sandboxes and hackathons to develop and encourage the regtech contingent of the UK startup market.

Anyone can look into a crystal ball, but it takes a visionary to translate vague details into useful information. Noted analyst, R "Ray" Wang is just such a visionary. As the founder and chairman of Silicon Valley-based Constellation Research, Wang advises companies on topics including how disruptive technologies impact the future of work, next generation customer experience, cross-channel commerce, big data and analytics, digital marketing transformation, and technology optimization. His latest book, Disrupting Digital Business, is required reading in many boardrooms. We spoke with Ray during the Oracle Cloud Days event in Denver to get his perspective on the role of cloud-based analytics in managing disruption and maximizing business opportunities.

Google jumped into the emerging space for analytics and big data when it revealed the new Cloud Machine Learning suite of services. "There's a new architecture emerging," Eric Schmidt, executive chairman of Google parent Alphabet, said at Google's GCP Next last week. "In a year, you will use machine learning to do something better than humans have been doing. Schmidt is not alone in that thinking. Google rivals Amazon, IBM, and Microsoft, in fact, have made similar cloud computing moves of late.