As the COVID-19 pandemic continues unfolding, technology solutions and government initiatives are multiplying to help monitor and control the virus’s journey. Their aid includes reducing the load on the health system and reinforcing the efforts of overworking and burned-out healthcare workers.
While smart technologies cannot replace or compensate public institution measures, they do play a crucial role in emergency responses. Let’s take a look at the promising use cases of how technology can help fight the novel coronavirus outbreak.
Technologies Used for Good
People tend to think of technology as a heartless machine, which is true, but only until it’s used for good. Just look at all the wonderful things we’ve managed to do with its help.
Telemedicine is gaining traction by offering remote patient monitoring and interactive remote doctor’s visits. At the same time, 3D printing and open-source solutions are facilitating the production of more affordable face masks, ventilators, and breathing filters as well as optimizing the supply of the medical equipment. Even more, the pandemic has driven scientists to desperate measures. They are now experimenting with gene editing, synthetic biology, and nanotechnology to develop and test vaccines faster than ever in the history of humanity.
Smart technologies like the Internet of things (IoT), big data, and artificial intelligence (AI) are being massively adopted to help track the disease spread and contagion, manage insurance payments, uphold medical supply chains, and enforce restrictive measures. Let’s go step by step to see how IoT, AI, big data, and mobile solutions are actually enhancing medical care.
IoT for Smart Patient Care Management and Home Automation
IoT has already found its use among healthcare providers. Today, connected patient imaging, health devices or applications, worker solutions, and ambulance programs are being adopted globally. But COVID-19 made the technology take on new applications to help the world combat the epidemic. Tracking quarantine, pre-screening and diagnosing, cleaning and disinfecting, innovative usage of drones, reducing in-home infections, are all “new normals” thanks to IoT.
For example, an American health technology company Kinsa creates smart thermometers that screen and aggregate people’s temperature and symptoms data in real-time. Having gathered data from over one million connected thermometers, Kinsa rolled out its US HealthWeather™ Map.
The map is updated daily, highlighting how severely the population is being affected by influenza-like illness (ILI). This real-time information helps health authorities see an increase. In fevers as early indicators of the community spread of COVID-19 to streamline the allocation of health resources. These areas are marked in the “Atypical” mode of the map.
To slow down the spread of COVID-19, a team of Seattle engineers created Immutouch, a smart wristband vibrating every time a person wearing it tries to touch their face.
Smart speakers, lights, and security systems are being used to open doors and switch on lights to reduce in-home infections. These gadgets allow people to avoid touching the surfaces of doorknobs, switches, mail, packages, or anything that could easily spread germs.
The Role of Big Data in Fighting Coronavirus
Tapping into big data is a must to develop real-time forecasts and arm healthcare professionals with a profound database to help with decision-making.
IBM Clinical Development system is an advanced Electronic Data Capture (EDC) platform that allows an accelerated delivery of medications to market and reduces the time and cost of clinical trials thanks to cognitive computing, patient data assets, and IoT. Additionally, the U.S. government had been in active talks with Facebook, Google, and others to determine how to use location data to glean insights for combating the COVID-19 pandemic.
Could Mobile Apps be Used to Control the Pandemic?
The COVID-19 pandemic has become a game-changer for the healthcare continuum. Today’s mobile apps are on guard to help patients receive online therapy, at-home testing, conclude self-checks, and improve mental well-being. Thanks to smartphone apps, it is now possible to trace the virus’s journey and help limit its spread.
Apple COVID-19, for instance, was created in partnership with the Centers for Disease Control and Prevention (CDC), the White House, and the Federal Emergency Management Agency (FEMA). The application contains vital and relevant information from trusted sources on the coronavirus pandemic: hand hygiene practices, social distancing FAQs, quarantine guidelines, self-checking tutorials, tips on cleaning, and disinfecting surfaces. On top of that, it has a screening tool that advises people on what to do when a person has COVID-19 symptoms, has just returned from abroad, or has come in close contact with someone who might be infected with the disease.
Meanwhile, health authorities in Abu Dhabi have created the TraceCovid app for Bluetooth-enabled smartphones to minimize the spread of the disease. The service allows tracing individuals who have come into proximity with a person tested positive for COVID-19. Thanks to it, medical professionals сan react faster and render the necessary healthcare. Germany, in turn, is going to roll out a smartphone app, which will use Bluetooth to alert people if they are close to someone with the confirmed viral infection.
Telemedicine has also proved to be an efficient tool for flattening the curve. The Sheba Medical Centre, the largest Israeli hospital, launched a telehealth program for remote patient-monitoring to control the pandemic spread. Doctolib, a Franco-German company, Qare (France), Livi (Sweden), Push Doctor (the UK), Compugroup Medical (Germany) are offering virtual doctors too.
Using AI to Identify, Track and Forecast Outbreaks
Artificial intelligence-powered by natural language processing (NLP) and location monitoring is crucial for identifying, tracking, and scanning outbreaks, predicting hotspots and helping make better decisions.
For example, Microsoft collaborated with the U.S. Centers for Disease Control and Prevention (CDC) to create an AI-based COVID-19 Assessment bot to treat patients more effectively and allocate limited resources. The bot, nicknamed Clara, can evaluate symptoms, advice on the next steps to take and track users who need urgent care the most.
The Canadian startup BlueDot has applied AI to spot and track the spread of COVID-19 and predict outbreaks, and the Japanese company Bespoke rolled out Bebot, an AI-powered chatbot that was developed specifically for travelers. This mobile app informs and assists them with coronavirus-related questions as they move about.
There’s no doubt that the coronavirus pandemic has become a real-life test for everyone. It has caused tremendous damage, but at the same time, it has forced tech innovators to roll out advanced solutions, and it seems that they don’t plan on slowing down anytime soon.
Healthcare providers across the globe are continually switching to smart technologies. So if you are in the smart technology niche, consider the current trends to steer your business in the right direction.
Join Hands with Instagram’s New Algorithm to Boost Your Business’s User Engagement
👉 Most people are not at all happy with the latest 📸 Instagram algorithm. However, if you want to make the most of your business, you should understand how it works. The trick here is you must work with its algorithm and not go against it here; it is easy for your know-how. 👇🔻
🚩This post will guide you on how the 🆕 new Instagram algorithm works and how you can use it for the advantage of your business-
How does the new Instagram algorithm work?
The new 📸 Instagram ♀️ algorithm is a mystery to many users on the platform. It no longer functions at a slow pace as it did in the past. To score high on Instagram today, you need to be extremely diligent as a business owner. The algorithm needs to be fully optimized so that you get success in the platform. So, read and remember the following factors to help you determine how your posts can perform well to boost user engagement and sales revenue for your business with success-
⦁ The volume of social engagement you receive -: Note, your posts with the maximum number of shares, views, comments, etc., will rank high in the Instagram feed over others. When your post receives a lot of comments and likes, the Instagram platform gets a signal that it is engaging to users and has high-quality content. It wants more users to see it, and the algorithm on the platform will display it to other users online.
However, here again, there is a catch. It is not only the engagement that Instagram considers now; it is how fast your Instagram post engages the readers in some cases. Trending hashtags on Instagram, for instance, is the best-known example of the above. The volume of engagement you get for your business posts is important; however, how quickly you get this engagement is even more important!
👉▶Tip- You should discover when the best time of the day to post is. In this way, you can schedule posts targeted at that time only. You should post when most users can see it. This increases your chances of boosting engagement faster, and the Instagram algorithm as it knows what somebody likes on Instagram and will take over from here to share it with those users who will ✌ ⚡ like, share and comment on your post.
⦁ How long are people looking at your post on Instagram -: If you see the algorithm that Facebook uses, it takes into account how long users look at your post and spend time interacting with its content. Instagram is no different! Its algorithm will take a look at the duration of time that users spend on your post too. This is again, another important factor that Instagram uses to boost posts.
👉▶Tip- Here, you should craft a great 📸 Instagram caption for your post to boost user engagement. If your Instagram caption is engaging, users will want to read it or click on the button that says “more” to spend more time on your post, and in this way, you can boost more engagement with success on 📸 Instagram.
This is why Boomerangs and videos are generally posted in the 📹 video format. This makes them perform well with the Instagram algorithm. Users take more time to watch them toll the end. Another great way for you to make users stay on your posts is to swipe up CTA action for them to view more. This is another great strategy you can use for your business to boost engagement.
⦁ When did you post your photo?-: This is another factor that influences how the 📸 Instagram algorithm works for your business is determining the time of the post. It depends on how often you use the 📲 app. In the past, the algorithm used to give you insights into the recent posts; however, if you tend to log on to Instagram just a few times in one week, it will show posts that have been posted recently. You might even get likes from a previous post that you published a few days ago. The target here is to keep users updated on the posts they might have missed due to the fact that they did not log in regularly.
👉 This means that users can see your posts now for longer periods.
⦁ The sort of content you post also influences engagement – Think about if 📸 Instagram only focused on content with the optimal engagement; users would only see that content every time they logged into 📸 Instagram. However, this is not the case with the Instagram algorithm.
For instance, the content 👥 users’ genre searches for in the platform also influence the algorithm of how Instagram works. If a user is a fan of sports, says the NBA and sees that genre of content regularly, Instagram will immediately catch on to this and bring the user more similar content related to sports and the NBA. It knows that the user will be interested in such posts and perhaps bring news and updates of the LA Lakers to boost 👤 user engagement and satisfaction.
⦁ Accounts that users search for -: Like the above, the accounts that users search for also determines how the 📸 Instagram algorithm works. This is why when users search a specific 🧾 account many times; Instagram will bring more such content from other accounts to that user. This is why they see it often in their Instagram feeds.
From the above, it is evident that if you want to work with the new Instagram algorithm, you must understand how it works and optimize your posts to help it boost your business. In the past, the feed on Instagram was chronological; however, things have changed now.
🟥 So, ensure that your CTA is strong; you use the right hashtags, post at the best time, and make your feed on Instagram as attractive as possible. In this way, you can boost user engagement, lead conversions, sales, and of course, gain a strategic edge in the market as well.
↘ Source: Ron Johnson. Ron is a Marketer. He always shares his tips on trendy marketing tricks. He always implements new tricks in his field.
Top 10 Big Data trends of 2020
During the last few decades, Big Data has become an insightful idea in all the significant technical terms. Additionally, the accessibility of wireless connections and different advances have facilitated the analysis of large data sets. Organizations and huge companies are picking up strength consistently by improving their data analytics and platforms.
2019 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2018, and is set to proceed with its rapid growth and reach $247 Billion by 2022.
It’s the ideal opportunity for us to look at Big Data trends for 2020.
Chief Data Officers (CDOs) will be the Center of Attraction
The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements.
CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.
Investment in Big Data Analytics
Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2020 may not be ready to go in 2021. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.)
The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2019. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2020.
Multi-cloud and Hybrid are Setting Deep Roots
As cloud-based advances keep on developing, organizations are progressively liable to want a spot in the cloud. Notwithstanding, the process of moving your data integration and preparation from an on-premises solution to the cloud is more confounded and tedious than most care to concede. Additionally, to relocate huge amounts of existing data, organizations should match up to their data sources and platforms for a little while to months before the shift is complete.
In 2020, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.
Actionable Data will Grow
Another development concerning big data trends 2020 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams.
Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.
Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data.
Gartner predicts more than 50% of new business systems will utilize continuous intelligence by 2022. This move has begun, and numerous companies will fuse continuous intelligence during 2020 to pick up or keep up a serious edge.
Machine Learning will Continue to be in Focus
Being a significant innovation in big data trends 2020, machine learning (ML) is another development expected to affect our future fundamentally. ML is a rapidly developing advancement that used to expand regular activities and business processes
ML projects have gotten the most investments in 2019, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.
Abandon Hadoop for Spark and Databricks
Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop.
However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.
In-memory computing has the additional advantage of helping business clients (counting banks, retailers, and utilities) to identify patterns rapidly and break down huge amounts of data without any problem. The dropping of costs for memory is a major factor in the growing enthusiasm for in-memory computing innovation.
In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2020, in-memory computing will pick up fame because of the decreases in expenses of memory.
IoT and Big Data
There are such enormous numbers of advancements that expect to change the current business situations in 2020. It is hard to be aware of all that, however, IoT and digital gadgets are required to get a balance in big data trends 2020.
The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.
Digital Transformation Will Be a Key Component
Digital transformation goes together with the Internet of Things (IoT), artificial intelligence (AI), machine learning and big data. With IoT connected devices expected to arrive at a stunning 75 billion devices in 2025 from 26.7 billion presently, it’s easy to see where that big data is originating from. Digital transformation as IoT, IaaS, AI and machine learning is taking care of big data and pushing it to regions inconceivable in mankind’s history.
What are the differences between Data Lake and Data Warehouse?
- Understand the meaning of data lake and data warehouse
- We will see what are the key differences between Data Warehouse and Data Lake
- Understand which one is better for the organization
From processing to storing, every aspect of data has become important for an organization just due to the sheer volume of data we produce in this era. When it comes to storing big data you might have come across the terms with Data Lake and Data Warehouse. These are the 2 most popular options for storing big data.
Having been in the data industry for a long time, I can vouch for the fact that a data warehouse and data lake are 2 different things. Yet I see many people using them interchangeably. As a data engineer understanding data lake and data warehouse along with its differences and usage are very crucial as then only will you understand if data lake fits your organization or data warehouse?
So in this article, let satiate your curiosity by explaining what data lake and warehousing are and highlight the difference between them.
Table of Contents
- What is a Data Lake?
- What is a Data Warehouse?
- What are the differences between Data Lake and Data Warehouse?
- Which one to use?
What is a Data Lake?
A Data Lake is a common repository that is capable to store a huge amount of data without maintaining any specified structure of the data. You can store data whose purpose may or may not yet be defined. Its purposes include- building dashboards, machine learning, or real-time analytics.
Now, when you store a huge amount of data at a single place from multiple sources, it is important that it should be in a usable form. It should have some rules and regulations so as to maintain data security and data accessibility.
Otherwise, only the team who designed the data lake knows how to access a particular type of data. Without proper information, it would be very difficult to distinguish between the data you want and the data you are retrieving. So it is important that your data lake does not turn into a data swamp.
Image Source: here
What is a Data Warehouse?
A Data Warehouse is another database that only stores the pre-processed data. Here the structure of the data is well-defined, optimized for SQL queries, and ready to be used for analytics purposes. Some of the other names of the Data Warehouse are Business Intelligence Solution and Decision Support System.
What are the differences between Data Lake and Data Warehouse?
|Data Lake||Data Warehouse|
|Data Storage and Quality||The Data Lake captures all types of data like structure, unstructured in their raw format. It contains the data which might be useful in some current use-case and also that is likely to be used in the future.||It contains only high-quality data that is already pre-processed and ready to be used by the team.|
|Purpose||The purpose of the Data Lake is not fixed. Sometimes organizations have a future use-case in mind. Its general uses include data discovery, user profiling, and machine learning.||The data warehouse has data that has already been designed for some use-case. Its uses include Business Intelligence, Visualizations, and Batch Reporting.|
|Users||Data Scientists use data lakes to find out the patterns and useful information that can help businesses.||Business Analysts use data warehouses to create visualizations and reports.|
|Pricing||It is comparatively low-cost storage as we do not give much attention to storing in the structured format.||Storing data is a bit costlier and also a time-consuming process.|
Which one to use?
We have seen what are the differences between a data lake and a data warehouse. Now, we will see which one should we use?
If your organization deals with healthcare or social media, the data you capture will be mostly unstructured (documents, images). The volume of structured data is very less. So here, the data lake is a good fit as it can handle both types of data and will give more flexibility for analysis.
If your online business is divided into multiple pillars, you obviously want to get summarized dashboards of all of them. The data warehouses will be helpful in this case in making informed decisions. It will maintain the data quality, consistency, and accuracy of the data.
Most of the time organizations use a combination of both. They do the data exploration and analysis over the data lake and move the rich data to the data warehouses for quick and advance reporting.
In this article, we have seen the differences between data lake and data warehouse on the basis of data storage, purpose to use, which one to use. Understanding this concept will help the big data engineer choose the right data storage mechanism and thus optimize the cost and processes of the organization.
The following are some additional data engineering resources that I strongly recommend you go through-
If you find this article informative, then please share it with your friends and comment below your queries and feedback.
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