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We’ve all heard the buzzword “Big Data“ and frankly you maybe even a bit tired of hearing it. Although the term is too generic and often improperly used, it is not just a hype. It’s a quiet revolution. The age of data-driven management has already arrived and those that don’t adapt will be stomped out by competition. Let’s look at some of the industries which have already been transformed by the use of Big Data analytics.
Creating a seamless user experience and managing multiple-channel customer interaction is essential. For example, a consumer might begin researching a product on a mobile app, purchase it online and pick it up at a store. Coordinating this multi-channel shopping interaction requires a business to effectively manage, integrate and understand this vast array of data coming at a non-stop pace. For example, you may figure out that certain video game is extremely popular but which of your customers order it online and which ones prefer to go to the store is a key question that can drive personalized marketing campaigns with a greater ROI. The following infographic from business and technology consulting firm Wipro explains further.
Figuring out the shortest route from the distribution center to the store and having a balanced stock in each distribution center drives huge savings in operating costs. The Boston Consulting Group analyzes how big data is being used in supply chain management in the article “Making Big Data Work: Supply Chain Management“. One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from that article.
Banking & Insurance
In both banking and insurance sector the name of the game is Risk Management. A bank issues you a loan or a credit card and they make money on the interest rate. Besides obvious risk of you not paying of your debt there is another risk which is you paying off your debt prematurely and thus generating less revenue for the bank.
Predictive analytics has been in use since the 90’s to identify the interest rates thresholds which result in early payoff / reduced loan interest rate income for the banks. In the financial world a single transaction is the key building block of huge amounts of data that are then analyzed with predictive models and based on trending on massive scale allow for categorization of customer profiles that can predict risk associated with individual users. Banks can model their clients’ financial performance on multiple data sources and scenarios. Data science can also help strengthen risk management in areas such as cards fraud detection, financial crime compliance, credit scoring, stress-testing and cyber analytics.
In the insurance world it also boils down to customer profiles – if the premium is too high (the offer is not a good fit to customer profile) they may switch to another insurance company. To contrast this, if you have a risky car driver your offering is costing your insurance company more in claims than it does in the insurance rate or premiums. Figuring out which customers are more risk-prone than others allows for custom tailored offers that mitigate the risk of losing a good customer or losing money on a bad customer. A good example of how technology is disrupting this field is the Snapshot device which transmits data about when customers drive, how often they drive, and how hard they brake.
It is not expensive and it is available now
According to the Accenture study the main reason why business owners aren’t implementing their Big Data ideas is the perception that it is very expensive. They would have been right 10 years ago. Not anymore.
Microsoft’s Power BI platform allows small and medium sized business owners to harvest the power of Big Data analytics without any technical expertise. Also, because it’s a platform it comes with insightful industry-specific BI tools – there’s no need to reinvent the wheel, you can start using the same reports that big players use, for a fraction of the cost. Using real-time business data, Power BI delivers crisp, clear dashboards that assist managers to comprehend where their business stands today, how it performed historically, and what can be done for future success.
Besides savings, on implementation costs (which can be tens or hundreds of thousands of dollars) your maintenance costs are virtually zero dollars. The Microsoft team not just keeps the platform running smooth, but improves and updates features as the market evolves, so you know that you will always get the latest industry-adopted reporting standards on your laptop, mobile or any other device anywhere you are.
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You can’t dig into Big Data storage without first discussing Big Data in general. Big Data is a concept that any IT professional or knowledge worker understands almost by instinct, as the trend has been covered so extensively.
Data has been growing exponentially in recent years, yet much of it is locked in application and database siloes. If you could drill into all of that data, if you could share it, if you could cross-pollinate, say, a CRM system with information from your marketing analytics tools, your organization would benefit. Easier said than done.
That, essentially, is the Big Data challenge.
Arguably, the concept of Big Data entered the public imagination with the publication of Michael Lewis’ Moneyballin 2003. Of course, the term “Big Data” is nowhere to be found in the book, but that’s what the book was about – finding hidden patterns and insights within the reams of data collected during each and every major league baseball game.
One statistic that has been buried – well, buried isn’t right; ignored is more accurate – was about drafting college players over high school players. College players have a track record. They have statistics that can be measured, and they played against at least a half-decent level of competition:
“[Bill James] looked into the history of the draft and discovered that “college players are a better investment than high school players by a huge, huge, laughably huge margin.” The conventional wisdom of baseball insiders – that high school players were more likely to become superstars – was also demonstrably false. What James couldn’t understand was why baseball teams refused to acknowledge that fact.”
Pushing past gathering raw information and onto challenging preconceptions is at the heart of Big Data. So, too, is discovering truths that no one would have ever suspected before.
However, in order to gain these new insights and to challenge our misconceptions, we must find ways to access all of that data, hidden away in all of those proprietary applications and databases.
That’s not just a Big Data problem. It’s also a management problem, and it’s most certainly a storage problem.
Just how much data is out there? No one knows for sure, of course, but IBM’s Big Data estimates conclude that “each day we create 2.5 quintillion bytes of data.” The exponential growth of data means that 90 percent of the data that exists in the world today has been created in the last two years. “This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, e-commerce transaction records, and cell phone GPS coordinates, to name a few.”
To put the data explosion in context, consider this. Every minute of every day we create:
• More than 204 million email messages
• Over 2 million Google search queries
• 48 hours of new YouTube videos
• 684,000 bits of content shared on Facebook
• More than 100,000 tweets
• 3,600 new photos shared on Instagram
• Nearly 350 new WordPress blog posts
This volume of data could not be saved, collected and stored were it not for the fact that data storage is so incredibly cheap. Today, everything from tablets to desktops is sold with ever bigger hard drives. Why would you bother deleting anything when it’s so cheap and easy to store it?
Between 2000 and today, the cost of storage has plummeted from about $9/GB to a mere $.08/GB, and as soon as I typed that low price point, you can bet that downward price pressure has already made those numbers obsolete.
If you are a highly paid knowledge worker, it’s probably cheaper to store data than delete it, since the productivity lost while purging old files may well cost your organization more than the storage costs — unless you have to find something lost in this data maze for, say, regulatory compliance.
Data is collected from everywhere, but where is it stored? That’s the crux of the problem. It’s stored everywhere, as well. Typically, these data repositories – “data silos”– are application specific.
Big Data storage, then, is as much about managing data as about storing it.
In Big Data storage management, we’re encountering a problem we’ve dealt with many times before.
We haven’t yet figured out a workable Dewey Decimal system for data. We’re moving in the right direction, with such tools as hyperlinks and wikis. But most data in enterprise applications, email servers and social networks is not structured for easy sharing to other applications.
1. Unstructured data. There are two types of data in storage, structured and unstructured data. Structured data has a high degree of organization, and is typically stored in a relational database that can be easily searched.
Unstructured data is, obviously, not structured in any meaningful way, including such things as photographs, videos, MP3 files, etc. Unstructured data is difficult to search and analyze.
2. I/O barriers. If you’re dealing with something like mapping genomes, gathering information from the Mars Rover or running sophisticated weather simulations, the transaction volumes of these data sets challenge traditional storage systems, which don’t have enough processing power to keep up with the huge number of I/O requests.
Introduction to Why Innovation is The Most Critical Aspect of Big Data?
Innovation and creativity have always played prime roles in helping brands and companies succeed in the short and long term. The need for creative problems has increased dramatically because the kinds and challenges that brand managers face are getting more complex and complicated daily. When brands are innovative in their approach to solving hurdles, then they, more often than not, can resolve their issues faster and easier manner.
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Creativity is one of the most significant driving forces behind innovation because it allows individuals to challenge the existing situation and develop innovative, unique, and effective solutions. It will enable brands to break the shackles of normality, uniquely expand their brands, and set new rules and benchmarks within the industry.Why is Creativity such a Critical Element in Organizations?
Innovation and creativity are the most essential elements in any organization and will result in gains and success. By embracing creativity and exploring new territories, brands can reach new levels of productivity and growth within a company. When brands encourage employees to think outside the box and allow them to explore their talents, companies can continue to grow and discover solutions to many of their internal and external conflicts. Therefore, creativity is a sure-shot way brands can solve their problems. So whether brands want to develop a new strategy, creative thinking will get them ahead. Creativity, without a doubt, sets organizations apart from others and gives them a competitive edge in all aspects of the operation.
Creative ideas and innovative approaches can help brands in all possible manners from the point of view of customers, target groups, employees, or partners. Bringing a unique perspective to their plans not just encourages communication but also brings a unique manner of functioning and management within the company. Some of how companies can develop a culture of innovation include the following:Failing is a Part of the Learning Curve
Many brands are afraid to fail in any manner. This is because failure is considered a sign of weakness, and even when there is an opportunity, many companies hesitate to take a leap of faith. As against popular notion, successful brands have often failed several times before eventually achieving success in the true sense. Further, failure is also a form of freedom. This is because now the worst has happened. While failure on a large scale can be avoided, it is essential to face failure strategically.
As J.K. Rowling says, There is no way to live life without failing occasionally. You won’t fail only if you live so guardedly that you barely live, wherein you fail by default. Overcoming failure is possible when brand managers accept, annoy, dissect, and learn from it. As they say, failure is the stepping stone to success and growth. The bottom line is that brands must learn from their failure and, even if there are obstacles, remain positive, as a good attitude can go a long way in helping brands gain the success they wish to achieve eventually.Brand Managers must Learn to Lead from the Front, In all Situations
Running a big brand or company is no easy task, as it requires constant attention and hard work. So while there may be many highs and successes, management will also face many lows and challenges. Often, a brand is successful because of the efforts and hard work of the entire team rather than just a single individual. A brand manager, therefore, needs to act in a manner that will enable them to distribute the required tasks according to the capabilities and talents of the employees.
Another thing that brand managers must learn to do is learn from past experiences. Share stories about your campaigns so that you learn about what went right and what went wrong. Good communication between team members and employees will keep the team motivated, encouraged, and keen to perform despite all obstacles. In short, bring everyone together so that everyone participates in the success story of the brand/company.Always Remember to Reward Innovation and Incentives at Every Stage
Innovation is, therefore, the basis of success for brands and companies across sectors. Additionally, when companies are responsive and resilient, they have all the ingredients to stay ahead of their competition successfully. This is where big data analytics can help companies strategically achieve goals. By analyzing the information, brands can deal with change effectively. By gathering data on various aspects of the organization and making required changes, brands can move ahead on the path of progress in a much more successful fashion.
This has always been the goal of brand managers who want to take their companies ahead on the path of development, profitability, and progress. Data analytics that helps support decision-making is, therefore, an essential element in any company, though it has one flaw is that it still involves human intervention and interaction. As Big Data gets bigger and bigger, the human element cannot be ignored. Still, the fact remains that human beings have a limited span of attention, especially when it comes to processing vast amounts of data.
Unless and until brands can make sense of the vast amounts of data available, techniques and methods of Big Data are redundant. That is why professionals who can help companies to use Big Data properly are in prime demand across companies and sectors. It is these people who can help companies use the insight that Big Data has generated so that it can be used to create campaigns that not just help brands reach their goals and objectives but also empower and strengthen brand power among their customers as well.
As humans lack attention, removing the human element is the only way to make Big Data seamless. This is to say that the entire big data process has to become completely automated. So if a business deals with equipment, everything from manufacturing to delivery is done through machines. Machines will fix these hurdles despite errors, making human interaction minimum and needed only in extreme situations.
Being more responsive to the needs of the brand needs is an essential requirement if brands are to stay in business, especially with rising competition and increasing consumer demands on the other hand. There are many areas in that brands can optimize their data in real time. These include optimizing salaries based on profit outcomes and corresponding security policies that align with the risk of loss. Brands can make automated and data-driven decisions by continually updating the company’s data.
Data is an essential part of almost every sector around the globe. It has become an integral part of how brands and companies function. It is as important as capital and labor in today’s economy. As the amount of data increases daily, it is estimated that 5 quintillion bytes of data are generated every minute, which is set to increase daily. By making sense of this vast data, companies can expand and grow in many ways that would have seemed impossible until yesterday. The rate of expansion and growth has grown tremendously after the introduction of Big Data, and add to that the angle of innovation, companies can quickly gain a competitive edge. Investing in innovative big data technology is how brands can increase and improve profitability in the coming years.
Big data can be used to create value in five distinct ways. The first way is to ensure that data is transparent in all manners. By providing that data is transparent throughout the organization, it becomes possible for brand managers to gain insights from them simply and effectively. Data transparency is essential; this is the first step towards creating value through big data.
The second way to generate value in big data is to create and store transactional data in the digital medium. By storing information in this manner, brand managers can collect accurate and detailed information; this, in turn, helps them optimize details for many things, including product inventories to employees’ sick days. By doing this, companies can keep a tab on all the company’s functioning and, in the process, boost their performance and use existing opportunities. Through the use of Big data, companies are successful in making better decisions and managing their inventories in a better manner. By adjusting their goals and objectives to align with the big data objectives, brands can successfully empower and strengthen their brand image.
The third way big data can add value to a company is by helping them personalize their products and services according to their target audience. Personalization of products is essential in this competitive age, so it is important to tailor products and services to their needs. Every customer loves having a personal touch when buying products and services. Always remember that an engaged and loyal customer is one of the most significant assets of any company. Developing ingenious engagement methods is the need of the hour for all brands and companies that want to succeed. The bottom line is that the personalization of services will help brands retain their existing customer base and expand it profitably.
The fourth and final way big data can help companies is by assisting them to make better decisions. It can help them provide not just practical and superior services but also enhance after-sales support as well. For instance, with Big data, manufacturers can embed their products with sensors to understand customer needs and provide better after-sales service offerings, helping them stand out.
“Convergence of social, mobile, cloud and big data technologies presents new requirements – getting the right information to the consumer quickly, ensuring reliability of external data you don’t have control over, validating the relationships among data elements, looking for data synergies and gaps, creating provenance of the data you provide to others, spotting skewed and biased data.”
As technology develops, brands will need new methods to keep up with this rapid change to remain viable and effective. In short, brands can stay ahead of the competitive curve by effectively combining powerful analytics with the correct data. So the earlier they start down this road, the more chances they have for learning and using this medium effectively.
When coupled with innovation, big data can help bring a new era of productivity and consumer growth. Many studies have proven that investing in good analytical techniques can help brands to increase their operating gains by more than 60 percent. By offering numerous benefits to consumers and companies, innovation and big data are here to stay long. In addition, with a host of personal location devices that are gaining a lot of prominence, big data can open a host of opportunities for brands to connect and engage with clients personally and intimately.
In summary, achieving success in a competitive environment is challenging. But at the same time, it is incredibly fulfilling and satisfying. And one way a company can continue on the path of success is by investing in techniques and innovation that will help them strengthen their internal communication and external relationships with clients, customers, and stakeholders.Recommended Articles
This has been a guide to Why Innovation is The Most Critical Aspect of Big Data. Here we have discussed a brief overview of why is creativity such a critical element in organizations? and failing is a part of the learning curve. You may also have a look at the following articles to learn more –
Big data analytics is about to become a massive part of enterprise of operations in 2023
In the era of data and information,Big data analytics powering digital transformation
Digital transformation is a global phenomenon that is driving a technological revolution all over the world. The transformation will continue to grow as IaaS providers scamper to cover the ground and build data centers. Digital transformation goes hand in hand withTransformation from SaaS to iPaaS
SaaS has been around for quite some time and has helped businesses optimize their businesses on the cloud. Earlier, the integration of SaaS has made headlines since it was relatively a new concept. But in 2023, we might not be able to see any revolutionary contributions from it. This is where iPaaS comes to play. As businesses try to avoidBig data will help climate change research
Solid data and proof might put the raging climate change research to rest by backing up the views and predictions by the climate change organization. The data might reveal some interesting insights about what is going on. The presence of legitimate data exempted from human biases will productively benefit the climate change debate.Big data might be used in local stores
Almost 90% of the local businesses and enterprises are using data to generate productive insights from these tools. The use of data-as-a-service is becoming more commonplace and is predicted to grow by US$10.7 billion by 2023. Customers might encounter DaaS in the form of purchased music, videos, and image files from multiple sources online.The use of small data is on the rise
Large enterprises can save massive amounts of time by just evaluating the most vitalData fabric will be the foundation
As data becomes increasingly complex and digital business accelerates, data fabric will become the architecture that will support composable data and analytics in its various forms. Data fabric reduces the time for integration by 30%, and for development by 70% since the technology designs will draw on the ability to reuse and combine different data integration styles.Composable data and analytics
The goal of composable data and analytics is to use components from multiple data, analytics, and AI solutions for a flexible, user-defined, and usable experience that will enable leaders to connect data insights to business outcomes. Composing new applications from the packaged business capabilities of each promotes productivity and agility.Big data to search novel medical cures
It is a primary responsibility for businesses to invest in human welfare. So, the use of raging big data applications in innovating cures for novel diseases might increase. Many scientists hope that by consolidating all the medical records accumulated on the planet, the discovery of medical cures will become faster and sooner than ever imagined.The use of XOps
The goal of XOps is to achieve efficiencies and economies of large scales using the best practices of DevOps for efficiency, reliability, and reusability while reducing the duplication of technology and processes and enabling automation.Planning and forecasting
In the era of data and information, big data is no longer new to businesses and society. It is a known fact that via big data solutions, organizations can generate insights and make well-informed decisions, discover new market trends, and improve productivity. As the amounts of data continue to grow, organizations are looking for new innovative ways to optimize big data. One of the major relationships of big data analytics with businesses is that their dependence on the internet increases, along with the amount of data generated by the rapid development and evolvement of technology. In 2023, the global big data market powered by big data analytics trends attained US$208 billion. It is expected that the big data market is expected to reach US$250 billion by 2026, with a CAGR of 10%. In this article, we have listed some big data analytics hidden trends to get to the core of its evolution in 2023.Digital transformation is a global phenomenon that is driving a technological revolution all over the world. The transformation will continue to grow as IaaS providers scamper to cover the ground and build data centers. Digital transformation goes hand in hand with big data , AI, machine learning, and the Internet of Things (IoT). Machine learning and AI tools will continue to handle the data generated from the data analytics to operate systems, make sense of complex hidden relationships, and store and project insights beyond human chúng tôi has been around for quite some time and has helped businesses optimize their businesses on the cloud. Earlier, the integration of SaaS has made headlines since it was relatively a new concept. But in 2023, we might not be able to see any revolutionary contributions from it. This is where iPaaS comes to play. As businesses try to avoid data losses and disjointed information between departments and platforms, iPaaS may provide logical solutions and become the next best trend in 2023.Solid data and proof might put the raging climate change research to rest by backing up the views and predictions by the climate change organization. The data might reveal some interesting insights about what is going on. The presence of legitimate data exempted from human biases will productively benefit the climate change debate.Almost 90% of the local businesses and enterprises are using data to generate productive insights from these tools. The use of data-as-a-service is becoming more commonplace and is predicted to grow by US$10.7 billion by 2023. Customers might encounter DaaS in the form of purchased music, videos, and image files from multiple sources online.Large enterprises can save massive amounts of time by just evaluating the most vital data instead of entire lots of the generated data. This can be efficiently achieved if businesses shift from big data to small data. It can enable more streamlined, fast, and bandwidth-sparring innovations to take chúng tôi data becomes increasingly complex and digital business accelerates, data fabric will become the architecture that will support composable data and analytics in its various forms. Data fabric reduces the time for integration by 30%, and for development by 70% since the technology designs will draw on the ability to reuse and combine different data integration chúng tôi goal of composable data and analytics is to use components from multiple data, analytics, and AI solutions for a flexible, user-defined, and usable experience that will enable leaders to connect data insights to business outcomes. Composing new applications from the packaged business capabilities of each promotes productivity and chúng tôi is a primary responsibility for businesses to invest in human welfare. So, the use of raging big data applications in innovating cures for novel diseases might increase. Many scientists hope that by consolidating all the medical records accumulated on the planet, the discovery of medical cures will become faster and sooner than ever chúng tôi goal of XOps is to achieve efficiencies and economies of large scales using the best practices of DevOps for efficiency, reliability, and reusability while reducing the duplication of technology and processes and enabling chúng tôi increased use of predictive analytics has also boosted the availability of affordable applications in the market, for both BI platforms, like Qlik or Anaplan and standalone cloud services like Amazon Forest, which can help the users to easily integrate predictive analytics in the systems. These tools can be used for planning based on the generated forecast data to make intelligent and profitable decisions.
Digital Twin Technology helps produce a replica of the physical assets of a product or service in an industry. It is a clone of the physical product just in digital form. More likely, a virtual model of the physical process, this technology helps in analyzing the data, lends a platform to check the functioning beforehand so as to develop a solution for any potential problems. It also provides an insight into the stimulations with the help of real-time data thereby connecting the product digitally with its own blueprint. Starting from the development phase to the design and testing, every step is examined carefully with the help of digital twin technology. The technology acts as a proxy for the actual model. It has now become easier to question the gap in a business model by exploiting machine learning, cloud computing, and artificial intelligence along with digital twin technology. A new form of data analysis has come to fore with the real-time data by having a virtual model handy. The term came into being in 2002 and was named one ofHow Does It Work?
Firstly, smart components use sensors to collect the real-time data, working status and other operational data attached to the physical model. These components send the relevant data via a cloud-based system to the other side of the bridge where with the help of data analytics required insights are obtained. A consistent flow of data is a big positive in optimizing the outcomes. Moreover, a digital twin also integrates historical data from past machine usage to the current data. It is a predictive analysis of data even before it is put to use.Birth of Digital Twin Technology
NASA was the first to give birth to digital twin technology. The argument was how can one possibly mend or update or check on a machine in outer space where it is practically impossible to be physically present at any given point if time. Hence, they developed a virtual replica which can work from the desired place and can fetch real-time data. NASA uses digital twins to develop new recommendations, roadmaps, and next-generation vehicles and aircraft.Applications and Importance
A comprehensive collaboration of artificial intelligence, machine learning, and data analytics, digital twin predicts the issue before it occurs in the physical machine. It is like knowing the future and having the capability to mold it. With digital twin technology, minimum time and capital is invested in resolving any issue. There are comparatively lesser downtimes and overhead expenditures. It is an integrated way to optimize and monitor the performance virtually. Innovation in business accompanied by seamless customer services is yet another application of digital twin. It manages the customer operations and understands their needs. It is increasingly finding its applications in aircraft engines, locomotives, wind turbines, buildings and HVAC control systems, healthcare and retail.
Digital Twin Technology helps produce a replica of the physical assets of a product or service in an industry. It is a clone of the physical product just in digital form. More likely, a virtual model of the physical process, this technology helps in analyzing the data, lends a platform to check the functioning beforehand so as to develop a solution for any potential problems. It also provides an insight into the stimulations with the help of real-time data thereby connecting the product digitally with its own blueprint. Starting from the development phase to the design and testing, every step is examined carefully with the help of digital twin technology. The technology acts as a proxy for the actual model. It has now become easier to question the gap in a business model by exploiting machine learning, cloud computing, and artificial intelligence along with digital twin technology. A new form of data analysis has come to fore with the real-time data by having a virtual model handy. The term came into being in 2002 and was named one of Gartner’s Top 10 Strategic Technology Trends for 2023 . Much with the help of Internet of things, it became cost-effective to implement it.Firstly, smart components use sensors to collect the real-time data, working status and other operational data attached to the physical model. These components send the relevant data via a cloud-based system to the other side of the bridge where with the help of data analytics required insights are obtained. A consistent flow of data is a big positive in optimizing the outcomes. Moreover, a digital twin also integrates historical data from past machine usage to the current data. It is a predictive analysis of data even before it is put to chúng tôi was the first to give birth to digital twin technology. The argument was how can one possibly mend or update or check on a machine in outer space where it is practically impossible to be physically present at any given point if time. Hence, they developed a virtual replica which can work from the desired place and can fetch real-time data. NASA uses digital twins to develop new recommendations, roadmaps, and next-generation vehicles and aircraft. According to the NASA’s leading manufacturing expert , John Vickers, the most valuable prospect of digital twin is to be able to build, check and test a technology or product in a virtual environment. Another example of digital twin technology is 3D modeling. One can analyze how the physical model would turn out to be a digital companion. The term ‘Device Shadow’ is also used to speak for digital twin technology.A comprehensive collaboration of artificial intelligence, machine learning, and data analytics, digital twin predicts the issue before it occurs in the physical machine. It is like knowing the future and having the capability to mold it. With digital twin technology, minimum time and capital is invested in resolving any issue. There are comparatively lesser downtimes and overhead expenditures. It is an integrated way to optimize and monitor the performance virtually. Innovation in business accompanied by seamless customer services is yet another application of digital twin. It manages the customer operations and understands their needs. It is increasingly finding its applications in aircraft engines, locomotives, wind turbines, buildings and HVAC control systems, healthcare and retail. Internet of things acts as a base for the digital twin technology. Hence, in the near future, most of the IoT platforms will adopt digital twin technology. GE uses the digital environment to inform the configuration of each wind turbine prior to construction. It has implemented over 500,000 digital twins. German packaging systems manufacturer, Optima , digitally mapped and examined its transport system using digital twin technology by Siemens. The Singapore government, in association with the 3D design software giant Dassault Systèmes, is building a virtual model of the country with an aim to optimize and augment the urban planning process. For every asset and product, there is a virtual replica of the same made functional via cloud services which consistently utilizes the operational data to produce better results and provide extra insights. It won’t be long that more and more companies would want to adopt digital twin to survive the competitive market and have favorable business outcomes.
by Yaniv Vardi
The industrial revolution occurred in the 18th century, ushering in the industrial age, which continued through the 20th century. On its heels, began the information age, which is ongoing, according to most experts. While the industrial age focused on automation and mass manufacturing, the information age is based on today’s extensive communication infrastructure, which has enabled access to virtually endless information.
The Evolution of Data
Historically, products were classified into tangible products and non-tangible services. Marketing theories have narrowed this distinction (tangible and non-tangible goods), to what is known as the “goods and services continuum,” a model in which some products are an obvious combination of purely tangible goods and associated services.
Until recently, information was considered too abstract a commodity to be classified as either a good or a service. Even intangible services were thought to require some physical presence, whether in their delivery or effect, to actualize their utility. Platonic information – or data as we so commonly refer to it today – was generally reserved for matters of education, statecraft or religious studies.
Yet, as businesses and technologies evolved, the data produced on sales, profit margins and trends began to influence corporate decisions, bringing information to the enterprise. While this general connection became clear, the specific connection between any specific parcel of information and its impact on business decisions remained as nebulous as ever.
Still, confident in the belief that within the knot of data there was somewhere a thread connecting information to decision, prospectors became convinced of incredible latent value. What the California Gold Rush was physically, the Silicon Valley Data Rush was virtually. The treasure was there, simply waiting to be mined.
The Data Rush was further enabled by the proliferation of connectivity, giving birth to the “always on” culture, which, when combined with social networks, GPS, digitization, online searches and ecommerce transactions, has created a mass of information, commonly coined “Big Data.”
Big Data has become so big and so pervasive that it’s spun an entirely new economic market. Many argue that while Wall Street rushes to confer enormous valuations upon Big Data enterprises, the information they collect has no inherent value. (If you think back to Facebook’s IPO, consider the massive disparity in analyst valuations.)
With the rise of information as a product, it’s worth asking “Are we witnessing a fundamental rearrangement of the global economy? Is data replacing physical goods and services as the premier engine of economic growth?”
While some may disagree, I respond uncompromisingly in the negative. The value of the data economy must come in its potential to enhance conventional markets, even if it’s a long and windy road from A to Z. Any value claimed beyond this, I contend, is nothing more than hot air – a bubble pumped up on animal spirits and undisciplined speculation.
Mark my words, the real engine of tomorrow’s global economy will be where Big Data and physical markets meet.
Enter the Internet of Things
Today, we are beginning to understand the incredible value that can be realized by coupling highly contextualized data with existing products and processes. The Internet of Things (IoT) – wherein traditionally non-responsive objects become dynamic interfaces constantly collecting, communicating and adjusting to data – has opened the path for organizations to zero in on the hidden points of micro-friction in their processes and thus improve efficiencies.
The Internet of Things is the paradigm of the type of value-generating convergence of Big Data and physical markets to which I refer. At the heart of the Internet of Things, are (weight, temperature, energy, etcetera) sensors and increasingly agile, quick, and sophisticated data processing techniques and tools.
Increasingly, every human and machine act is being catalogued and examined for any and all useful revelations. Consider transactional data, which provides customer insights and purchasing trends, or social data taken from social media. These datasets are being leveraged to evermore successful effect by enterprises looking to create real value throughout their operations – from internal efficiencies through commercialization and marketing strategies.
Deloitte has highlighted key trends in analytics that will influence the business world in the coming years, in what they coin “the next evolution”. The growth of IoT will similarly have a high impact on businesses in the coming years, affecting consumer products and business models.
Aggregation of data and data analysis will facilitate the creation of new products, markets and services. Analytics will expand across all facets of enterprise, with businesses increasingly investing in Big Data infrastructure and technologies. Such data-driven insights will support decision-making processes.
What we’re witnessing is not the replacement of physical markets with digital markets, but the perfection of physical markets through digital markets.
According to BI Insider, while there were 10 billion devices connected to the internet as of 2024, the volume of connected devices will grow to reach 34 billion by 2023. Fueling this bonanza is the nearly $6 trillion expected to be spent on IoT solutions through 2023.
Big Data Explodes
So how big exactly is this data?
According to IBM, we create 2.5 quintillion (a quintillion has 18 zeros) bytes of data each day. Sales of Big Data and business analytics applications, tools, and services reached $122 billion in 2024 and are projected to increase over 50% to reach $187 billion by 2023, according to IDC.
Services related revenues are projected to account for over half of this market, followed by software and business analytics. The manufacturing industry will be the largest consumer of Big Data and associated technologies, accounting for close to $23 billion of the aforementioned Big Data sales.
The immenseness of the data produced daily creates challenges, as enterprises and organizations scramble to translate the data into value and data-driven business models. Data scientists and analysts are in such demand that analysts are warning of talent gaps in the near future. A similar demand is projected for managers who know how make data-driven decisions on processes and strategies.
Harness the Power of IoT and Big Data
The Big Data created and stored in an enterprise is unstructured. Rapid analytics are required in order to create the practical insights, which can improve margins and efficiencies. Platforms such as the open source Hadoop or IBM’s Watson offer data processing and analytical tools, which can identify trends, predict behaviors, detect patterns and enhance responsiveness – forging new opportunities for businesses, and improving relationships with customers.
Similarly, IoT-enabled operations analytics platforms identify trends in operations and manufacturing, enabling companies to improve their efficiencies, more accurately manage controls, better track inventory and ultimately pad their bottom lines. Intelligent energy monitoring and analysis, for example, can detect anomalies and automatically generates actionable energy insights to reduce consumption and machine downtime while eliminating failures altogether.
New Economic Model
Put simply, Big Data and physical markets meet through the Internet of Things, and this convergence drives profit. Integration of data-driven decisions and processes as part of an enterprise’s physical operations creates remarkable value via improved efficiencies, increased productivity, and novel product offerings.
While physical markets aren’t going anywhere and the rise of the data economy does not signal a new world order, there can be no doubt that the Data Rush has altered the face of the commercial landscape forever, for the better.
Yaniv Vardi is the CEO of Panoramic Power, a leader in device level energy monitoring and performance optimization.
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