6) Using Health Data For Informed Strategic Planning – The use of big data in healthcare allows for strategic planning thanks to better insights into people’s motivations. Care managers can analyze check-up results among people in different demographic groups and identify what factors discourage people from taking up treatment.
How big data is changing?
7. Reduced costs – Big data has the power to reduce business costs. Specifically, companies are now using this information to find trends and accurately predict future events within their respective industries. Knowing when something might happen improves forecasts and planning.
Planners can determine when to produce, how much to produce and how much inventory to keep on hand. A good example is inventory expenses. It’s expensive to carry inventory; there is not only an inventory carrying cost but also an opportunity cost of tying up capital in unneeded inventory. Big data analysis can help predict when sales will happen and thus when production needs to occur.
Further analysis can reveal the optimal time to purchase inventory and even how much inventory to keep on hand. Businesses need to embrace big data if they want to achieve more. It won’t be long before businesses that haven’t embraced big data find themselves left behind.
What is main characteristics of big data in healthcare?
In Healthcare BigData analytics, the big data is described by three primary characteristics: volume, velocity and variety. Over time, health-related data will be created and accumulated continuously, resulting in an incredible volume of data. The already daunting volume of existing healthcare data includes personal medical records, radiology images, clinical trial data FDA submissions, human genetics and population data genomic sequences, etc.
- Newer forms of big data, such as 3D imaging, genomics and biometric sensor readings, are also fueling this exponential growth.
- Fortunately, advances in data management, particularly virtualization and cloud computing, are facilitating the development of platforms for more effective capture, storage and manipulation of large volumes of data.
Data is accumulated in real-time and at a rapid pace, or velocity. The constant flow of new data accumulating at unprecedented rates presents new challenges. Just as the volume and variety of data that is collected and stored has changed, so too has the velocity at which it is generated and that is necessary for retrieving, analyzing, comparing and making decisions based on the output.
- Most healthcare data has been traditionally static—paper files, x-ray films, and scripts.
- Velocity of mounting data increases with data that represents regular monitoring, such as multiple daily diabetic glucose measurements (or more continuous control by insulin pumps), blood pressure readings, and EKGs.
Meanwhile, in many medical situations, constant real-time data (trauma monitoring for blood pressure, operating room monitors for anesthesia, bedside heart monitors, etc.) can mean the difference between life and death. Future applications of real-time data, such as detecting infections as early as possible, identifying them swiftly and applying the right treatments (not just broad-spectrum antibiotics) could reduce patient morbidity and mortality and even prevent hospital outbreaks.
Already, real-time streaming data monitors neonates in the ICU, catching life-threatening infections sooner. The ability to perform real-time analytics against such high-volume data in motion and across all specialties would revolutionize healthcare. Therein lies variety. As the nature of health data has evolved, so too have analytics techniques scaled up to the complex and sophisticated analytics necessary to accommodate volume, velocity and variety.
Gone are the days of data collected exclusively in electronic health records and other structured formats. Increasingly, the data is in multimedia format and unstructured. The enormous variety of data—structured, unstructured and semi-structured—is a dimension that makes healthcare data both interesting and challenging.
- Structured data is data that can be easily stored, queried, recalled, analyzed and manipulated by machine.
- Historically, in healthcare, structured and semi-structured data includes instrument readings and data generated by the ongoing conversion of paper records to electronic health and medical records.
Historically, the point of care generated unstructured data: office medical records, handwritten nurse and doctor notes, hospital admission and discharge records, paper prescriptions, radiograph films, MRI, CT and other images. Already, new data streams—structured and unstructured—are cascading into the healthcare realm from fitness devices, genetics and genomics, social media research and other sources.
But relatively little of this data can presently be captured, stored and organized so that it can be manipulated by computers and analyzed for useful information. Healthcare applications in particular need more efficient ways to combine and convert varieties of data including automating conversion from structured to unstructured data.
The structured data in EMRs and EHRs include familiar input record fields such as patient name, data of birth, address, physician’s name, hospital name and address, treatment reimbursement codes, and other information easily coded into and handled by automated databases.
- The need to field-code data at the point of care for electronic handling is a major barrier to acceptance of EMRs by physicians and nurses, who lose the natural language ease of entry and understanding that handwritten notes provide.
- On the other hand, most providers agree that an easy way to reduce prescription errors is to use digital entries rather than handwritten scripts.
The potential of big data in healthcare lies in combining traditional data with new forms of data, both individually and on a population level. We are already seeing data sets from a multitude of sources support faster and more reliable research and discovery.
If, for example, pharmaceutical developers could integrate population clinical data sets with genomics data, this development could facilitate those developers gaining approvals on more and better drug therapies more quickly than in the past and, more importantly, expedite distribution to the right patients.
The prospects for all areas of healthcare are infinite. Some practitioners and researchers have introduced a fourth characteristic, veracity, or ‘data assurance’. That is, the big data, analytics and outcomes are error-free and credible. Of course, veracity is the goal, not (yet) the reality.
- Data quality issues are of acute concern in healthcare for two reasons: life or death decisions depend on having the accurate information, and the quality of healthcare data, especially unstructured data, is highly variable and all too often incorrect.
- Inaccurate “translations” of poor handwriting on prescriptions are perhaps the most infamous example).
Veracity assumes the simultaneous scaling up in granularity and performance of the architectures and platforms, algorithms, methodologies and tools to match the demands of big data. The analytics architectures and tools for structured and unstructured big data are very different from traditional business intelligence (BI) tools.
They are necessarily of industrial strength. For example, big data analytics in healthcare would be executed in distributed processing across several servers (“nodes”), utilizing the paradigm of parallel computing and ‘divide and process’ approach. Likewise, models and techniques—such as data mining and statistical approaches, algorithms, visualization techniques—need to take into account the characteristics of big data analytics.
Traditional data management assumes that the warehoused data is certain, clean, and precise. Veracity in healthcare data faces many of the same issues as in financial data, especially on the payer side: Is this the correct patient/ hospital/ payer/ reimbursement code/dollar amount? Other veracity issues are unique to healthcare: Are diagnoses/treatments/prescriptions/procedures/outcomes captured correctly? Improving coordination of care, avoiding errors and reducing costs depend on high-quality data, as do advances in drug safety and efficacy, diagnostic accuracy and more precise targeting of disease processes by treatments.
- But increased variety and high velocity hinder the ability to cleanse data before analyzing it and making decisions, magnifying the issue of data “trust”.
- The ‘4Vs’ are an appropriate starting point for a discussion about big data analytics in healthcare.
- But there are other issues to consider, such as the number of architectures and platforms, and the dominance of the open source paradigm in the availability of tools.
Consider, too, the challenge of developing methodologies and the need for user-friendly interfaces. While the overall cost of hardware and software is declining, these issues have to be addressed to harness and maximize the potential of big data analytics in healthcare.
Why small data vs big data for healthcare?
Why clinicians prefer Small Data to Big Data for healthcare prediction models – The big difference between big and small data is in big data large volumes of data are analyzed for patterns while small data looks at an individual’s historical data to develop models for predictions and futuristic treatment. Healthcare data is strictly governed by compliance. Here’s how you can ensure HIPAA compliance on the healthcare cloud. While big data has been at the forefront in healthcare technology for some time now, clinicians are often turning to small data to efficiently manage patient care.
Small data helps them by providing quick input on allergies, times for blood cultures, missed appointments, and so forth, which are tactical in nature but extremely important inefficient patient care. Big data for example can say, X number of patients were admitted in the ER during a certain period of time.
Can big data quickly identify how often or why Mr. or Mrs. John was admitted to the ER last month? Small data is providing big insights for the individual. An app for managing pain for example quietly collects data about the individual, such as a fitness tracker, and can be presented to the individual and his clinician.
What is the market size for medical data?
REPORT COVERAGE – An Infographic Representation of Medical Devices Market To get information on various segments, share your queries with us The global medical devices market research report provides a detailed analysis of the market and focuses on key aspects such as leading companies, products, and end-users.
What will replace big data in future?
The terminology ‘Big data’ should be replaced as ‘Large data’, because we study the large data sets instead of the big numbers. What will replace ‘Big Data’ as a hot buzzword?
Is big data really the future?
The increasing velocity of big data analytics – The days of exporting data weekly, or monthly, then sitting down to analyze it are long gone. In the future, big data analytics will increasingly focus on data freshness with the ultimate goal of real-time analysis, enabling better-informed decisions and increased competitiveness. Materialized tables were described as the mid-point between streams & tasks and materialized views Snowflake, for example, announced Snowpipe streaming at this year’s summit. The company has refactored their Kafka connector and made it so that when data lands in Snowflake it is queryable immediately resulting in a 10x lower latency.
What are the 7 V’s of big data?
7. Value – Value is the end game. After addressing volume, velocity, variety, variability, veracity, and visualization — which takes a lot of time, effort, and resources —, you want to be sure your organization is getting value from the data.
What are the 5 V’s of big data?
What are the 5 V’s of Big Data? | Teradata is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.
Volume: the size and amounts of big data that companies manage and analyze Value: the most important “V” from the perspective of the business, the value of big data usually comes from insight discovery and pattern recognition that lead to more effective operations, stronger customer relationships and other clear and quantifiable business benefits Variety: the diversity and range of different data types, including unstructured data, semi-structured data and raw data Velocity: the speed at which companies receive, store and manage data – e.g., the specific number of social media posts or search queries received within a day, hour or other unit of time Veracity: the “truth” or accuracy of data and information assets, which often determines executive-level confidence
The additional characteristic of variability can also be considered:
Variability: the changing nature of the data companies seek to capture, manage and analyze – e.g., in sentiment or text analytics, changes in the meaning of key words or phrases
: What are the 5 V’s of Big Data? | Teradata
Why is bigger data better?
Why is big data important? – Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns and take other actions that, ultimately, can increase revenue and profits. Businesses that use it effectively hold a potential competitive advantage over those that don’t because they’re able to make faster and more informed business decisions.
- For example, big data provides valuable insights into customers that companies can use to refine their marketing, advertising and promotions in order to increase customer engagement and conversion rates.
- Both historical and real-time data can be analyzed to assess the evolving preferences of consumers or corporate buyers, enabling businesses to become more responsive to customer wants and needs.
Big data is also used by medical researchers to identify disease signs and risk factors and by doctors to help diagnose illnesses and medical conditions in patients. In addition, a combination of data from electronic health records, social media sites, the web and other sources gives healthcare organizations and government agencies up-to-date information on infectious disease threats or outbreaks.
In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids. Financial services firms use big data systems for risk management and real-time analysis of market data. Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes. Other government uses include emergency response, crime prevention and smart city initiatives.
These are some of the business benefits organizations can get by using big data.
Why is a big data set better?
More Data = More Features – Let’s start in the world of data science. The first and perhaps most obvious way in which more data delivers better results in data science is the ability to expose more features to feed your data, science models. In this case, accessing and using more data assets can lead to “wider datasets” containing more variables.
- Uniting more datasets into one helps the feature engineering process in two ways.
- First, it gives you more raw variables that can be used as features.
- Second, it gives you more fields that you can combine to make derived variables,
- It is important to note that the brute force approach of throwing more features at a model is NOT the objective.
That would be over-engineering the model. The aim is to explore as many features as possible to find their fit for the problem at hand and choose the best parts,
What is the fastest growing segment of medical devices?
Medical Devices Market Segmentation – The medical devices market is segmented by type of device, by type of expenditure, by end user, and by geography. By Type Of Device – The medical devices market is segmented by type of device into
- a) In-Vitro Diagnostics
- b) Dental Equipment
- c) Ophthalmic Devices
- d) Diagnostic Equipment
- e) Hospital Supplies
- f) Cardiovascular Devices
- g) Surgical Equipment
- h) Patient Monitoring Devices
- i) Orthopedic Devices
- j) Diabetes Care Devices
- k) Nephrology And Urology Devices
- l) ENT Devices
- m) Anesthesia And Respiratory Devices
- n) Neurology Devices
- o) Wound Care Devices
The in-vitro diagnostics market was the largest segment of the medical devices market segmented by product, accounting for 15.7% of the total in 2020. Going forward, the hospital supplies segment is expected to be the fastest growing segment in the medical devices market segmented by product, at a CAGR of 10.8% during 2020-2025.
- a) Public
- b) Private
The public expenditure market was the largest segment of the medical devices market segmented by application, accounting for 51.7% of the total in 2020. Going forward, the public expenditure segment is expected to be the fastest growing segment in the medical devices market segmented by application, at a CAGR of 8.2% during 2020-2025.
- a) Hospitals And Clinics
- b) Homecare
- c) Diagnostics Centers
The hospitals and clinics market was the largest segment of the medical devices market segmented by end-user, accounting for 88.7% of the total in 2020. Going forward, the diagnostics centers segment is expected to be the fastest growing segment in the medical devices market segmented by end-user, at a CAGR of 9.2% during 2020-2025.
- • China
- • India
- • Japan
- • Australia
- • Indonesia
- • South Korea
- • USA
- • Brazil
- • France
- • Germany
- • UK
- • Russia
- o Asia Pacific
o North America
o South America
o Western Europe
o Eastern Europe
o Middle East o Africa
North America was the largest region in the global medical devices market, accounting for 39.7% of the total in 2020. It was followed by the Asia Pacific, Western Europe and then the other regions. Going forward, the fastest-growing regions in the medical devices market will be South America and, Africa where growth will be at CAGRs of 11.1% and 10.4% respectively.
What is the fastest growing medical device market?
Medical Device Contract Manufacturing Market – IVD is the fastest growing segment.
Why is big data dying?
For more than a decade now, the fact that people have a hard time gaining actionable insights from their data has been blamed on its size. “Your data is too big for your puny systems,” was the diagnosis, and the cure was to buy some new fancy technology that can handle massive scale.
Of course, after the Big Data task force purchased all new tooling and migrated from Legacy systems, people found that they still were having trouble making sense of their data. They also may have noticed, if they were really paying attention, that data size wasn’t really the problem at all. The world in 2023 looks different from when the Big Data alarm bells started going off.
The data cataclysm that had been predicted hasn’t come to pass. Data sizes may have gotten marginally larger, but hardware has gotten bigger at an even faster rate. Vendors are still pushing their ability to scale, but practitioners are starting to wonder how any of that relates to their real world problems.
Why big data is failing?
Lack of Objectives – One of the most common reasons big data projects fail is a lack of clear objectives. Without a clear goal, it can be challenging to determine what data you need to collect and how to use it effectively. Make sure that you have a clear idea of what you want to achieve with your project before you begin, and be sure to communicate this to all of the stakeholders involved.
What is the next big thing after big data?
What is the next big thing after Big data? – Several sources claim that Artificial Intelligence (AI) will be the next big thing in technology, and we believe that Big Data will be as well.
Will big data go away?
Everything will be big data, so it won’t need a special name – Photo by Markus Spiske on Unsplash Big data is a great marketing term but in reality, that’s all that it is. It’s a term used to excite business executives to make them feel like Google or Amazon. The reality is that big data doesn’t mean anything and its meaning is only going to reduce.
- As companies become more familiar with data processing and service providers abstract away more complexity, big data will just become data.
- Big data engineers will just become data engineers and any data engineer worth their salt will be handling what we now call “big data processing”.
- Fear not though, this doesn’t mean your big data knowledge is obsolete, just that its name might not mean as much as it once did.
No. It isn’t dead at all. In fact, it’s only going to become more prominent. By 2025 it’s predicted that the global “data sphere” will be 175ZB (zettabytes), up from 50ZB today. All of this data is going to need crunching one way or another so how can big data be dying? The answer is that “big data processing” itself isn’t going anywhere, it will just become the norm.
Because of this, we’ll no longer be calling it big data and need specialised “big data engineers”. The complexity and scaling behind big data applications will be abstracted away by cloud providers like Amazon so that all data engineering could in effect be “big data engineering”. This isn’t a new phenomenon either, it started back in the early days of Hadoop.
When Facebook started out using Hadoop to manage its huge data sets, they found writing MapReduce jobs long, laborious and expensive. This is because at the time MapReduce jobs required programming, so they built Hive. Hive is SQL on Hadoop. Facebook abstracted the complexity away from writing MapReduce jobs and they became simple SQL queries.
This meant that anyone who knew SQL had the ability to build big data applications, not just big data engineers. Fast forward to today and you have on demand scaling data solutions like Amazon Redshift or Google Big Table. Most modern services cater to small data but can be easily scaled to work for big data.
You could use Amazon S3 as the data store for a “small data” application but if your data footprint grows then you can still use S3 as a data lake as it’s effectively an unlimited data store. There are even data processing tools like Athena that sit on top of S3 now making it even more compelling.
- None of us really know what the next big marketing buzz word will be.
- Data Science is already following a similar path.
- At the minute it’s the poster child of the data world but again, as its complexity gets abstracted away then the need for specialised data scientists will reduce and its buzz will dwindle.
The important thing to take away is not that big data processing is dying, but the term “Big Data” itself is dying. Behind all of the abstractions, big data processing techniques will still be there. We’ll still be using horizontally scaling clusters, we’ll still be reducing data ingestion latency and processing petabytes of data.
- None of this is going away, these techniques are just being hidden so they are more accessible to everyone.
- We will still need data engineers who are skilled in data extraction and manipulation.
- We’ll also still need data scientists and analysts who can build predictions and provide reporting.
- What will be missing is the ability of the engineers to build a robust and scalable data lake from the ground up, as this can be deployed with the push of a button.
The data scientists won’t need to understand as much of the “nitty-gritty” maths either. Instead, they’ll only need to know which models are required and what data to provide. The complexity of training and deploying the model will be abstracted away and provided as a cloud service.
Why is big data Overhyped?
2. Overplaying the value of harvesting greater volumes of data – Another hyped factor is that harvesting more data will create more value. This is not true. Data with a certain amount of history is always more beneficial than large, freshly-acquired data sets.
- Instead of mining more data, which can ultimately prove futile in its application to produce desired results, even less data with more history will prove beneficial.
- In this regard, Michael Jordan, a professor in the University of California, Berkeley, a respected authority on machine learning, points out that the combination of results that a humungous volume of data can produce continues to grow exponentially and therefore cannot be accurate.
Further, as a possible solution to this flaw in the technology, he adds that ‘error bands’ could be introduced in all the predictions that are based on Big Data analytics. This will, in turn, improve accuracy in prediction.3. Lack Of Standard Model For Repeatability: while Data Scientists can create insights based on results derived from this technology, one needs an entire operating model to apply and put to use the collected data and the analytics in a repeatable manner.
One possible solution could be to embed Artificial Intelligence with Big Data Analytics, in its application. Applying experience and knowledge into insights is another way in which results from Big Data Analytics can be implemented effectively. In the long-term, it could be said that Big Data, despite being a technology that needs improvement, cannot be swept off existence.
As Jordan himself points out – “the field will continue to progress and move forward, because it’s real, and it’s needed.” As improvements are constantly researched and introduced, Big Data will continue to remain the most sought-after technology in a majority of industries for a long time to come.
What will be the data size in 2030?
“Leveraging exponential technology to tackle big goals and using rapid iteration and fast feedback to accelerate progress toward those goals is about innovation at warp speed. But if entrepreneurs can’t upgrade their psychology to keep pace with this technology, then they have little chance of winning this race.” ― Peter H.
Diamandis, Bold: How to Go Big, Create Wealth and Impact the World It’s still Day 1 for data. Companies, governments, non-profits around the world are already extracting a whole lot of value from data. But really, compared to the things that are coming, today is really just Day 1, Data is growing exponentially, as is our ability to extract knowledge from it.
If you imagine the amount of data available to us as an apple, then by 2030, this apple has turned into a soccer ball. By 2050, it’s gonna be the size of an entire soccer field! But what really does that mean for you and me? What exactly contributes to this kind of data growth? Will it mean the value we can extract from the data will grow linearly with the amount of data? As I was still a bit puzzled about the implications, I decided to take a short tour around the future data universe.
- This article provides what I personally think the future of data will bring, based on the growing amount of data, our ability to extract value from it, and what research tells us about it.
- I do truly believe the future of data will shape any industry, period.
- If I take any sample industry, take a look at Porters five (or six) forces at work, and ask myself what the impact of data will be on them then it seems clear to me, that every single industry will look very different in 10 or 20 years, shaped by the impact of data.
So for you, the question is not whether to grab this “opportunity” or not, but what you are going to do about it because if not, you will be disrupted by it. Alright, so data is growing quickly. Big deal Technology will adapt and our company will simply follow in on the lead.
- Only, that it will be too late.
- Way too late.
- Data is not just growing quickly, according to the current forecasts, it’s probably growing exponentially! To add to that, our ability to extract information from data, the computation power is growing exponentially as well! That’s the problem with exponential growing technologies, you won’t notice until it’s too late.
“today we live in a world that is global and exponential. The problem is that our brains — and thus our perceptual capabilities — were never designed to process at either this scale or this speed. Our linear mind literally cannot grok exponential progression.” ― Peter H.
- Diamandis, Bold: How to Go Big, Create Wealth and Impact the World Let me make this even clearer: In 6 years, a single company will face four times the data it had available today.
- It will be able to get predictions on the data it has today 8 times faster/ cheaper.
- If the company analyses the data of today in 6 years, it will take them half the time it took them today.
Most of the new data will be real-time, it will be event/ behavioral kind of data, not “state” data. A lot of the data will be in image form A lot of data will be on edge devices, a lot of computation will be done there as well. And to top that, a lot of freely available technologies to deal with all these changes will flood the open-source market. Image by Sven Balnojan. The thing is, current data forecasts only cover parts of the future. But if you extrapolate the data, you will notice that the amount of data already is doubly roughly every 3 years and is forecasted to do so for the next 5 years. I do believe, that a lot of underlying forces will continue to carry this trend. The forces I see are mostly:
- The development of edge devices which are forecasted to reach a number of 41,6 billion connected IoT devices by 2025,
- Smartphone adoption rates will go close to 100%, currently, they are at 44%,
- Internet adoption rate will go close to 100%, currently, it is at 59% with large projects from Google & Facebook aiming to provide internet to the whole world.
- The exponential growth of a lot of underlying technologies like data storage, computation that produces data, cost of edge devices etc. All of which are all essentially related to Moore’s Law.
The best forecast I could find is from SeaGate which you can find here. IDC SeaGate DataAge Whitepaper, Here’s what the data looks like if you fit an exponential to it. That’s just the simple forecast by IDC & SeaGate. Image by Sven Balnojan. Next, let’s look at the exponential continued to the year 2030. Forecast by exponential fitting. Image by Sven Balnojan. Finally, the really interesting question is, how will things possibly look like in 2050? We’re in the Data Stone Age! Image by Sven Balnojan. Notice this describes the data, not the data stored. But really do extract value from data it’s actually not that important to store it, but more to extract the information, make a decision, act on it and then discard the data.
- By 2030 we will have around 572 Zettabytes of data, which is round about 10 times more than today.
- By 2050 we will have 50,000–500,000 Zettabytes, which is 1,000–10,000 times bigger (forecast by exponential continuation).
- By 2025, more than 50% of the data will be on the edge
- By 2025, more than 50% of the data will be real-time, a trend that will continue to hit probably close to 90% by 2050
- By 2025, 80–90% of the data will be behavioral/transactional or what I would call it “event data”, a trend which again will probably stay at 90–100%.
On the other side of things is our ability to extract information from these massive amounts of data. As far as I can tell Moore’s Law is still going strong, although some people think it has hit the final roadblock along the way, Still, the density of microprocessors on a given chip has been doubling roughly every 18 months, providing us all with edge devices, smartphones, and amazing computers for bargain prices.
Another trend in the same direction is the development of GPUs, which from 1996 to 2020 actually did follow an exponential growth curve as well, GPUs are not just used to produce graphics, as graphics usually mean we need to do a lot of matrix multiplication and addition GPUs are optimized for just that.
Turns out, this is exactly what today’s data analysis also needs, lots of matrix maths. In particular, all of the deep learning, AI, and machine learning fundamentally is about adding and multiplying matrices which is the reason our ability to extract information from data currently has a lot to do with the exponential growth of the computation power of GPUs.
- But that is not all, the emergence of TPUs, chips specifically designed for machine learning will continue to push these trends further faster and faster.
- TPUs essentially do the same thing as GPUs but are much more energy-efficient thus pushing down the prices,
- Finally, there’s the exponential growth of quantum computers, which can be measured by ” Rose’s Law “, which has held pretty well for now 17 years.
Rose’s Law says that the number of Qubits, the bits on a quantum chip, will double roughly every year. While quantum machine learning is still largely theoretical, I have no doubt that quantum data analysis will come into existence as an everyman’s tool in the next decade.
- Socio-demographic data like your age, the place you live at, your income, etc.
- Behavioral data, like whether you bought certain items this year, whether you opened an e-mail or clicked on a link.
But I really think the distinction between “state and event” data is much better. Let’s define a “state” as “The condition of any system at any point in time.” (That’s kind of the thermodynamic definition of state) A reasonable definition of an event could be “The transition of a system from condition A → condition B” So if I’m the system, then me buying an item on amazon.com looks like this for a guy named Sven:
- Today 12 am; Nothing bought yet; State: “non-buyer”; Events: none.
- Today 1 pm; Just clicked on the checkout button on amazon.com State: “non-buyer”; Event: “Sven buys new item”
- Today 1:01 pm; State: “buyer”
Now the fun part is, that of course, state data and event data are really equivalent, Kind simply two ways of looking at the world, because if I give you the series:
- “Here is the event at 1 pm today: Sven buys new item”, then you can tell me exactly what state Sven was before and after that event.
- “Here are the states: at 12 am Sven was a non-buyer, at 1:01 pm he was a buyer”, then you can also tell me, that Sven buys something at 1 pm.
So if the two kinds of data are equivalent, why does it matter? Because they are only equivalent in theory! In reality, you either:
- Don’t have the “state” data for any given point in time => thus are not able to get to the events (because you wouldn’t know that Sven was a non-buyer at 12 am)
- Don’t have all the events, but just a very small portion of it => are not able to tell the state, but just what really happened today.
Why is this important for us? Because a lot of companies have built large data analytics capabilities around state data, yet in 10 years, 99% of all data will be event data! The only good part about it? A great study by Martens & Provost gives some hints that using event data is actually a highly profitable thing to do.
By 2025, more than 50% of the data will be collected on the edge. On 41,6 billion IoT devices, including only round about 6 billion smartphones. Much more will come from sensors, cameras, and the likes. Of course, your smartwatch will also be on this list. On top of that, edge devices without an internet connection will be out there.
All of these things already today can do two things with data:
- Collect it, and send it to some central hub for storage/ evaluation. Given they have an internet connection. Like your smartphone which analyses your locally taken photos, sends them to a central hub and gets back a “collection of photos”, or annotates them, etc.
- Compute on this data locally, on the device. This is always a small challenge because these devices are much smaller in computational power than our usual cloud computer.
So what does that mean? For me, it implies, that both things will have an impact on future companies. First, as a company you will be faced with a lot of edge collected data that wants to be evaluated in a central place, stemming from suppliers, your customers, and other third-parties that will supply you with such kind of data.
Second, you as a company can take control of your own edge devices to collect data. Think about package & shipping tracking chips, wearable chips, chips, cameras, etc. There will be a lot of touchpoints for you to take more control of edge devices than before. Third, with or without an internet connection, edge devices will want to compute to evaluate data themselves.
Besides these three different directions in which data analytics will have to move, the kind of data involved will also pose a paradigm shift for a lot of companies. Large amounts of current data analysis are based on historical data, as well as “state” data as described above. Real-time data usually do not make it into the algorithms at all.
If it does, it comes at the end of a large series of data, maybe with some “weights” to make current data more important than the historical one. In stark contrast to that is the data sphere of the future, where the real and only important stuff is the real-time data because there will be so much of it! The reason why many companies resort to historical and state data is that in the past, we had next to no event kind of data.
- The only thing we knew about someone ordering at someplace was his socio-demographic data, which we probably had to buy somewhere.
- But in the future, this dynamic will shift.
- It will be like image recognition.
- In the past, we needed 100+ images to have a computer tell us who the person on the image was.
Today the computer is actually better than a human expert in this task, based on just one image, So it will be in the future, we will be able to tell a person exactly from a day of event data, what he likes, what he will probably buy later, and so on.
We won’t need any of the data like his income, his gender, etc. it will all be in the events, the real-time interactions of a single day. And that again will pose a large paradigm shift for analytics people around the world. In the future the real value won’t be in collecting large amounts of specific data through time, it will be much more about collecting a large spectrum of data.
The simple truth is, most companies don’t think that image data will be important to their business ever, because there is no good “core product fit”. An e-procurement system will probably think that their complete interaction is through a website or some digital system, and the usage of image data is limited in that area. Porter’s six forces. Image by Sven Balnojan. Now the thing is, image data can possibly be integrated into the core of any of the six forces shaping your industry. If you’re an e-procurement system, your forces are:
- Competition/ New Entrants : There are traditional e-procurement systems that probably follow your thinking. How about competitors that only hold a small segment of your market? That serve more specialized parts and feel that VR/AR is a smart way to present pieces?
- Customers/ Substitutes/ Complementors : What if your customers suddenly can use an app on their smartphone (provided by someone else) to instantly recognize the thing they are looking for, and find an offer at some other marketplace?
- Suppliers : What if your suppliers suddenly start using image technology to form a deeper connection with their end-users, bypassing you because they provide superior customer service through an AI? What if suppliers can use edge data to quickly detect when an end-user has to reorder the same thing, like copy paper, or a spear part?
All of these things would be powered in their core by image data, all of them could disrupt this industry, which I’d consider to be far away from getting a core product fit with image data. Second, the massive wave of image data available will not only bring the data but also technology to use this on a grande scale.
Anyone will be able to do a lot of computation on images cheaply and easily, again changing dynamics. It will probably bring OCR to a level where no paper documents will be flying around. At least that’s what your customers & suppliers will expect. It will bring identification of objects on images to a pretty close to a perfect level (way beyond human level).
And a lot of things that don’t come to my mind right now. I’m not sure image data in itself will change any industry, but the same principle applies to real-time data, event data, and all the things that we discussed up to now. And I do believe not a single one of these topics should be discarded right away as together they will disrupt every industry.
- The IDC IoT devices forecast,
- Data on smartphone adoption rates around the globe,
- Internet adoption data provided by Statista,
- The main source of information, the IDC & SeaGate white paper on the Datasphere forecasting the data growth worldwide.
- An InfoQ article on Moore’s Law mentioning potential bottoms that are already hit.
- Google’s article on its tensor processing units (TPUs).
- A small mention of Rose’s law for the growth rate of Qubits in quantum computers, coming back to a comment made by Steve Jurvetson.
- The Wikipedia page which paints a good picture of machine learning & quantum computing in combination,
- The Martens & Provost article from 2011 about using transactional data vs. socio-demographic data in large scale targeting efforts.
- Forbes article about super-human level image recognition performance from 2015.
How big data is increasing variation?
Data is also becoming more varied in terms of its format, type, and source. Traditionally, data was mostly in a structured form, which means that it was stored in a tabular form such as a database. However, now, data is also generated in unstructured and semi-structured forms, such as videos, images, audio, and text.
How big data is changing industries?
Manufacturing – While manufacturing is historically a “low-tech” sector, Big Data is shaking up the industry across the board. Big Data analytics in manufacturing allows organizations to gain end-to-end visibility into production processes, supply chain metrics, and environmental conditions that impact productivity and deliverables.
How big data is expanding?
TOKYO, Dec.15, 2022 (GLOBE NEWSWIRE) – The Global Big Data Market Size accounted for USD 163.5 Billion in 2021 and is projected to occupy a market size of USD 473.6 Billion by 2030 growing at a CAGR of 12.7% from 2022 to 2030. Growing penetration of the internet and an increasing number of smartphone users are primary factors that are driving the global big data market.
Global big data market revenue valued at USD 163.5 Billion in 2021, with a 12.7% CAGR from 2022 to 2030Recent big data statistics reveal that more than 97% of key global businesses plan to invest in big data and AIOver 58% of the world’s large and small businesses are focusing to implement big data technologyNorth America big data market share occupied around 35% in 2021Asia-Pacific big data market growth is estimated to attain 14% CAGR from 2022 to 2030By component, the services sub-segment grabbed revenue of USD 75.2 billion in 2021Based on industry vertical, the BFSI sub-segment gathered US$ 29.4 billion in revenue in 2021An increasing number of social media users is a prominent big data market trend driving the industry demandAccording to a recent study, there were 4.95 billion active social media users worldwide in January 2021
Request For Free Sample Report @ https://www.acumenresearchandconsulting.com/request-sample/3093 Big Data Market Coverage:
|Big Data Market
|Big Data Market Size 2021
|USD 163.5 Billion
|Big Data Market Forecast 2030
|USD 473.6 Billion
|Big Data Market CAGR During 2022 – 2030
|Big Data Market Analysis Period
|2018 – 2030
|Big Data Market Base Year
|Big Data Market Forecast Data
|2022 – 2030
|By Component, By Deployment Model, By Business Function, By Application, By Industry Vertical, And By Geography
|Big Data Market Regional Scope
|North America, Europe, Asia Pacific, Latin America, and Middle East & Africa
|Key Companies Profiled
|Accenture, Alteryx, AWS, Cloudera, Equifax, Inc. Google, IBM, Oracle, Microsoft, SAS, SAP, TIBCO, Teradata, Salesforce, Qlik, and VMware.
|Market Trends, Drivers, Restraints, Competitive Analysis, Player Profiling, Regulation Analysis
Big Data Market Dynamics Growing Trend of Social Media Analytics Retail, hospitality, travel, and other industries are increasingly implementing cloud-based and AI-driven social media analytics solutions to support market expansion. Analytics-based AI services are becoming more and more common in developed nations like the U.K., the U.S., France, Japan, and others.
- As businesses look to use information assets to enhance customer relationships, business results, and operational efficiency, big data analytics is in high demand.
- Eeping up with the evolving needs and expectations of burgeoning big data analytics users, however, has grown more difficult.
- On the other hand, new big data analytics trends like text analytics and social media analytics are expected to open up a lot of business opportunities.
Growing Implementation across Businesses to Generate Lucrative Opportunities The growing implementation of big data across enterprises is expected to provide key players with numerous opportunities as well as optimize their growth. Since big data facilitates the gathering and analysis of the enormous amounts of data that governments and businesses deal with on a daily basis, this technology is gaining the much-needed traction.
- Additionally, as big data analytics software enables organizations to study the factors influencing outcomes and provides the power of decision optimization, the increased need to gain insights for business planning is expected to provide lucrative opportunities for market expansion.
- Growth in Security Concerns Restricts the Big Data Market Growth Data misuse, whether intentional or unintentional, can have serious legal ramifications for both organizations and customers.
To ensure that no data usage violates government laws, businesses should explicitly state clauses related to data usage, process, and detainment in their project contracts. The rate of data intrusions is increasing in tandem with the constant evolution of technologies.
As a result, while improving analytics solutions for various vertical-specific applications, data privacy and security are critical. Check the detailed table of contents of the report @ https://www.acumenresearchandconsulting.com/table-of-content/big-data-market Big Data Market Segmentation The global market has been split into components, deployment model, business function, application, industry vertical, and region.
Hardware, software, and services are the three components of the component segment. Based on the deployment model, the market is divided into three segments: on-premise, cloud-based, and hybrid. Finance, sales and marketing, human resources, and operations are the business function segments.
Customer analytics, operational analytics, fraud detection, compliance, data warehouse optimization, and other applications are covered in the application segment. Furthermore, the industry vertical segment is split into manufacturing, media & entertainment, BFSI, IT & telecommunication, healthcare, energy and power, retail & e-commerce, government, transportation, and others.
Big Data Market Share According to our big data market forecast, the services sub-segment will account for the majority of the market share in 2021. In terms of growth, the software will see rapid expansion in the coming years. Among deployment models, the on-premise sub-segment dominated the market and is expected to continue to do so in the coming years.
- Meanwhile, the cloud-based and hybrid segments are expected to grow at a rapid pace between 2022 and 2030.
- As per our big data industry analysis, the finance business function dominated the market with the utmost shares in 2021 and will continue to do so in the coming years.
- Based on our industry vertical analysis, BFSI will lead the market in terms of revenue, whereas retail & e-commerce will lead in terms of growth from 2022 to 2030.
Big Data Market Regional Outlook North America, Latin America, Europe, Asia-Pacific, and the Middle East & Africa account for the majority of the worldwide big data industry. The emergence of big data technology has provided numerous opportunities for businesses to manage valuable data streams and transform them into valuable information.
The regional market demand is expected to be driven by high usage in the retail and healthcare sectors. Asia-Pacific is expected to have the highest CAGR during the forecast period, owing to enormous growth in transactions and business strategies such as mergers and acquisitions and joint ventures across all industry verticals.
Buy this premium research report – https://www.acumenresearchandconsulting.com/buy-now/0/3093 Big Data Market Players Some prominent big data companies covered in the industry include Accenture, Alteryx, AWS, Cloudera, Equifax, Inc. Google, IBM, Oracle, Microsoft, SAS, SAP, TIBCO, Teradata, Salesforce, Qlik, and VMware.
What was the market size of Big Data Market in 2021?What will be the CAGR of Big Data Market during the forecast period from 2022 to 2030?Who are the major players in Global Big Data Market?Which region held the largest share Big Data Market in 2021?What are the key market drivers Big Data Market?Who is the largest end user Big Data Market?What will be the Big Data Market value in 2030?
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The Global Data Preparation Tools Market accounted for USD 3.1 Billion in 2020 with a considerable CAGR of 20% during the forecast period of 2021 to 2028. About Acumen Research and Consulting : Acumen Research and Consulting is a global provider of market intelligence and consulting services to information technology, investment, telecommunication, manufacturing, and consumer technology markets.
ARC helps investment communities, IT professionals, and business executives to make fact-based decisions on technology purchases and develop firm growth strategies to sustain market competition. With the team size of 100+ Analysts and collective industry experience of more than 200 years, Acumen Research and Consulting assures to deliver a combination of industry knowledge along with global and country level expertise.
Will 70% of companies switch to wide and small data by 2025?
From big data to small and wide data: what you need to know A shift away from big data to small and wide data is unlocking new opportunities for innovation and data-driven decision-making. With the emergence of AI, data fabric and composable analytics solutions, organisations are increasingly able to examine a combination of small and large – and structured and unstructured – data.
Combined with the correct data strategy, these data sources can help organisations uncover useful insights in small and even micro data tables. To illustrate, while traditional data sources may provide a column for the colour of an item, an AI-friendly (wide) data source could have multiple columns – or features – that ask: “Is it red? Is it yellow? Is it blue?” Each of the additional columns demand special consideration from the database engine to unlock the true value of wide data sources.
Organisations are likely to continue leveraging their access to big, small and wide data sources as a key competitive capability. In fact, Gartner analysts predict that by 2025, (or data that comes from a variety of sources), thus enabling more context for analytics and intelligent decision-making.
- But what’s the difference between these data types and why should organisations care? Big vs small vs wide data Big data has been prevalent since computer software and hardware gained the ability to process huge data sets.
- At first used by scientists and researchers to conduct meaningful statistical analyses, by the mid-2000s big data had become a must-have for every large enterprise.
The Economist even famously called data ‘the new oil’, and companies duly set about generating massive data lakes and mining them for value. Big data is great for big-picture analyses and gaining a better view into broader trends. In short, it’s a good tool for understanding whether you are looking at a man or a horse.
- Small and wide data is better at focusing on specific bits of information to gain distinct insights.
- To use the man or horse example, small and wide data is more about understanding what type of horse you’re looking at, what colour the man’s eyes are and why both are in the picture in the first place.
Wide data specifically ties together disparate data from a wide variety of data sources to understand aspects such as behaviour. For example, wide data analyses can help retailers understand how likely a shopper is to purchase a specific item based on the items in their basket.
Small data takes on a more individual element. Small data analyses focus on collecting and understanding smaller data sets sourced from a single organisation. It’s basically the opposite of big data and requires a separate data strategy as small data is not readily gained from big data sets. Towards small, wide data use cases The sheer cost of big data initiatives make a compelling case for greater adoption of wide and small data capabilities.
In addition to enormous complexity introduced by vast pools of big data and difficulties with extracting value from big data sets, any strategy depending on big data also requires often scarce and expensive skills. This level of investment is unfortunately beyond the means of most organisations.
- In contrast, the more company-specific insights gained from small data sets can more easily be leveraged to improve decision-making, while insights from wide data should be integrated into organisational decision-making to improve the quality of outcomes from such decisions.
- Companies exploring the potential of AI to augment decision-making capabilities can find enormous benefit from wide data.
The smaller data sets are easier to manage than big data lakes and thus more likely to remain up to date and of immediate relevance. Applying algorithms to augment decision-making using wide data typically produces more accurate and timely insights than relying on large-scale analyses using big data.
To return to the man and horse example, wide data can help companies not only better understand different attributes – hair colour, eye colour, age, etc – of the man as well as key characteristics (breed, colour, age, size) of the horse, but also draw from other data sources to understand the lineage of the horse, the man’s family connections and hobbies, and whether the man rode the horse to where they now are.These types of hyper-contextual data insights can bring greater clarity to organisational processes and help companies better understand their customers, employees and operating environment.As the volume of data grows to unmanageable levels, companies will increasingly have to turn to small and wide data to support the business.Companies wishing to achieve true data-driven decision-making capabilities should start exploring the potential of small and wide data in specific use cases and embark on a process of discovery to unlock the power of data to improve business outcomes.
: From big data to small and wide data: what you need to know