MES: The path to intelligent manufacturing

The adoption of technology in the Manufacturing Industry has been slow, but steady. Technology adoption has been relatively faster when it has helped improve productivity, boost quality, and reduce costs. Industry CXOs are convinced that IT has a major role to play in manufacturing, but less than 30 percent of manufacturers have adopted Industry 4.0 technologies.[i] Now, with COVID-19 disrupting supply chains, manufacturers are being forced to examine virtualization and automation opportunities for their plants and MES in a bid to make them more resilient.

The problem is that many manufacturing organizations have created home-grown tools around Manufacturing Operations Management (MOM). These solutions cannot withstand the shock of COVID-19 type disruptions. They need to evolve into Smart Manufacturing systems. This is why, now is a good time to invest in full-fledged MES that leads to a connected, transparent, optimized and agile organization.

There are pockets in the manufacturing sector that appreciate the potential of MES. However, most are still improving their understanding of MES and how it can extract benefits across the supply chain.

At KONE, the Finnish engineering services organization known for its moving walkways and elevators, MES has been used as a starting point for its transformation to a digital factory. “MES is not only about tool implementation,” says Martin Navratil, Director, Manufacturing Network Development, who has been implementing MES at KONE for a few years now. “It is about the commitment of leadership and change management.”[ii] KONE embedded MES into its manufacturing strategy to access real time data while executing processes in their warehouse or during production on the shop floor. The availability of continuous data (during a shift, day-wise, week-wise, etc.) has improved efficiency and responsiveness to customer needs. Navratil says that MES has made an impact in four areas:

• Driving collaborative innovation: MES is the foundation to bring digital competencies into the organizations by synchronizing the processes, tools, materials, equipment and people on a global scale.
• Enabling a service mind set: MES connects geographically dispersed factories, putting an end to unsustainable and fragmented systems. The flexibility it provides supports a service mind set.
• Building customer centric solutions: MES minimizes the cycle time and improves responsiveness. It also provides data to continuously drive a Lean and Six Sigma culture to improve quality.
• Fast and smart execution: MES provides maximum transparency to customers regarding deliveries. Real time data is visually available to everyone, allowing the organization to put the customer at the center of the operations, reduce time to market and create customer trust.

MES places real time data into the hands of the organization, allowing it to become intelligent and exploit opportunities for faster improvement. Not only can supervisors monitor production during a shift (to achieve targets or improve asset utilization) but they can also transfer the granular data to other parts of the organization such as the maintenance and engineering teams for faster service response and to enable continuous improvement. The data also introduces great traceability, leading to excellence in delivery. “MES has brought many more opportunities to achieve better results,” observes Navratil.

For manufacturing organizations, MES is strategic to changing the way of working and to increase technological maturity – an essential pre-condition to the adoption of Industry 4.0 technologies. KONE provides an example of how MES can impact the organization and make it future ready.

[i] https://iot-analytics.com/industry-4-0-adoption-2020-who-is-ahead/

[ii] https://www.youtube.com/watch?v=BYmpRt9BtsA&list=PLAqtx75lIaRK5…

Co-Authored by:

Martin Navratil

Director, Manufacturing Network Development, KONE

Pareekh Jain

Founder and Lead Analyst, EIIR Trend

Nitin Kumar Kalothia

Associate Partner, Business Consulting Group

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Introduction to Probabilistic programming

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Distributed Artificial Intelligence with InterSystems IRIS

What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

Distributed AI scenarios “for the masses”

We will not be discussing edge computations, confidential data operators, scattered mobile searches, or similar fascinating yet not the most consciously and wide-applied (not at this moment) scenarios. We will be much “closer to life” if, for instance, we consider the following scenario (its detailed demo can and should be watched here): a company runs a production-level AI/ML solution, the quality of its functioning is being systematically checked by an external data scientist (i.e., an expert that is not an employee of the company). For a number of reasons, the company cannot grant the data scientist access to the solution but it can send him a sample of records from a required table following a schedule or a particular event (for example, termination of a training session for one or several models by the solution). With that we assume, that the data scientist owns some version of the AI/ML mechanisms already integrated in the production-level solution that the company is running – and it is likely that they are being developed, improved, and adapted to concrete use cases of that concrete company, by the data scientist himself. Deployment of those mechanisms into the running solution, monitoring of their functioning, and other lifecycle aspects are being handled by a data engineer (the company employee).

An example of deployment of a production-level AI/ML solution on InterSystems IRIS platform that works autonomously with a flow of data coming from equipment, was provided by us in this article. The same solution runs in the demo under the link provided in the above paragraph. You can build your own solution prototype on InterSystems IRIS using the content (free with no time limit) in our repo  Convergent Analytics (visit sections Links to Required Downloads and Root Resources).

Which “degree of distribution” of AI do we get via such scenario? In our opinion, in this scenario we are rather close to the ideal because the data scientist is “cut from” both the data (just a limited sample is transmitted – although crucial as of a point in time) and the algorithms of the company (data scientist’s own “specimens” are never in 100% sync with the “live” mechanisms deployed and running as part of the real-time production-level solution), he has no access at all to the company IT infrastructure. Therefore, the data scientist’s role resolves to a partial replay on his local computational resources of an episode of the company production-level AI/ML solution functioning, getting an estimate of the quality of that functioning at an acceptable confidence level – and returning a feedback to the company (formulated, in our concrete scenario, as “audit” results plus, maybe, an improved version of this or that AI/ML mechanism involved in the company solution).

Figure 1 Distributed AI scenario formulation

We know that feedback may not necessarily need to be formulated and transmitted during an AI artifact exchange by humans, this follows from publications about modern instruments and already existing experience around implementations of distributed AI. However, the strength of InterSystems IRIS platform is that it allows equally efficiently to develop and launch both “hybrid” (a tandem of a human and a machine) and fully automated AI use cases – so we will continue our analysis based on the above “hybrid” example, while leaving a possibility for the reader to elaborate on its full automation on their own.

How a concrete distributed AI scenario runs on InterSystems IRIS platform

The intro to our video with the scenario demo that is mentioned in the above section of this article gives a general overview of InterSystems IRIS as real-time AI/ML platform and explains its support of DevOps macromechanisms. In the demo, the “company-side” business process that handles regular transmission of training datasets to the external data scientist, is not covered explicitly – so we will start from a short coverage of that business process and its steps.

A major “engine” of the sender business processes is the while-loop (implemented using InterSystems IRIS visual business process composer that is based on the BPL notation interpreted by the platform), responsible for a systematic sending of training datasets to the external data scientist. The following actions are executed inside that “engine” (see the diagram, skip data consistency actions):

Figure 2 Main part of the “sender” business process

(a) Load Analyzer – loads the current set of records from the training dataset table into the business process and forms a dataframe in the Python session based on it. The call-action triggers an SQL query to InterSystems IRIS DBMS and a call to Python interface to transfer the SQL result to it so that the dataframe is formed;

(b) Analyzer 2 Azure – another call-action, triggers a call to Python interface to transfer it a set of Azure ML SDK for Python instructions to build required infrastructure in Azure and to deploy over that infrastructure the dataframe data formed in the previous action;

As a result of the above business process actions executed, we obtain a stored object (a .csv file) in Azure containing an export of the recent dataset used for model training by the production-level solution at the company:

Figure 3 “Arrival” of the training dataset to Azure ML

With that, the main part of the sender business process is over, but we need to execute one more action keeping in mind that any computation resources that we create in Azure ML are billable (see the diagram, skip data consistency actions):

Figure 4 Final part of the “sender” business process

(c) Resource Cleanup – triggers a call to Python interface to transfer it a set of Azure ML SDK for Python instructions to remove from Azure the computational infrastructure built in the previous action.

The data required for the data scientist has been transmitted (the dataset is now in Azure), so we can proceed with launching the “external” business process that would access the dataset, run at least one alternative model training (algorithmically, an alternative model is distinct from the model running as part of the production-level solution), and return to the data scientist the resulting model quality metrics plus visualizations permitting to formulate “audit findings” about the company production-level solution functioning efficiency.

Let us now take a look at the receiver business process: unlike its sender counterpart (runs among the other business processes comprising the autonomous AI/ML solution at the company), it does not require a while-loop, but it contains instead a sequence of actions related to training of alternative models in Azure ML and in IntegratedML (the accelerator for use of auto-ML frameworks from within InterSystems IRIS), and extracting the training results into InterSystems IRIS (the platform is also considered installed locally at the data scientist’s):

Figure 5 “Receiver” business process

(a) Import Python Modules – triggers a call to Python interface to transfer it a set of instructions to import Python modules that are required for further actions;

(b) Set AUDITOR Parameters – triggers a call to Python interface to transfer it a set of instructions to assign default values to the variables required for further actions;

(c) Audit with Azure ML – (we will be skipping any further reference to Python interface triggering) hands “audit assignment” to Azure ML;

(d) Interpret Azure ML – gets the data transmitted to Azure ML by the sender business process, into the local Python session together with the “audit” results by Azure ML (also, creates a visualization of the “audit” results in the Python session);

(e) Stream to IRIS – extracts the data transmitted to Azure ML by the sender business process, together with the “audit” results by Azure ML, from the local Python session into a business process variable in IRIS;

(f) Populate IRIS – writes the data transmitted to Azure ML by the sender business process, together with the “audit” results by Azure ML, from the business process variable in IRIS to a table in IRIS;

(g) Audit with IntegratedML – “audits” the data received from Azure ML, together with the “audit” results by Azure ML, written into IRIS in the previous action, using IntegratedML accelerator (in this particular case it handles H2O auto-ML framework);

(h) Query to Python – transfers the data and the “audit” results by IntegratedML into the Python session;

(i) Interpret IntegratedML – in the Python session, creates a visualization of the “audit” results by IntegratedML;

(j) Resource Cleanup – deletes from Azure the computational infrastructure created in the previous actions.

Figure 6 Visualization of Azure ML “audit” results

Figure 7 Visualization of IntegratedML “audit” results

How distributed AI is implemented in general on InterSystems IRIS platform

InterSystems IRIS platform distinguishes among three fundamental approaches to distributed AI implementation:

• Direct exchange of AI artifacts with their local and central handling based on the rules and algorithms defined by the user
• AI artifact handling delegated to specialized frameworks (for example: TensorFlow, PyTorch) with exchange orchestration and various preparatory steps configured on local and the central instances of InterSystems IRIS by the user
• Both AI artifact exchange and their handling done via cloud providers (Azure, AWS, GCP) with local and the central instances just sending input data to a cloud provider and receiving back the end result from it

Figure 8 Fundamental approaches to distributed AI implementation on InterSystems IRIS platform

These fundamental approaches can be used modified/combined: in particular, in the concrete scenario described in the previous section of this article (“audit”), the third, “cloud-centric”, approach is used with a split of the “auditor” part into a cloud portion and a local portion executed on the data scientist side (acting as a “central instance”).

Theoretical and applied elements that are adding up to the “distributed artificial intelligence” discipline right now in this reality that we are living, have not yet taken a “canonical form”, which creates a huge potential for implementation innovations. Our team of experts follows closely the evolution of distributed AI as a discipline, and constructs accelerators for its implementation on InterSystems IRIS platform. We would be glad to share our content and help everyone who finds useful the domain discussed here to start prototyping distributed AI mechanisms.

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Seeking Out the Future of Search

The future of search is the rise of intelligent data and documents.

Way back in 1991, Tim Berners-Lee, then a young English software developer working at CERN in Geneva, Switzerland, came up with an intriguing way of combining a communication protocol for retrieving content (HTTP) with a descriptive language for embedding such links into documents (HTML). Shortly thereafter, as more and more people began to create content on these new HTTP servers, it became necessary to be able to provide some kind of mechanism to find this content.

Simple lists of content links worked fine when you were dealing with a few hundred documents over a few dozen nodes, but the need to create a specialized index as the web grew led to the first automation of catalogs, and by extension led to the switch from statically retrieved content to dynamically generated content. In many respects, search was the first true application built on top of the nascent World Wide Web, and it is still one of the most fundamental.

Web Crawler, Infoseek, Yahoo, Altavista, Google, Bing, and so forth emerged over the course of the next decade as progressively more sophisticated “search engines”, Most were built on the same principle – a particular application known as a spider would retrieve a web page, then would read through the page to index specific terms. An index in this particular case is a look-up table, taking particular words or combinations of words as keys that were then associated with a given URL. When the term is indexed, the resulting link is then weighted based upon various factors that in turn determined the search ranking of that particular URL.

One useful way of thinking about an index is that it takes the results of very expensive computational operations and stores them so these operations need to be done infrequently. It is the digital equivalent of creating an index for a book, where specific topics or keywords are mentioned on certain pages, so that, rather than having to scan through the entire book, you can just go to one of the page numbers of the book to get to the section that talks about “search” as a topic.

There are issues with this particular process, however. The first is syntactical – there are variations of words that are used to specify different modalities of comprehension. For instance, you have verb tenses – “perceives”, “perceived”, “perceiving”, and so on – that indicate different forms of the word “perceive” based upon how they are used in a sentence. The process of identifying that these are all variations off of the same base is called stemming, and even the most rudimentary search engine does this as a matter of course.

A second issue is that phrases can change the meaning of a given word: Captain America is a superhero, Captain Crunch is a cereal. A linguist would properly say that both are in fact “characters”, and that most languages will omit qualifiers when context is known. Significantly Captain Crunch the character (who promotes the Captain Crunch Cereal) is a fictional man dressed in a dark blue and white uniform with red highlights. But then again, this also describes Captain America (and to make matters even more intriguing, the superhero also had his own cereal at one point).

Separated At Birth?

This ambiguity of semantics and reliance upon context has generally meant that, even if documents had an underlying structure that was consistent, that straight lexical search generally has an upper limit of relevance. Such relevance can be thought of as the degree to which the found content matches the expectation of what the searcher was specifically looking for.

This limitation is an important point to consider – straight keyword matching obviously has a higher degree of relevance than a purely random retrieval, but after a certain point, lexical searches must be able to provide a certain degree of contextual metadata. Moreover, search systems need to infer the contextual cloud of sought metadata that the user has in his or her head, usually by analysis of previous search queries made by that individual.

There are five different approaches to improving the relevance of such searches:

• Employ Semantics. Semantics can be thought of as a way to index “concepts” within a narrative structure, as well as a way of embedding non-narrative information into content. These embedded concepts provide ways of linking and tagging common conceptual threads, so that the same concept can link related works together. It also provides a way of linking non-narrative content (what’s typically thought of as data) so that it can be referenceable from within narrative content.
• Machine Learning Classification. Machine learning has become increasingly useful as a way of identifying associations that occur frequently in topics of a specific type, as well as providing the foundation for auto-summarization – building summary content automatically, using existing templates as guides.
• Text Analytics. This involves the use of statistical analysis tools for the building of concordances, for identifying Bayesian assemblages, and for TF-IDF Vectorization, among other uses.
• Natural Language Processing. This bridges the two approaches, using graphs constructed by partially indexed content in order to extract semantics while taking advantage of machine learning to winnow out spurious connections. Typically such NLP systems do require the development of corpuses or ontologies, though word embedding and similar machine language based tools such as Word2Vec for vectorization illustrate that the dividing line between text analytics and NLP is decreasing.
• Markup Utilization. Finally, most contemporary documents contain some kind of underlying XML representation. Most major office software shifted to zipped-XML content in the late 2000s, and a significant amount of content processing systems today take advantage of this to perform structural lexical analysis.

Arguably, much of the focus in the 2010s tended to be on data manipulation (and speech recognition) at the expense of document manipulation, but the market is ripe for a re-focusing on document and semi-conversational structures such as meeting notes and transcripts that cross the chasm between formal documents and pure data structures, especially in light of the rise of screen mediated meetings and conferencing. The exact nature of this renaissance is still somewhat unclear, but it likely will involve unifying the arenas of XML, JSON, RDF (for Semantics), and machine-learning mediated technologies in conjunction with transformational pipelines (a successor to both XSLT 3.0 and OWL 2).

What does this mean in practice? Auto-transcription of speech content, visual identification of video content, and increasingly automated pipelines for doing both dynamically generated markup and semantification make most forms of media content more self-aware and contextually richer, significantly reducing (or in many cases eliminating outright) the overhead of manual curation of content. Tomorrow’s search engines will be able to identify not only the content that most closely matches based upon keywords, but will even be able to identify the part in a video or the location in a meeting where a superhero appeared or an agreement was made.

Combine this with event-driven process automation. When data has associated metadata, not just in terms of features or properties but in terms of conceptual context, that data can ascertain how best to present itself, without the specific need for costly dashboards or similar programming exercises, can check itself for internal consistency, and can even establish the best mechanisms for building dynamic user interfaces for pulling in new data when needed.

In other words, we are moving beyond search, where search can then be seen primarily as the way that you frame the type of information you seek, and the output then taking the resulting data and making it available in the most appropriate form possible. In general, the conceptual difficulties usually come down to ascertaining the contexts for all concerned, something we are getting better at doing.

Kurt Cagle is the Community Editor of Data Science Central, and has been an information architect, author and programmer for more than thirty years.

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The Role of Technology in Fostering E-commerce Business Growth

“E-commerce industries have originated from technology, and any innovation that knocks here solely belongs to technology.”

The retail sector has gone through tremendous changes in the last ten years. We have also seen a significant hike in the growth of e-commerce industries. The industry has recorded humongous sales figures and increased demand. 

According to the e-commerce development stats, worldwide e-commerce sales had reached 4.1 trillion U.S. dollars worldwide in 2020, and it will continue this whooping growth with $5 trillion in 2022.

We have to say that that e-commerce potential is undeniable. The industry has helped many businesses as well as the country in boosting economies. Its applications are diverse and encapsulate almost every business and sector.

The advent of advanced technologies has further strengthened the roots of e-commerce companies in this digital market. No matter how far we go, the role of technology in e-commerce, will always remain an indispensable part.

Let’s see how this technology is fostering e-commerce growth in society. 

#1. Artificial Intelligence & E-commerce

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Artificial Intelligence is a buzzing technology of today’s digital age. In e-commerce, it has a significant role as it provides valuable marketing insights into customer preferences. It guides them into creating better marketing campaigns for business.

This e-commerce technology also offers the automation and transfer of data management operations to boost performance. In the e-commerce world, retailers rely on AI for various unique business aspects.

#2. Personalized User Experience with Technology

Around 74% of businesses believe that user experience is essential for growing sales and conversions. AI provides a personalized user experience that 59% of customers say impacts their shopping decisions. It can facilitate a shopping experience that supports customers’ personal preferences. 

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Big data, machine learning services, and artificial intelligence can offer analytics and foresight into customer behavior patterns. It can drive advertising campaigns, provide support and services, plus automate communication, increasing businesses’ engagement rates. 

#3. Technology for Sending Customer Recommendations 

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As we know that AI can predict customer behavior patterns, it can recommend essential and helpful information to customers on products, products, and more. The technology’s algorithms efficiently forecast this information by reviewing customer’s search history and other third-party data. It leads to a practical proposal of information and solution to satisfy customer’s requirements.

#4. Automated Campaign Management

Steps For Implementing the Successful Marketing Automation Software

Customer behavior patterns are the driving force of every marketing campaign. With artificial intelligence, online retailers can target potential and existing customers by studying data such as past specific history. They can use these analytics to provide a better-aimed business content marketing strategy. 

After this, they may prepare engaging content and advertisements to target audiences and post them on the correct media platform to capture their attention. With AI and marketing automation, marketers can create a strategic and tactical campaign using customer data insights.

#5. The Cloud Technology in eCommerce

There is hardly any business today that doesn’t have at least one aspect of its operations on the cloud. Data management and processing in the cloud is essential for others to access it instantly on their device.

Especially for e-commerce businesses, a cloud ERP can increase delivery speeds, make your e-store more flexible, and bring business stability and growth both.

#6. Chatbots For eCommerce

Chatbots are known for their wide-scale availability and high customer satisfaction rate. This advanced technology has pivoted itself as virtual call center agents, and now it’s a part of every e-commerce website and mobile app. 

According to eCommerce chatbot statistics, 70% of e-commerce retailers will integrate a chatbot into their website by 2023.

Rather than providing information to customers through the phone, better give space to chatbots in your e-commerce website. It can provide a variety of services and solutions of optimal quality. 

#7. 24/7 Assistance with Technology

In my view,  the key secret of a successful e-commerce business is to handle customer-queries in real-time. I know it’s not possible for a human agent, but a virtual chat agent, i.e., chatbot, can do this better. 

Hardly any business is there that can answer customer’s questions in real-time.  Human agents are often unable to sort their problems, and when you put them on hold, it makes them more frustrated. It’s impossible to handle high customer volume with just the limited number of staff. 

But chatbots are available 24/7 to provide any answers and solutions that a potential customer can inquire about. This automated communication is valuable for e-commerce businesses. It frees up workers and lets them focus on other business operations, efficiently communicates with shoppers, and may even propose services and products.

#8. Voice Assistants For eCommerce

From m-commerce to e-commerce, now we have stepped into voice commerce. Not all people browse your site for products and services on their devices. You have to accommodate these potential customers too, and for this, start employing virtual voice assistants like Siri, the Amazon Echo, and Google Home. 

These will not even charge you anything and are getting increasingly popular day by day. The freedom and convenience that it offers are incredible and well-enough to retain customers and keep them encouraged. 

By deploying voice recognition technology, customers will use voice commands to find and purchase products online. For a successful business, retailers leverage this technology of e-commerce  and its benefits to capture consumers’ new wave.

#9. Assistive Technology for E-commerce

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In the digital marketing world, assistive technology and voice commerce help reach a wide variety of new audiences — not just the younger generation who use advanced devices but also the visually impaired people.

WIth speech-to-text technology, the visually impaired can forgo traditional search experience struggles and order what they need through new and developing assistive technology.

All interconnected, AI, voice assistants, and chatbots are becoming critical for any successful e-commerce business. Businesses must adapt to these new technologies to stay with the times, which appeals to potential and existing customers.

#10. Audio Brand Signatures for Business

Any company music composition, jingle, or auditory tone is considered an audio brand signature. It’s a great way to establish a brand identity in the market and let customers remember the brand’s name for a longer time.

Businesses can set their audio brand signature to play through voice assistants and let their customers know where they are shopping. By associating with an auditory signature, customers will know and remember calling from your store – even when laying on their couch, speaking to a voice assistant.

Final Thoughts

The role of technology in the e-commerce industry is inevitable and seamless. It’s the origin point, and innovation occurring every day is impacting the whole industry. No matter we are proceeding to the robotic world, e-commerce will not go anywhere and keep on thriving with technological transformations.

But it doesn’t mean retailers will not do anything. They have to participate in it actively and introduce new advancements to their store. It will guarantee your e-commerce success in the competitive digital world.

For better guidance and assistance, you can also reach out to India’s top ecommerce development company. They will sort your problem out and help you in running a successful e-commerce business.  

Good Luck!

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10 Web Development Trends That You Can’t Skip Reading

The importance of web development trends in everyday life

Technology is rapidly evolving in today’s world, and if you want to take full advantage of its potential, you need to keep up with the latest technological trends.

As we all know, the internet is present in almost every aspect of our lives, from ordering pizza to booking flights. The internet is behind it all, and creating an engaging digital experience is a real challenge.

To create an attractive website with a good strategy, you need to be aware of the latest trends in web development technology. Create a niche among the 1.8 billion websites that are still competing to attract your target audience.

Top 10 web development technology trends for 2021

| Progressive Web Applications (PWA)

Today, progressive web apps are being discussed by many businesses as they offer a plethora of benefits such as push notifications, the ability to work offline, provide a user experience similar to native apps. Progressive web apps are much easier to download and offer the advantages of native apps, such as excellent responsiveness and fast loading.

Leading companies such as Twitter, Starbucks, Forbes, and Uber use PWAs to respond to their customers faster. Some companies reported that with the introduction of PWAs, their users spent 40% more time on PWAs than on previous mobile sites.

Thus, we will see more spikes in PWA growth in 2021, and this trend will continue in the future.

| Artificial Intelligence (AI)

AI has already infiltrated our daily lives, often in conscious and unconscious ways, and in recent years AI has become a trend. AI has some excellent capabilities that many companies are looking for, such as processing and personalizing large amounts of data and presenting relevant or exciting information to the target audience.

AI collects essential information, such as the most popular pages, the number of visits to a website, the user’s search history, and stores to make more accurate and relevant recommendations.

AI helps shape the development of websites better, and with features like chatbots and voice interaction on offer, we’re sure that AI will become even more fashionable in the days to come.

| Dark mode or low light UX

Dark mode or low light UX is another trend to look out for in 2021 due to the increasing demand for such features and innovative web design.

Dark mode has already been introducing on some websites and activate. Many sites offer a simple toggle button, while some users will need to access the settings section.

 The leading reasons for the growing popularity of dark mode are that it saves power on OLED or AMOLED displays and minimizes the strain on the user’s eyes. However, no one can deny that using a dark mode on a website can make it look more attractive and relaxed for the user, resulting in a better user experience. Moreover, end-users are bound to keep it as a trend in the future.

| The Internet of Things (IoT)

IoT is one of the most popular web development trends that we see today, and IoT is making its presence felt in many things around us, such as smartwatches and personal assistants.

The IoT consists of sensors interconnected with other computing devices, which process the data collected from the sensors and further transfer the data through cloud networks to have no latency.

IoT is experiencing a steady growth in web development due to the high level of security of all data-related processes, accurate results, and the creation of dynamic and interactive UI experiences. 2025 expects to see around 60 billion IoT devices.

| Optimizing voice search

Since its inception, voice search technology has become popular everywhere, not just in web development. With the increase in IoT devices, people can now communicate using voice prompts instead of pressing buttons.

The popularity of voice search technology can also see the many examples, such as Google Assistant, Microsoft’s Cortana, and Apple’s Siri, that work primarily with voice search technology and significantly improve the user experience. Voice search features can work wonders if they are correctly optimized. By redesigning their websites based on voice search, companies that adopt it can expect to see a 30% increase in digital revenue.

| Single Page Applications (SPA)

A single page application is a type of web application loaded as a single page and runs within the browser; these SPAs do not require the page to reload at runtime.

The main advantage that SPAs offer is that, unlike traditional applications, they reduce the need to wait for pages to reload, making them more suitable for slow internet connections.

Other benefits offered by SPAs include simplicity of development, reusability of back-end code, ease of debugging, local data caching, and offline usability.

With major global companies such as Uber, Facebook, and Google already adopting single-page apps, it is safe to say that the trend for single-page apps has begun and will continue.

| Single-Page Website (SPW)

A single-Page Website is a concept that, as the name suggests, aims to provide a single-page website with no additional pages of services or information, etc. SPWs provide users with an intuitive user journey through a neat and comprehensive layout.

Compared to a multi-page site, SPWs make it easier to keep all the essential information to site visitors in one place, thus capturing their attention.

In the process, you can check the flow of information and put specific info in front of each user. Single-page websites are simple to optimize for mobile devices. Even in development, the time and costs are reduced, investing in these web technologies a beneficial proposition for both users and businesses.

| AMP (Accelerated Mobile Pages)

The idea behind creating AMP (Accelerated Mobile Pages) was to develop swift pages for mobile devices. These mobile accelerated pages are handy for sites with high traffic volumes, such as websites.

AMP pages show to work well on mobile search engine results pages, e-commerce sites, news sites, and other websites. AMP has shown to work.

AMP is a project jointly developed by Twitter and Google, a kind of open-source library for creating websites, web pages, and web applications with very lightweight, fast-loading, so-called “diet HTML.”

| Motion UI

A different bent to watch for in 2021 is “Motion UI,” a technology used to develop animation websites. Animations, graphics, and transitions play an important role in creating attractive websites and applications, and so does Motion UI as a current trend in web design.

Motion UI allows web developers to create web pages with minimalist design without working with JavaScript and jQuery. Leveraging Motion UI technology can increase user engagement, improve user experience and ultimately increase your business profitability.

| Advanced chatbots

In recent years, the number of chatbots integrated into websites has never been higher. With the rise of artificial intelligence and increasing demand for automated communication solutions, chatbots are here to wait and play an essential role in web development.

Chatbots are software built to handle and simulate conversations between people and can themselves suggest, answer and provide intelligent solutions to common questions.

These features make chatbots very popular because they can speed up the problem-solving process, eliminating companies’ need to hire multiple customer service specialists. Therefore, chatbots will continue to be a trend in web development.

| Conclusions

From 2021 onwards, the demand for the above technologies in web development will increase dramatically due to the global pandemic and other factors in 2020. In 2020, many companies had learned to operate remotely, demonstrating that they are challenging the previously only spoken technology’s unexplored potential. We’ve seen it happen.

For businesses, online presence is no longer just an option but a necessity. By taking advantage of these trending technologies, companies can not only survive but thrive by providing their users with a superior user experience. It’s all about web and web development trends in today’s world if you want to have a global presence you can take a help from top web development companies in India.

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What is Good Data and Where Do You Find It?

• Bad data is worse than no data at all.
• What is “good” data and where do you find it?
• Best practices for data analysis.

There’s no such thing as perfect data, but there are several factors that qualify data as good [1]:

• It’s readable and well-documented,
• It’s readily available. For example, it’s accessible through a trusted digital repository.
• The data is tidy and re-usable by others with a focus on ease of (re-)executability and reliance on deterministically obtained results [2].

Following a few best practices will ensure that any data you collect and analyze will be as good as it gets.

1. Collect Data Carefully

Good data sets will come with flaws, and these flaws should be readily apparent. For example, an honest data set will have any errors or limitations clearly noted. However, it’s really up to you, the analyst, to make an informed decision about the quality of data once you have it in hand. Use the same due diligence you would take in making a major purchase: once you’ve found your “perfect” data set, perform more web-searches with the goal of uncovering any flaws.

Some key questions to consider [3] :

• Where did the numbers come from? What do they mean?
• How was the data collected?
• Is the data current?
• How accurate is the data?

Three great sources to collect data from

US Census Bureau

U.S. Census Bureau data is available to anyone for free. To download a CSV file:

• Go to data.census.gov[4]
• Search for the topic you’re interested in. 
• Select the “Download” button.

The wide range of good data held by the Census Bureau is staggering. For example, I typed “Institutional” to bring up the population in institutional facilities by sex and age, while data scientist Emily Kubiceka used U.S. Census Bureau data to compare hearing and deaf Americans [5].

Data.gov

Data.gov [6] contains data from many different US government agencies including climate, food safety, and government budgets. There’s a staggering amount of information to be gleaned. As an example, I found 40,261 datasets  for “covid-19” including:

• Louisville Metro Government estimated expenditures related to COVID-19. 
• State of Connecticut statistics for Connecticut correctional facilities.
• Locations offering COVID-19 testing in Chicago.

Kaggle

Kaggle [7] is a huge repository for public and private data. It’s where you’ll find data from The University of California, Irvine’s Machine Learning Repository, data on the Zika virus outbreak, and even data on people attempting to buy firearms.  Unlike the government websites listed above, you’ll need to check the license information for re-use of a particular dataset. Plus, not all data sets are wholly reliable: check your sources carefully before use.

2. Analyze with Care

So, you’ve found the ideal data set, and you’ve checked it to make sure it’s not riddled with flaws. Your analysis is going to be passed along to many people, most (or all) of whom aren’t mind readers. They may not know what steps you took in analyzing your data, so make sure your steps are clear with the following best practices [3]:

• Don’t use X, Y or Z for variable names or units. Do use descriptive names like “2020 prison population” or “Number of ice creams sold.”
• Don’t guess which models fit. Do perform exploratory data analysis, check residuals, and validate your results with out-of-sample testing when possible.
• Don’t create visual puzzles. Do create well-scaled and well-labeled graphs with appropriate titles and labels. Other tips [8]: Use readable fonts, small and neat legends and avoid overlapping text.
• Don’t assume that regression is a magic tool. Do test for linearity and normality, transforming variables if necessary.
• Don’t pass on a model unless you know exactly what it means. Do be prepared to explain the logic behind the model, including any assumptions made.  
• Don’t leave out uncertainty. Do report your standard errors and confidence intervals.
• Don’t delete your modeling scratch paper. Do leave a paper trail, like annotated files, for others to follow. Your predecessor (when you’ve moved along to better pastures) will thank you.

3. Don’t be the weak link in the chain

Bad data doesn’t appear from nowhere. That data set you started with was created by someone, possibly several people, in several different stages. If they too have followed these best practices, then the result will be a helpful piece of data analysis. But if you introduce error, and fail to account for it, those errors are going to be compounded as the data gets passed along. 

References

Data set image: Pro8055, CC BY-SA 4.0 via Wikimedia Commons

[1] Message of the day

[2] Learning from reproducing computational results: introducing three …

[3] How to avoid trouble:  principles of good data analysis

 [4] United States Census Bureau

[5] Better data lead to better forecasts

[6] Data.gov

[7] Kaggle

[8]Twenty rules for good graphics

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