Home > Resources > Articles > Role of Edge Analytics in Smarter Computing & Business Growth

Role of Edge Analytics in Smarter Computing & Business Growth

Role of Edge Analytics in Smarter
As businesses are moving towards using more and more data for decision-making, data-driven insights have become the most valuable asset for any company. Today, businesses are feeling the need to process data and access analytics in real-time. In the past, businesses collected data from various IoT devices and sensors, centralized it in a data warehouse or data lake, and then analyzed it to get insights.

What if businesses could bypass the data centralization or integration stage entirely and go straight to the analysis stage? This technique is known as edge analytics. This method allows businesses to accomplish autodidact machine learning, improve data security, and reduce data transfer costs.

With edge analytics and edge computing, businesses can not only generate more sales but also boost efficiency, enhance productivity, and save costs.

Let’s dive deeper into edge analytics, how it complements cloud computing, and why businesses are increasingly opting for it.


How can Edge Analytics Complement Cloud Computing?

Real-time decision-making is still challenging in IoT systems due to factors like bandwidth, latency, power consumption, cost, and various other considerations. This problem, however, can be addressed by using of artificial intelligence in edge analytics, which also makes cloud computing better.

Cloud computing and edge computing are very different approaches and purely depend on the software implemented. These two technologies don’t discredit each other, but rather complement each other.

  • Reduces utilization of data bandwidth or transfer
  • Ends the need for continuous connectivity to the cloud
  • Boosts the real-time performance with faster processing
  • Enhances data security


Common Pitfalls to Dodge with Edge Analytics and Edge Computing

According to Statista, the number of Internet of Things (IoT) devices will reach 30.9 billion units by 2025. Moreover, the global IoT market is expected to grow to $1.6 trillion by 2025.

The cost of transferring and storing all of that data, combined with the lack of a clear advantage, has led many to question whether the IoT is worth the hype. That is why the industry is shifting its focus to edge analytics or computing to fully leverage the data collected from IoT devices. Let’s take a look at some of the challenges that can be addressed with the help of edge analytics:

  • Many industrial IoT solutions require complete uptime.
  • Consumer IoT apps need to process localized events in real-time.
  • A power outage might result in a security breach.
  • Difficulties in adhering to data regulations.


Why You Should Employ Edge Analytics?

“To remain competitive in the post-cloud era, innovative companies are adopting edge computing due to its endless breakthrough capabilities that are not available at the core.”

- David Williams, managing principal at AHEAD.

Edge analytics solutions assist businesses wherever data insights are needed at the edge. It can be used in various industries for numerous things, such as retail customer behavior analysis, remote monitoring and maintenance, detecting fraud at ATMs and other financial sites, and monitoring manufacturing and logistical equipment. Here are some reasons you should choose edge analytics and edge computing for your business.


Saves Time

The prime objective of adopting an edge analytics system is to filter out unnecessary information prior to analysis, and only relevant data is sent via higher-order methods. This saves a lot of time when it comes to processing and uploading data, which makes the complex analytical process done on the cloud a lot more valuable and effective.


Reduces Cost

The use of edge analytics in IoT cuts the cost of data storage and administration. It also saves operating expenses, bandwidth requirements, and resources spent on data processing. All of these things add up to substantial financial savings.


Safeguards Privacy

Edge analytics assists in the preservation of privacy when sensitive or confidential data is gathered by a device, such as GPS data or video streams. This sensitive data is pre-processed on-site rather than being transferred to the cloud for processing. This additional step ensures that only data that complies with privacy laws leaves the device for further analysis.


Reduces Data Analysis Delay

Edge analytics tools enables faster, autonomous decision-making since insights are identified at the data source, preventing latency. It is more effective to analyze data on the defective device itself and shut down the faulty equipment immediately instead of waiting for the data from the equipment to be transferred to a central data analytics environment and waiting for the result.


Solves Connectivity Issues

By making sure that applications are not disrupted by restricted or interrupted network access, edge analytics in IoT helps to safeguard against possible connectivity disruptions in IoT. It is particularly beneficial in rural areas or for minimizing connection costs when utilizing costly technologies such as cellular networks.

 

Industries Leveraging Edge Analytics


Closing Lines

Edge analytics is an exciting field, with businesses in the Internet of Things (IoT) sector growing their expenditures every year. Leading vendors are actively investing in this rapidly growing market. Edge analytics provides measurable business advantages in certain industries such as retail, manufacturing, energy, and logistics by decreasing decision latency, scaling out analytics resources, resolving bandwidth issues, and perhaps reducing expenditures. The potential at the edge leads to a very exciting future of smart computing as sensors get more affordable, applications need more real-time analytics, and developing optimized, cost-effective edge algorithms becomes simpler.


FAQ

What distinguishes edge analytics from regular analytics?

Except for the location of the analysis, edge analytics offers remarkably similar capabilities to regular analytics systems. One significant difference is that edge analytics apps can run on edge devices that can have memory, processing power, or communication.


What are edge devices, and what are some examples?

An edge device serves as an access point to the core networks of businesses or service providers. Some examples include routers, switching devices, integrated access devices (IADs), multiplexers, and other metropolitan area network (MAN) and wide area network (WAN) access devices.


What exactly are edge machines?

Edge ML is a technology that allows smart devices to analyze data locally through local servers or at the device level. This is done with the help of machine and deep learning algorithms, decreasing dependency on cloud networks.

Spotlight

ValuePoint Systems Pvt Ltd

Value Point Systems, one of the leading Digital Systems & Services Integrator company in South Asia. We are a global System Integrator with expertise in providing end-to-end IT Infrastructure solutions and services with Best in class technology partnerships. Headquartered in Bengaluru, India, Value Point Systems is one of its Top 100 IT companies and one amongst the first 5 Tier II Systems Integrators having Pan India presence. Built on the foundation of trust, commitment & mutual respect, our thought leaders are committed in ensuring customer‐centric approaches to guarantee lasting experiences & sustainable relationships. We have been delivering innovative, optimum, effective solutions and services to over 15,000 large enterprises and SMEs including Fortune 500 customers since the past two decades, worldwide.

OTHER ARTICLES
Business Strategy

Will We Be Able to Use AI to Prevent Further Pandemics?

Article | April 4, 2022

For many, 2021 has brought hope that they can cautiously start to prepare for a world after Covid. That includes living with the possibility of future pandemics, and starting to reflect on what has been learned from such a brutal shared experience. One of the areas that has come into its own during Covid has been artificial intelligence (AI), a technology that helped bring the pandemic under control, and allow life to continue through lockdowns and other disruptions. Plenty has been written about how AI has supported many aspects of life at work and home during Covid, from videoconferencing to online food ordering. But the role of AI in preventing Covid causing even more havoc is not necessarily as widely known. Perhaps even more importantly, little has been said about the role AI is likely to play in preparing for, responding to and even preventing future pandemics. From what we saw in 2020, AI will help prevent global outbreaks of new diseases in three ways: prediction, diagnosis and treatment. Prediction Predicting pandemics is all about tracking data that could be possible early signs that a new disease is spreading in a disturbing way. The kind of data we’re talking about includes public health information about symptoms presenting to hospitals and doctors around the world. There is already plenty of this captured in healthcare systems globally, and is consolidated into datasets such as the Johns Hopkins reports that many of us are familiar with from news briefings. Firms like Bluedot and Metabiota are part of a growing number of organisations which use AI to track both publicly available and private data and make relevant predictions about public health threats. Both of these received attention in 2020 by reporting the appearance of Covid before it had been officially acknowledged. Boston Children’s Hospital is an example of a healthcare institution doing something similar with their Healthmap resource. In addition to conventional healthcare data, AI is uniquely able to make use of informal data sources such as social media, news aggregators and discussion forums. This is because of AI techniques such as natural language processing and sentiment analysis. Firms such as Stratifyd use AI to do this in other business settings such as marketing, but also talk publicly about the use of their platform to predict and prevent pandemics. This is an example of so-called augmented intelligence, where AI is used to guide people to noteworthy data patterns, but stops short of deciding what it means, leaving that to human judgement. Another important part of preventing a pandemic is keeping track of the transmission of disease through populations and geographies. A significant issue in 2020 was difficulty tracing people who had come into contact with infection. There was some success using mobile phones for this, and AI was critical in generating useful knowledge from mobile phone data. The emphasis of Covid tracing apps in 2020 was keeping track of how the disease had already spread, but future developments are likely to be about predicting future spread patterns from such data. Prediction is a strength of AI, and the principles used to great effect in weather forecasting are similar to those used to model likely pandemic spread. Diagnosis To prevent future pandemics, it won’t be enough to predict when a disease is spreading rapidly. To make the most of this knowledge, it’s necessary to diagnose and treat cases. One of the greatest early challenges with Covid was the lack of speedy, reliable tests. For future pandemics, AI is likely to be used to create such tests more quickly than was the case in 2020. Creating a useful test involves modelling a disease’s response to different testing reagents, finding right balance between speed, convenience and accuracy. AI modelling simulates in a computer how individual cells respond to different stimuli, and could be used to perform virtual testing of many different types of test to accelerate how quickly the most promising ones reach laboratory and field trials. In 2020 there were also several novel uses of AI to diagnose Covid, but there were few national and global mechanisms to deploy these at scale. One example was the use of AI imaging, diagnosing Covid by analysing chest x-rays for features specific to Covid. This would have been especially valuable in places that didn’t have access to lab testing equipment. Another example was using AI to analyse the sound of coughs to identify unique characteristics of a Covid cough. AI research to systematically investigate innovative diagnosis techniques such as these should result in better planning for alternatives to laboratory testing. Faster and wider rollout of this kind of diagnosis would help control spread of a future disease during the critical period waiting for other tests to be developed or shared. This would be another contribution of AI to preventing a localised outbreak becoming a pandemic. Treatment Historically, vaccination has proven to be an effective tool for dealing with pandemics, and was the long term solution to Covid for most countries. AI was used to accelerate development of Covid vaccines, helping cut the development time from years or decades to months. In principle, the use of AI was similar to that described above for developing diagnostic tests. Different drug development teams used AI in different ways, but they all relied on mathematical modelling of how the Covid virus would respond to many forms of treatment at a microscopic level. Much of the vaccine research and modelling focused on the “spike” proteins that allow Covid to attack human cells and enter the body. These are also found in other viruses, and were already the subject of research before the 2020 pandemic. That research allowed scientists to quickly develop AI models to represent the spikes, and simulate the effects of different possible treatments. This was crucial in trialling thousands of possible treatments in computer models, pinpointing the most likely successes for further investigation. This kind of mathematical simulation using AI continued during drug development, and moved substantial amounts of work from the laboratory to the computer. This modelling also allowed the impact of Covid mutations on vaccines to be assessed quickly. It is why scientists were reasonably confident of developing variants of vaccines for new Covid mutations in days and weeks rather than months. As a result of the global effort to develop Covid vaccines, the body of data and knowledge about virus behaviour has grown substantially. This means it should be possible to understand new pathogens even more rapidly than Covid, potentially in hours or days rather than weeks. AI has also helped create new ways of approaching vaccine development, for example the use of pre-prepared generic vaccines designed to treat viruses from the same family as Covid. Modifying one of these to the specific features of a new virus is much faster than starting from scratch, and AI may even have already simulated exactly such a variation. AI has been involved in many parts of the fight against Covid, and we now have a much better idea than in 2020 of how to predict, diagnose and treat pandemics, especially similar viruses to Covid. So we can be cautiously optimistic that vaccine development for any future Covid-like viruses will be possible before it becomes a pandemic. Perhaps a trickier question is how well we will be able to respond if the next pandemic is from a virus that is nothing like Covid. Was Rahman is an expert in the ethics of artificial intelligence, the CEO of AI Prescience and the author of AI and Machine Learning. See more at www.wasrahman.com

Read More
Business Strategy

Taking a qualitative approach to a data-driven market

Article | July 22, 2022

While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before. Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading. But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions. Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value. Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before. And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages. Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery. While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of. Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns. For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards. So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase. The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based. Human error While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure. Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes. But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue. Drawing a line The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened. So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing. It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor. But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise. Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur. Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on. So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition. But if you are looking to get ahead, you may want to consider taking a human approach too.

Read More
Business Strategy

Business Intelligence VS Predictive Analytics: Key Differentiators

Article | April 21, 2022

Predictive analytics and business intelligence have become some of the most important tools for businesses because of their outstanding capabilities. Most people believe that predictive analytics is a part of business intelligence (BI), but that is not the case. If we look at the definition of business intelligence, we can argue that predictive analytics actually falls under the umbrella of BI, but that's not entirely true. While that definition is pretty much correct for both terms, if we dig down a little deeper, we will see that there are significant differences between business intelligence and predictive analytics in both practices as well as theories. Let’s drill down to understand the key differentiators between predictive analytics and business intelligence. Key Differentiators: Predictive Analytics VS BI BI seeks to answer queries like "what happens now" and "what is happening now," whereas predictive analytics tries to predict "what will happen" and provides a more practical method to assess information. Data Raw data is processed into insights for direct consumer use during the business intelligence process. With predictive analytics, unstructured data is turned into structured data that can be used to make predictions about the future. Decision Users can make decisions based on insights provided by business intelligence. Businesses can use predictive analytics to make decisions based on facts, data sets, and predictions. Purpose The objective of business intelligence tools is to equip users with information about their company's historical data performance. Predictive analytics utilizes forecasting techniques to help in the solving of complex business challenges. Methods Business intelligence uses data visualization, data mining, reporting, dashboards, OLAP, etc., with previous performance indicators. Predictive analytics predicts future occurrences and analyzes raw data patterns. Technologies Ad-hoc reporting technology, alerting technology, and other technologies are covered in business intelligence. Predictive analytics includes technologies such as predictive modeling, forecasting, etc. Use Predictive Analytics in Business Intelligence to Optimize Marketing Efforts Businesses now have a plethora of information about their customers’ and target audience's purchasing patterns and preferences, all thanks to business intelligence insights. With all of this information, predictive analytics can determine the possibility of a consumer purchasing a product, allowing businesses to target their marketing efforts on customers who are more likely to purchase their items. Businesses that employ predictive analytics and business intelligence solutions can constantly remain one step ahead of their competition. Summing Up At times, the sheer variety of tools available can be intimidating, and misinformation can sometimes hamper the selection process of technology. Business intelligence and predictive analytics are two of the most productive technologies in the market, but when combined, they can do wonders for businesses.

Read More
Data Architecture

Evolution of capabilities of Data Platforms & data ecosystem

Article | October 27, 2020

Data Platforms and frameworks have been constantly evolving. At some point of time; we are excited by Hadoop (well for almost 10 years); followed by Snowflake or as I say Snowflake Blizzard (who managed to launch biggest IPO win historically) and the Google (Google solves problems and serves use cases in a way that few companies can match). The end of the data warehouse Once upon a time, life was simple; or at least, the basic approach to Business Intelligence was fairly easy to describe… A process of collecting information from systems, building a repository of consistent data, and bolting on one or more reporting and visualisation tools which presented information to users. Data used to be managed in expensive, slow, inaccessible SQL data warehouses. SQL systems were notorious for their lack of scalability. Their demise is coming from a few technological advances. One of these is the ubiquitous, and growing, Hadoop. On April 1, 2006, Apache Hadoop was unleashed upon Silicon Valley. Inspired by Google, Hadoop’s primary purpose was to improve the flexibility and scalability of data processing by splitting the process into smaller functions that run on commodity hardware. Hadoop’s intent was to replace enterprise data warehouses based on SQL. Unfortunately, a technology used by Google may not be the best solution for everyone else. It’s not that others are incompetent: Google solves problems and serves use cases in a way that few companies can match. Google has been running massive-scale applications such as its eponymous search engine, YouTube and the Ads platform. The technologies and infrastructure that make the geographically distributed offerings perform at scale are what make various components of Google Cloud Platform enterprise ready and well-featured. Google has shown leadership in developing innovations that have been made available to the open-source community and are being used extensively by other public cloud vendors and Gartner clients. Examples of these include the Kubernetes container management framework, TensorFlow machine learning platform and the Apache Beam data processing programming model. GCP also uses open-source offerings in its cloud while treating third-party data and analytics providers as first-class citizens on its cloud and providing unified billing for its customers. The examples of the latter include DataStax, Redis Labs, InfluxData, MongoDB, Elastic, Neo4j and Confluent. Silicon Valley tried to make Hadoop work. The technology was extremely complicated and nearly impossible to use efficiently. Hadoop’s lack of speed was compounded by its focus on unstructured data — you had to be a “flip-flop wearing” data scientist to truly make use of it. Unstructured datasets are very difficult to query and analyze without deep knowledge of computer science. At one point, Gartner estimated that 70% of Hadoop deployments would not achieve the goal of cost savings and revenue growth, mainly due to insufficient skills and technical integration difficulties. And seventy percent seems like an understatement. Data storage through the years: from GFS to Snowflake or Snowflake blizzard Developing in parallel with Hadoop’s journey was that of Marcin Zukowski — co-founder and CEO of Vectorwise. Marcin took the data warehouse in another direction, to the world of advanced vector processing. Despite being almost unheard of among the general public, Snowflake was actually founded back in 2012. Firstly, Snowflake is not a consumer tech firm like Netflix or Uber. It's business-to-business only, which may explain its high valuation – enterprise companies are often seen as a more "stable" investment. In short, Snowflake helps businesses manage data that's stored on the cloud. The firm's motto is "mobilising the world's data", because it allows big companies to make better use of their vast data stores. Marcin and his teammates rethought the data warehouse by leveraging the elasticity of the public cloud in an unexpected way: separating storage and compute. Their message was this: don’t pay for a data warehouse you don’t need. Only pay for the storage you need, and add capacity as you go. This is considered one of Snowflake’s key innovations: separating storage (where the data is held) from computing (the act of querying). By offering this service before Google, Amazon, and Microsoft had equivalent products of their own, Snowflake was able to attract customers, and build market share in the data warehousing space. Naming the company after a discredited database concept was very brave. For those of us not in the details of the Snowflake schema, it is a logical arrangement of tables in a multidimensional database such that the entity-relationship diagram resembles a snowflake shape. … When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. Needless to say, the “snowflake” schema is as far from Hadoop’s design philosophy as technically possible. While Silicon Valley was headed toward a dead end, Snowflake captured an entire cloud data market.

Read More
Business Strategy

Importance of Encryption in the Business World

Article | April 4, 2022

For many, 2021 has brought hope that they can cautiously start to prepare for a world after Covid. That includes living with the possibility of future pandemics, and starting to reflect on what has been learned from such a brutal shared experience. One of the areas that has come into its own during Covid has been artificial intelligence (AI), a technology that helped bring the pandemic under control, and allow life to continue through lockdowns and other disruptions. Plenty has been written about how AI has supported many aspects of life at work and home during Covid, from videoconferencing to online food ordering. But the role of AI in preventing Covid causing even more havoc is not necessarily as widely known. Perhaps even more importantly, little has been said about the role AI is likely to play in preparing for, responding to and even preventing future pandemics. From what we saw in 2020, AI will help prevent global outbreaks of new diseases in three ways: prediction, diagnosis and treatment. Prediction Predicting pandemics is all about tracking data that could be possible early signs that a new disease is spreading in a disturbing way. The kind of data we’re talking about includes public health information about symptoms presenting to hospitals and doctors around the world. There is already plenty of this captured in healthcare systems globally, and is consolidated into datasets such as the Johns Hopkins reports that many of us are familiar with from news briefings. Firms like Bluedot and Metabiota are part of a growing number of organisations which use AI to track both publicly available and private data and make relevant predictions about public health threats. Both of these received attention in 2020 by reporting the appearance of Covid before it had been officially acknowledged. Boston Children’s Hospital is an example of a healthcare institution doing something similar with their Healthmap resource. In addition to conventional healthcare data, AI is uniquely able to make use of informal data sources such as social media, news aggregators and discussion forums. This is because of AI techniques such as natural language processing and sentiment analysis. Firms such as Stratifyd use AI to do this in other business settings such as marketing, but also talk publicly about the use of their platform to predict and prevent pandemics. This is an example of so-called augmented intelligence, where AI is used to guide people to noteworthy data patterns, but stops short of deciding what it means, leaving that to human judgement. Another important part of preventing a pandemic is keeping track of the transmission of disease through populations and geographies. A significant issue in 2020 was difficulty tracing people who had come into contact with infection. There was some success using mobile phones for this, and AI was critical in generating useful knowledge from mobile phone data. The emphasis of Covid tracing apps in 2020 was keeping track of how the disease had already spread, but future developments are likely to be about predicting future spread patterns from such data. Prediction is a strength of AI, and the principles used to great effect in weather forecasting are similar to those used to model likely pandemic spread. Diagnosis To prevent future pandemics, it won’t be enough to predict when a disease is spreading rapidly. To make the most of this knowledge, it’s necessary to diagnose and treat cases. One of the greatest early challenges with Covid was the lack of speedy, reliable tests. For future pandemics, AI is likely to be used to create such tests more quickly than was the case in 2020. Creating a useful test involves modelling a disease’s response to different testing reagents, finding right balance between speed, convenience and accuracy. AI modelling simulates in a computer how individual cells respond to different stimuli, and could be used to perform virtual testing of many different types of test to accelerate how quickly the most promising ones reach laboratory and field trials. In 2020 there were also several novel uses of AI to diagnose Covid, but there were few national and global mechanisms to deploy these at scale. One example was the use of AI imaging, diagnosing Covid by analysing chest x-rays for features specific to Covid. This would have been especially valuable in places that didn’t have access to lab testing equipment. Another example was using AI to analyse the sound of coughs to identify unique characteristics of a Covid cough. AI research to systematically investigate innovative diagnosis techniques such as these should result in better planning for alternatives to laboratory testing. Faster and wider rollout of this kind of diagnosis would help control spread of a future disease during the critical period waiting for other tests to be developed or shared. This would be another contribution of AI to preventing a localised outbreak becoming a pandemic. Treatment Historically, vaccination has proven to be an effective tool for dealing with pandemics, and was the long term solution to Covid for most countries. AI was used to accelerate development of Covid vaccines, helping cut the development time from years or decades to months. In principle, the use of AI was similar to that described above for developing diagnostic tests. Different drug development teams used AI in different ways, but they all relied on mathematical modelling of how the Covid virus would respond to many forms of treatment at a microscopic level. Much of the vaccine research and modelling focused on the “spike” proteins that allow Covid to attack human cells and enter the body. These are also found in other viruses, and were already the subject of research before the 2020 pandemic. That research allowed scientists to quickly develop AI models to represent the spikes, and simulate the effects of different possible treatments. This was crucial in trialling thousands of possible treatments in computer models, pinpointing the most likely successes for further investigation. This kind of mathematical simulation using AI continued during drug development, and moved substantial amounts of work from the laboratory to the computer. This modelling also allowed the impact of Covid mutations on vaccines to be assessed quickly. It is why scientists were reasonably confident of developing variants of vaccines for new Covid mutations in days and weeks rather than months. As a result of the global effort to develop Covid vaccines, the body of data and knowledge about virus behaviour has grown substantially. This means it should be possible to understand new pathogens even more rapidly than Covid, potentially in hours or days rather than weeks. AI has also helped create new ways of approaching vaccine development, for example the use of pre-prepared generic vaccines designed to treat viruses from the same family as Covid. Modifying one of these to the specific features of a new virus is much faster than starting from scratch, and AI may even have already simulated exactly such a variation. AI has been involved in many parts of the fight against Covid, and we now have a much better idea than in 2020 of how to predict, diagnose and treat pandemics, especially similar viruses to Covid. So we can be cautiously optimistic that vaccine development for any future Covid-like viruses will be possible before it becomes a pandemic. Perhaps a trickier question is how well we will be able to respond if the next pandemic is from a virus that is nothing like Covid. Was Rahman is an expert in the ethics of artificial intelligence, the CEO of AI Prescience and the author of AI and Machine Learning. See more at www.wasrahman.com

Read More
Business Strategy

Metadata Driven Data Fabric

Article | July 22, 2022

While digital transformation is proving to have many benefits for businesses, what is perhaps the most significant, is the vast amount of data there is available. And now, with an increasing number of businesses turning their focus to online, there is even more to be collected on competitors and markets than ever before. Having all this information to hand may seem like any business owner’s dream, as they can now make insightful and informed commercial decisions based on what others are doing, what customers want and where markets are heading. But according to Nate Burke, CEO of Diginius, a propriety software and solutions provider for ecommerce businesses, data should not be all a company relies upon when making important decisions. Instead, there is a line to be drawn on where data is required and where human expertise and judgement can provide greater value. Undeniably, the power of data is unmatched. With an abundance of data collection opportunities available online, and with an increasing number of businesses taking them, the potential and value of such information is richer than ever before. And businesses are benefiting. Particularly where data concerns customer behaviour and market patterns. For instance, over the recent Christmas period, data was clearly suggesting a preference for ecommerce, with marketplaces such as Amazon leading the way due to greater convenience and price advantages. Businesses that recognised and understood the trend could better prepare for the digital shopping season, placing greater emphasis on their online marketing tactics to encourage purchases and allocating resources to ensure product availability and on-time delivery. While on the other hand, businesses who ignored, or simply did not utilise the information available to them, would have been left with overstocked shops and now, out of season items that would have to be heavily discounted or worse, disposed of. Similarly, search and sales data can be used to understand changing consumer needs, and consequently, what items businesses should be ordering, manufacturing, marketing and selling for the best returns. For instance, understandably, in 2020, DIY was at its peak, with increases in searches for “DIY facemasks”, “DIY decking” and “DIY garden ideas”. For those who had recognised the trend early on, they had the chance to shift their offerings and marketing in accordance, in turn really reaping the rewards. So, paying attention to data certainly does pay off. And thanks to smarter and more sophisticated ways of collecting data online, such as cookies, and through AI and machine learning technologies, the value and use of such information is only likely to increase. The future, therefore, looks bright. But even with all this potential at our fingertips, there are a number of issues businesses may face if their approach relies entirely on a data and insight-driven approach. Just like disregarding its power and potential can be damaging, so can using it as the sole basis upon which important decisions are based. Human error While the value of data for understanding the market and consumer patterns is undeniable, its value is only as rich as the quality of data being inputted. So, if businesses are collecting and analysing their data on their own activity, and then using this to draw meaningful insight, there should be strong focus on the data gathering phase, with attention given to what needs to be collected, why it should be collected, how it will be collected, and whether in fact this is an accurate representation of what it is you are trying to monitor or measure. Human error can become an issue when this is done by individuals or teams who do not completely understand the numbers and patterns they are seeing. There is also an obstacle presented when there are various channels and platforms which are generating leads or sales for the business. In this case, any omission can skew results and provide an inaccurate picture. So, when used in decision making, there is the possibility of ineffective and unsuccessful changes. But while data gathering becomes more and more autonomous, the possibility of human error is lessened. Although, this may add fuel to the next issue. Drawing a line The benefits of data and insights are clear, particularly as the tasks of collection and analysis become less of a burden for businesses and their people thanks to automation and AI advancements. But due to how effortless data collection and analysis is becoming, we can only expect more businesses to be doing it, meaning its ability to offer each individual company something unique is also being lessened. So, businesses need to look elsewhere for their edge. And interestingly, this is where a line should be drawn and human judgement should be used in order to set them apart from the competition and differentiate from what everyone else is doing. It makes perfect sense when you think about it. Your business is unique for a number of reasons, but mainly because of the brand, its values, reputation and perceptions of the services you are upheld by. And it’s usually these aspects that encourage consumers to choose your business rather than a competitor. But often, these intangible aspects are much more difficult to measure and monitor through data collection and analysis, especially in the autonomous, number-driven format that many platforms utilise. Here then, there is a great case for businesses to use their own judgements, expertise and experiences to determine what works well and what does not. For instance, you can begin to determine consumer perceptions towards a change in your product or services, which quantitative data may not be able to pick up until much later when sales figures begin to rise or fall. And while the data will eventually pick it up, it might not necessarily be able to help you decide on what an appropriate alternative solution may be, should the latter occur. Human judgement, however, can listen to and understand qualitative feedback and consumer sentiments which can often provide much more meaningful insights for businesses to base their decisions on. So, when it comes to competitor analysis, using insights generated from figure-based data sets and performance metrics is key to ensuring you are doing the same as the competition. But if you are looking to get ahead, you may want to consider taking a human approach too.

Read More
Business Strategy

Embrace Corporate Performance Management to Enhance Your Business

Article | April 21, 2022

Predictive analytics and business intelligence have become some of the most important tools for businesses because of their outstanding capabilities. Most people believe that predictive analytics is a part of business intelligence (BI), but that is not the case. If we look at the definition of business intelligence, we can argue that predictive analytics actually falls under the umbrella of BI, but that's not entirely true. While that definition is pretty much correct for both terms, if we dig down a little deeper, we will see that there are significant differences between business intelligence and predictive analytics in both practices as well as theories. Let’s drill down to understand the key differentiators between predictive analytics and business intelligence. Key Differentiators: Predictive Analytics VS BI BI seeks to answer queries like "what happens now" and "what is happening now," whereas predictive analytics tries to predict "what will happen" and provides a more practical method to assess information. Data Raw data is processed into insights for direct consumer use during the business intelligence process. With predictive analytics, unstructured data is turned into structured data that can be used to make predictions about the future. Decision Users can make decisions based on insights provided by business intelligence. Businesses can use predictive analytics to make decisions based on facts, data sets, and predictions. Purpose The objective of business intelligence tools is to equip users with information about their company's historical data performance. Predictive analytics utilizes forecasting techniques to help in the solving of complex business challenges. Methods Business intelligence uses data visualization, data mining, reporting, dashboards, OLAP, etc., with previous performance indicators. Predictive analytics predicts future occurrences and analyzes raw data patterns. Technologies Ad-hoc reporting technology, alerting technology, and other technologies are covered in business intelligence. Predictive analytics includes technologies such as predictive modeling, forecasting, etc. Use Predictive Analytics in Business Intelligence to Optimize Marketing Efforts Businesses now have a plethora of information about their customers’ and target audience's purchasing patterns and preferences, all thanks to business intelligence insights. With all of this information, predictive analytics can determine the possibility of a consumer purchasing a product, allowing businesses to target their marketing efforts on customers who are more likely to purchase their items. Businesses that employ predictive analytics and business intelligence solutions can constantly remain one step ahead of their competition. Summing Up At times, the sheer variety of tools available can be intimidating, and misinformation can sometimes hamper the selection process of technology. Business intelligence and predictive analytics are two of the most productive technologies in the market, but when combined, they can do wonders for businesses.

Read More
Data Architecture

Evolution of capabilities of Data Platforms & data ecosystem

Article | October 27, 2020

Data Platforms and frameworks have been constantly evolving. At some point of time; we are excited by Hadoop (well for almost 10 years); followed by Snowflake or as I say Snowflake Blizzard (who managed to launch biggest IPO win historically) and the Google (Google solves problems and serves use cases in a way that few companies can match). The end of the data warehouse Once upon a time, life was simple; or at least, the basic approach to Business Intelligence was fairly easy to describe… A process of collecting information from systems, building a repository of consistent data, and bolting on one or more reporting and visualisation tools which presented information to users. Data used to be managed in expensive, slow, inaccessible SQL data warehouses. SQL systems were notorious for their lack of scalability. Their demise is coming from a few technological advances. One of these is the ubiquitous, and growing, Hadoop. On April 1, 2006, Apache Hadoop was unleashed upon Silicon Valley. Inspired by Google, Hadoop’s primary purpose was to improve the flexibility and scalability of data processing by splitting the process into smaller functions that run on commodity hardware. Hadoop’s intent was to replace enterprise data warehouses based on SQL. Unfortunately, a technology used by Google may not be the best solution for everyone else. It’s not that others are incompetent: Google solves problems and serves use cases in a way that few companies can match. Google has been running massive-scale applications such as its eponymous search engine, YouTube and the Ads platform. The technologies and infrastructure that make the geographically distributed offerings perform at scale are what make various components of Google Cloud Platform enterprise ready and well-featured. Google has shown leadership in developing innovations that have been made available to the open-source community and are being used extensively by other public cloud vendors and Gartner clients. Examples of these include the Kubernetes container management framework, TensorFlow machine learning platform and the Apache Beam data processing programming model. GCP also uses open-source offerings in its cloud while treating third-party data and analytics providers as first-class citizens on its cloud and providing unified billing for its customers. The examples of the latter include DataStax, Redis Labs, InfluxData, MongoDB, Elastic, Neo4j and Confluent. Silicon Valley tried to make Hadoop work. The technology was extremely complicated and nearly impossible to use efficiently. Hadoop’s lack of speed was compounded by its focus on unstructured data — you had to be a “flip-flop wearing” data scientist to truly make use of it. Unstructured datasets are very difficult to query and analyze without deep knowledge of computer science. At one point, Gartner estimated that 70% of Hadoop deployments would not achieve the goal of cost savings and revenue growth, mainly due to insufficient skills and technical integration difficulties. And seventy percent seems like an understatement. Data storage through the years: from GFS to Snowflake or Snowflake blizzard Developing in parallel with Hadoop’s journey was that of Marcin Zukowski — co-founder and CEO of Vectorwise. Marcin took the data warehouse in another direction, to the world of advanced vector processing. Despite being almost unheard of among the general public, Snowflake was actually founded back in 2012. Firstly, Snowflake is not a consumer tech firm like Netflix or Uber. It's business-to-business only, which may explain its high valuation – enterprise companies are often seen as a more "stable" investment. In short, Snowflake helps businesses manage data that's stored on the cloud. The firm's motto is "mobilising the world's data", because it allows big companies to make better use of their vast data stores. Marcin and his teammates rethought the data warehouse by leveraging the elasticity of the public cloud in an unexpected way: separating storage and compute. Their message was this: don’t pay for a data warehouse you don’t need. Only pay for the storage you need, and add capacity as you go. This is considered one of Snowflake’s key innovations: separating storage (where the data is held) from computing (the act of querying). By offering this service before Google, Amazon, and Microsoft had equivalent products of their own, Snowflake was able to attract customers, and build market share in the data warehousing space. Naming the company after a discredited database concept was very brave. For those of us not in the details of the Snowflake schema, it is a logical arrangement of tables in a multidimensional database such that the entity-relationship diagram resembles a snowflake shape. … When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. Needless to say, the “snowflake” schema is as far from Hadoop’s design philosophy as technically possible. While Silicon Valley was headed toward a dead end, Snowflake captured an entire cloud data market.

Read More
MORE ARTICLES

Spotlight

ValuePoint Systems Pvt Ltd

Value Point Systems, one of the leading Digital Systems & Services Integrator company in South Asia. We are a global System Integrator with expertise in providing end-to-end IT Infrastructure solutions and services with Best in class technology partnerships. Headquartered in Bengaluru, India, Value Point Systems is one of its Top 100 IT companies and one amongst the first 5 Tier II Systems Integrators having Pan India presence. Built on the foundation of trust, commitment & mutual respect, our thought leaders are committed in ensuring customer‐centric approaches to guarantee lasting experiences & sustainable relationships. We have been delivering innovative, optimum, effective solutions and services to over 15,000 large enterprises and SMEs including Fortune 500 customers since the past two decades, worldwide.

Related News

Business Strategy

Powered by Airbyte Offers Software Makers More than 100 Data Integrations

Business Wire | November 02, 2023

Airbyte, creators of the fastest-growing open-source data integration platform, today announced its "Powered by Airbyte" version that enables makers of software to embed over 100 integrations into their applications – accelerating their time-to-market while increasing the focus on their core product value. “Powered by Airbyte” removes the time-consuming task of building data integrations from scratch. Instead, using Airbyte's extensive library of connectors enables rapid data movement and synchronization between various sources and destinations. This novel approach liberates engineering teams from the burdensome task of integration development, letting them redirect their resources to building out their product's features and capabilities. Engineering teams shouldn't need to build everything themselves, said Michel Tricot, co-founder and CEO, Airbyte. They are wasting time on integration work when they don't need to do that. Now, their data movement and integration needs are covered with Powered by Airbyte so that they can re-focus their engineering time on their product's core value proposition. Early users of Powered by Airbyte report high satisfaction and excellent results. Cart.com is a fast-growing provider to 6,000 brands helping them get their products in the hands of their buyers. Last year, the company helped move over $5 billion in gross merchandise value with a total of 140 million product listings. Cart utilizes Powered by Airbyte in its Unified Analytics product that helps its merchant customers to predict demand, allocate inventory and adjust advertising spending by pulling in the merchant's data from sources such as Facebook, Google Ads, Google Analytics, Hubspot, Mailchimp, Shopify, and more. The data is then consolidated in Cart’s Snowflake data warehouse with all of the integration and data movement done with Powered by Airbyte. Cart is able to offer its merchants self-service to sign up for an account, connect their data sources, connect their integrations, and begin using the product on their own. “With Airbyte, we don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless,” said Chase Zieman, chief data officer, Cart. “The time and energy saved allows us to disrupt and grow faster.” Digital marketing firm, KORTX, delivers personalized experiences for clients and custom reporting built on BigQuery and Looker. That requires centralizing all of their clients’ data from Facebook Marketing, Google Ads, Google Analytics, Hubspot and more into BigQuery so it can be analyzed. Prior to using Powered by Airbyte, data was pulled manually and put into spreadsheets and, of course, clients did not have real-time access to the data. “Without Airbyte’s ease-of-use and library of API connectors, we would have either had to lean heavily on valuable engineering resources to build out custom connections to power our custom reporting dashboard solutions or continue with time-consuming manual reporting,” said Jeff Isreal, director of analytics at KORTX. Damon Henry, KORTX founder and CEO, said, “With Airbyte, our team of data engineers could easily develop new connectors or customize existing ones to meet our clients' needs. The platform’s resilience and reliability mean that we can now focus more on deriving insights and less on managing data pipelines. With Airbyte as our ally, KORTX is not just meeting but exceeding client expectations, ensuring that advertising campaigns are not just effective but are also backed by data that is as robust as it is reliable.” Airbyte makes moving data easy and affordable across almost any source and destination, helping enterprises provide their users with access to the right data for analysis and decision-making. Airbyte has the largest data engineering contributor community – with more than 800 contributors – and the best tooling to build and maintain connectors. Pricing for Powered by Airbyte is based primarily on the number of customers syncing data through Airbyte. This makes pricing easier to forecast and more predictable than alternative solutions. Additional pricing details, and a pricing estimator can be found here. About Airbyte Airbyte is the open-source data movement leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Enterprise, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

Read More

Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

Read More

Big Data Management

Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

Read More

Business Strategy

Powered by Airbyte Offers Software Makers More than 100 Data Integrations

Business Wire | November 02, 2023

Airbyte, creators of the fastest-growing open-source data integration platform, today announced its "Powered by Airbyte" version that enables makers of software to embed over 100 integrations into their applications – accelerating their time-to-market while increasing the focus on their core product value. “Powered by Airbyte” removes the time-consuming task of building data integrations from scratch. Instead, using Airbyte's extensive library of connectors enables rapid data movement and synchronization between various sources and destinations. This novel approach liberates engineering teams from the burdensome task of integration development, letting them redirect their resources to building out their product's features and capabilities. Engineering teams shouldn't need to build everything themselves, said Michel Tricot, co-founder and CEO, Airbyte. They are wasting time on integration work when they don't need to do that. Now, their data movement and integration needs are covered with Powered by Airbyte so that they can re-focus their engineering time on their product's core value proposition. Early users of Powered by Airbyte report high satisfaction and excellent results. Cart.com is a fast-growing provider to 6,000 brands helping them get their products in the hands of their buyers. Last year, the company helped move over $5 billion in gross merchandise value with a total of 140 million product listings. Cart utilizes Powered by Airbyte in its Unified Analytics product that helps its merchant customers to predict demand, allocate inventory and adjust advertising spending by pulling in the merchant's data from sources such as Facebook, Google Ads, Google Analytics, Hubspot, Mailchimp, Shopify, and more. The data is then consolidated in Cart’s Snowflake data warehouse with all of the integration and data movement done with Powered by Airbyte. Cart is able to offer its merchants self-service to sign up for an account, connect their data sources, connect their integrations, and begin using the product on their own. “With Airbyte, we don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless,” said Chase Zieman, chief data officer, Cart. “The time and energy saved allows us to disrupt and grow faster.” Digital marketing firm, KORTX, delivers personalized experiences for clients and custom reporting built on BigQuery and Looker. That requires centralizing all of their clients’ data from Facebook Marketing, Google Ads, Google Analytics, Hubspot and more into BigQuery so it can be analyzed. Prior to using Powered by Airbyte, data was pulled manually and put into spreadsheets and, of course, clients did not have real-time access to the data. “Without Airbyte’s ease-of-use and library of API connectors, we would have either had to lean heavily on valuable engineering resources to build out custom connections to power our custom reporting dashboard solutions or continue with time-consuming manual reporting,” said Jeff Isreal, director of analytics at KORTX. Damon Henry, KORTX founder and CEO, said, “With Airbyte, our team of data engineers could easily develop new connectors or customize existing ones to meet our clients' needs. The platform’s resilience and reliability mean that we can now focus more on deriving insights and less on managing data pipelines. With Airbyte as our ally, KORTX is not just meeting but exceeding client expectations, ensuring that advertising campaigns are not just effective but are also backed by data that is as robust as it is reliable.” Airbyte makes moving data easy and affordable across almost any source and destination, helping enterprises provide their users with access to the right data for analysis and decision-making. Airbyte has the largest data engineering contributor community – with more than 800 contributors – and the best tooling to build and maintain connectors. Pricing for Powered by Airbyte is based primarily on the number of customers syncing data through Airbyte. This makes pricing easier to forecast and more predictable than alternative solutions. Additional pricing details, and a pricing estimator can be found here. About Airbyte Airbyte is the open-source data movement leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Enterprise, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

Read More

Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

Read More

Big Data Management

Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

Read More

Events