Blockchain is not middleware meant to tie into existing legacy systems, but there are ways of automating the flow of data from ERP systems to a distributed ledger technology. Typically, APIs and data-sharing standards, such as GS1 (best known for the machine-readable barcode protocol), have been used to enable interoperability with legacy data systems. The IBM Food Trust, which is used by Walmart and other big box retailers to track food from farm to shelf, avoids manual data input by leveraging legacy tech investments through the GS1 standard; it automates the transfer and understanding of data between different parties on the electronic ledger. Regardless of how blockchain is implemented, most of the cost and legwork for rolling it out requires business partner participation in the network and involves hammering out business agreements and governance rules, said Kevin McMahon, director of emerging technologies at Chicago-based consultancy SPR. "Putting together the governance model and putting in the effort, time and energy building out a consortium as well as solving business challenges — that's always been the surprise for our clients," McMahon said.
Companies and governments are rushing to embrace and integrate AI. Leading AI advocates such as Andrew Ng are encouraging companies to jump into AI use sooner rather than later. Research suggests that companies that fall behind in AI adoption might not ever catch up. Northeastern University professor Nada Sanders said recently that “organizations that take a measured and piecemeal approach to implementing emerging technologies will fall off the map, fade into irrelevance.” A recent op-ed argues that nations should be doubling down on AI research and development to remain competitive. It’s definitely a global race to see who will dominate with AI. Mark Cuban has famously said that the world's first trillionaires are going to come from somebody who masters AI and all its derivatives and applies it in ways we never thought of. All this change and the value it is creating is being driven by “narrow” or “weak AI,” algorithms that are incredibly proficient at a single task. Impressive as these algorithms are for discovering new drugs, forecasting volcanic eruptionsand even for deploying personalized meditations but they cannot share insights across information domains.
Make no mistake. Shadow IT as a challenge remains. The toughness of that challenge does remain—in fact, it has grown. Gartner estimates that 40-50% of cloud and enterprise application consumption is already happening over uncontrolled and unaccounted for sources, as businesses can no longer rely on slow procurement processes from Central IT. By 2020, half of all IT spending at large enterprises with digital business aspirations will occur at the business-unit level, it says. Also, a 2017 survey by NTT Communications found 83% IT professionals reporting that employees stored company data on unsanctioned cloud services. This suggests how the increase in cloud adoption and prevalence of SaaS and mobile applications, have further facilitated the rise of shadow IT. With Internet of Things (IoT) and other emerging technologies already underway, analysts believe this to be an even starker reality. While shadow IT is used usually without ill-intent, owing to either negligence or for the sake of convenience, it poses a serious threat to data security. In most cases companies are unaware of their use and hence do not know whether their data comes from secured sources or not.
Microservices create lots of small, distributed single-purpose services, with each service owning its own data. This service and data coupling support the notion of a bounded context and a shared-nothing architecture. Each service and its corresponding data compartmentalize and are completely independent of all other services. The data-driven migration antipattern occurs when you are migrating from a monolithic application to a Microservices architecture. Anti-pattern because of the migration for both the service functionality and the corresponding data together at the start while creating Microservices. There are two primary goals during any Microservices conversion effort. The first is to split the functionality of the monolithic application into small, single-purpose services. The second is to migrate the monolithic data into small databases owned by each service. The important aspect of developing Microservices rather than a monolithic application is inter-service communication. There are two communication styles i.e. synchronous vs asynchronous, one-to-one vs one-to-many mechanisms.
Working in the gig economy works both for small businesses and startups, and large enterprises and public sector organisations. Yorkshire Water is one of the businesses mentioned in the TopCoders report. The water utility firm opened up 12 months of its data through the Leeds Open Data Institute to crowd-source the discovery of new trends or patterns. According to Yorkshire Water, it received a number of interesting submissions, such as an app proposal to use artificial intelligence (AI) to automate the recognition of leak noise, and a Fitbit-like device for monitoring water usage in household water pipes. New research has found that crowd-sourcing ideas for the smart use of public sector data offers a huge economic benefit. In July, the European Union (EU) reported that the total direct economic value of the data held in the public sector is expected to increase from a baseline of €52bn in 2018 to €194bn in 2030.
It is also hard to define the market size for CI/CD since most surveys do not measure the depth of adoption. Just because a CI/CD tool is used within a company does not mean it is widely used, nor that its use cases have gone beyond the most basic. A better metric is what percentage of processes are automated Git commit to code to production. A DevOps focused survey from Codefresh reported that a third of companies had automated more than half of their workloads, but only 1% were all the way there. Another way to think about the issue is in terms of the percentage of developers at a company that use a particular product or service. The relevance of measuring CI/CD adoption came up in a recent twitter conversation, in which GitLab CEO Sid Sijbrandijg said about half of the Global 2000 companies have use CI/CD best practices like feature flags and tracing, but that only about 1% of workloads are being handled through this way.
Augmented data management uses machine learning and AI to make enterprise data management disciplines, such as data quality and integration, metadata management, master data management, and database management systems, "self-configuring and self-tuning," according to Gartner. Gartner included augmented data management in its recent list of top 10 data and analytics trends for 2019.Augmented data management is already starting to change how data professionals prepare and govern data with the help of more advanced machine learning capabilities and AI-driven automation, experts said. "Augmented data management will be an important enabler to faster, more scalable, more intelligent and higher quality augmented business decisions," said Bill Hostmann, research fellow at Dresner Advisory Services. David Menninger, an analyst at Ventana Research in Bend, Ore., said he sees augmented data management as part of a larger trend toward augmented software applications of all types, including analytics, which tends to get more attention.
The recommendation stands not only for Microsoft accounts but also for any other profile, on any other website or online service. If the service provider supports multi-factor authentication, Microsoft recommends using it, regardless if it's something as simple as SMS-based one-time passwords, or advanced biometrics solutions. "Based on our studies, your account is more than 99.9% less likely to be compromised if you use MFA," said Alex Weinert, Group Program Manager for Identity Security and Protection at Microsoft. Weinert said that old advice like "never use a password that has ever been seen in a breach" or "use really long passwords" doesn't really help. He should know. Weinert was one of the Microsoft engineers who worked to ban passwords that became part of public breach lists from Microsoft's Account and Azure AD systems back in 2016. As a result of his work, Microsoft users who were using or tried to use a password that was leaked in a previous data breach were told to change their credentials. But Weinert said that despite blocking leaked credentials or simplistic passwords, hackers continued to compromise Microsoft accounts in the following years.
The mainstreaming of AI business writing began with Google Smart Reply four years ago. Google Inbox users were offered a few colorless options for a reply to most emails. The feature still exists in Gmail, and with a single click you can respond with “Thanks!” or “I’ll send it to you” or “Let’s do Friday!” Last year Google added Smart Compose, which finishes the sentences you start. You can choose Google’s words by pressing the tab key. Using Smart Reply and Smart Compose saves time but makes replies dull. They’re dull because Google makes sure the replies are generic and designed to not annoy or offend anyone (for example, Google’s AI never uses gendered pronouns like “he” or “she”), and also because millions of other Gmail users are using the exact same wording for their replies. We all sound the same in our replies. Google is not alone. Lightkey makes a Windows application that works like Google’s Smart Compose. Quillbot is a cloud-based tool that can rephrase what you write (or what you copy and paste from others’ writing). It typically produces awkward prose. Machines have no ear for language.
While CIOs might be in a position of power, their success will depend on how they are developing the right blend of technical, business and influencing skills within their organization. The spotlight is therefore on the CIO’s expertise in solving these problems at hand. A study by MIT’s Center for Information Systems Research (CISR) brings to light that companies with experienced technologists on their board outperform others in areas such as revenue growth, return on assets and market capitalization growth. In other words, the significant contribution that CIO/CTO’s can bring to table gets reflected in the company’s financial outcomes. The analysis shows that out of 1,200 large enterprises with revenues over USD 1 billion, about 24% had board members that were classified as technology experts. These board members included those with experience as a CIO/CTO and expertise in software, digital platforms, big data and innovation, besides substantial years of leadership skills. According to the study, “Revenue growth over three years for boards with three or more such directors was 17.6% compared with 12.8% for boards without technology experts...."
Quote for the day:
"All leadership takes place through the communication of ideas to the minds of others." -- Charles Cooley