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Information Management

Introduction To Web Applications: Part 1


It is hardly surprising that web applications have seen such an impressive development over the course of the last approximately ten years. If one were to make a synthesis of the overall experience of desktop applications, there are a couple of valid arguments that we can be almost certain would appear. First and foremost, a piece of desktop software has to be manually retrieved (downloaded from the Internet or physically) and installed, which can present issues to the "non-technical" user. Needless to say that this process can bring, and mostly has, subsequent issues in regards to updating and/or patching the software, system requirements, etc. Also, cross-platform development efforts are needed in order to provide versions for the three major operating systems (macOS, Windows, Linux) if the target is to reach as large of an audience as possible. Desktop applications used to be bound to the machine in terms of the licensing as well, which further reduced the flexibility in approaching one's work. A further valid point has to do with the limited, often delayed, user feedback and how that can lead to the diminishing of testing scenarios. Of course no solution is constructed out of disadvantages alone and we are not dealing with such a case here, either: desktop applications tend to be faster and are generally considered more secure than their web counterparts. However, history has shown that while the web is not the perfect solution, its advantages were simply too powerful to ignore. Not only does a web application require no installation from the user, updates can be easily rolled out and made available to all users instantly after a new release.



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Master Data Management eats AI for breakfast, or does it?


In a widely circulated and discussed article on Forbes, Nallan Sriram, Global Technology Strategist of Unilever makes a compelling argument for the need for master data for AI initiatives in the enterprise. The article describes that master data gets siloed in operational systems like ERP with the key decision-makers realizing the need for correct master data when faced with revenue loss or increased operational expense. As master data provides context to business transactions, it is fundamental to business operations. In earlier times, we could manage master data through human intervention. But now with cloud data lakes and our aspirations to build predictive algorithms for business operations and operations, the need for clean, contextual and unified master data is all the more enhanced.

Features are the New Data


In my prior blog "Reframing Data Management: Data Management 2.0", I talked about the importance of transforming data management into a business strategy that supports the sharing, re-using and continuous refinement of the data and analytics assets to derive and drive new sources of customer, product, and operational value. If data is "the world's most valuable resource", then we must transform data management into an offensive, "data monetization" business strategy that proactively guides organizations in the application of their data to the business to drive quantifiable financial impact (Figure 1). In this blog I want to drill into the importance of Machine Learning (ML) "Features". Now, I have always found the term "features" a bit confusing. When we describe the "features" for a car, we talk about manufacturer, model, style, color, cruise control, lane departure warnings, blue tooth connectivity, and such.

How Artificial Intelligence Can Improve Your Pay-Per-Click Ad Efforts


If you want to achieve the highest level of success with your paid ads, be sure to utilize AI in your efforts. Doing so will pay off and help you get more bank for your paid advertisement dollar. Don't underestimate how beneficial AI and machine learning can be. Also, as it continues to evolve and grow, even more opportunities will arise. Keep this in mind to create campaigns that outrank the competition.

TechDay - How Artificial Intelligence Impacts Startups


You've probably already heard about how artificial intelligence (AI) is slowly but surely sweeping the business sector and making things easier for large companies. AI is constantly used to make cost-effective improvements, as well as to gain an advantage over the rest of the competition. That said, what about newer companies? Is AI something that only experienced industry leaders can utilize? It's a valid question to ask, as there are so many struggling startup owners out there looking for any advantage they can use in today's digital world. So how exactly does AI impact startups, and can you use it to your advantage to help expand your new business? A matter of informationThere's no denying that the amount of data available to a new company is staggering. There are many ways to track relevant metrics that it can be overwhelming to most first-time business owners. As a result, business management is learning about how best to harness the data. Such is the reason why analysts are in such high demand. Their ability to make good use of data makes them indispensable for most business owners. It’s also the reason why artificial intelligence is so successful. It can help streamline so many processes, including video annotation, workforce management, and so much more. Understanding AI and accessibilityOne of the reasons why AI is useful, despite being a form of emerging technology, is that it can help deal with human error. For example, a company can use AI transcription services to help with closed captions for their video content. Not only does it help gather the support of those who benefit from closed captions in videos, but it also offers a means for search algorithms to index content. Adding a transcript to videos is a form of search engine optimization (SEO), allowing even the most inexperienced company owner to benefit from robust marketing strategies. Those who are looking to make their company more accessible will naturally stand to benefit from what AI has to offer. It also helps that accessibility is one of the keys to a successful business venture. It’s easy to support a company that takes the time to be more accessible to its target demographic. It also has the added benefit of taking human error out of the picture. While it might take some time for mainstream AI to master accurate transcription, there are many services that offer accurate transcription services for an affordable price range. The subtle impact of customer serviceFor startups, it can be quite challenging to adopt a meaningful customer service system without outsourcing. It's quite similar to IT managed services, where it's best for newer companies to get the help of professionals to get the job done. The good news is that most types of customer service roles can be filled by artificial intelligence. In addition, there are many professional services available that offer a full system for customer service without effort. Quality customer service matters, which is why most newer companies are recommended to keep an active social media account no matter the situation. If users are happy with the customer service, it can go a long way to boosting a new company’s overall popularity. Creative and core tasksMany company owners have a hard time expanding their reach because creative and core tasks take a secondary role in keeping the company afloat. As a result, most company owners and their staff are stuck performing the most tedious tasks to get the job done, which doesn't free up time and resources to do anything else. The dawn of AI allows companies to use AI services for the most tedious processes, giving the creatives of your company the freedom to do what they do best. The future of AIOne of the most exciting parts of AI is that it’s still considered an emerging technology. What it means is everything artificial intelligence can do today is only the tip of the iceberg. There is so much more that AI could potentially accomplish, and it’s bound to improve as time goes by. Larger companies are typically best-equipped to handle AI, but there will come a time when even the smallest company can use quality AI services to deal with various issues. Artificial intelligence is a fascinating subject and one that undoubtedly impacts companies no matter their size. Even brand new companies have a chance at success with the help of emerging tech such as AI. All you have to do is look at what's trending to figure out the best tools for your business.

The Role of Professional Certifications in Computer Occupations

Communications of the ACM

Before presenting employer certification-demand findings, it is necessary to describe the methodology in order to assist in interpreting the results. The certification-demand analysis was performed using the Economic Modeling Specialists International (EMSI) dataset. To populate the dataset, EMSI combs through 100,000 websites, effectively capturing job listings for more than 1.5 million companies. The same job listings regularly appear on multiple websites. To reduce duplicates, EMSI uses a machine learning-based duplicate-detection process.

How smart contracts and AI could work together


It's a common refrain within IT teams: challenges with data management can inhibit business agility and slow AI-driven innovation to a crawl. Because as data grows and complexifies, proper data management becomes increasingly time-consuming and effort-intensive. This type of data conundrum is what keeps Data Scientists awake at night (and not just figuratively). In order for data management to be done right, it requires teams to aggregate data from multiple internal and external silos, reconcile inconsistencies and build on a clean repository of information, to create a "single source of truth" holy grail. Moreover, companies face ongoing challenges wrangling this disparate data for compliance and regulatory reporting. Smart contracts have emerged as one of the most efficient and effective ways to streamline data management.

Data Insights for Everyone -- The Semantic Layer to the Rescue


What is a semantic layer? That's a good question, but let's first explain semantics. The way that I explained it to my data science students years ago was like this. In the early days of web search engines, those engines were primarily keyword search engines. If you knew the right keywords to search and if the content providers also used the same keywords on their website, then you could type the words into your favorite search engine and find the content you needed.