Why database performance creates business value

by Vivien | Feb 20, 2021 | Edge Computing, Insights, IoT

“Why does database performance matter?” “What is the business value of database speed?” “Why should I care about the performance of a database?”

Why database performance matters in a nutshell

Make Faster Decisions

Database speed is the key to compute more data faster and make data-based decisions quickly. Faster decision-making drives business value.

Save Resources & Costs

Database speed translates into resource efficiency. Saving resources (like battery, CPU, memory) saves money and reduces waste.

Better UX & Response Rates

Database speed affects end user response rates significantly – smooth and fast user experiences keep people happy and more productive.

As a developer, it seems clear that database performance matters. At the very least, a fast database that gives you out-of-the-box speed saves time and nerves during development. Any piece of the tech stack that works super-fast makes a developer’s job easier. But there is more to it. Learn, why and how database performance impacts business value and get ideas on how to quantify this for your business case.

Data should be available when need where needed

We all dream of a future transformed by data. Cars that drive themselves to be repaired before a failure occurs. Fridges that are restocked while we are at work. Reducing resource waste to an absolute minimum. Building sustainable cities and communities.[1] It is truly amazing what is possible today…

Then reality hits: Before you can implement amazing solutions to make the world a better place for everyone, someone needs to solve the technical challenges, including hidden requirements. For example: you need the necessary data, and you need it available when needed where needed. This often isn’t that simple. Data persistence, database speed, and data synchronization are typical non-functional or “hidden” requirements. These are prerequisite technologies to allow the application to access, process and possibly depict the data required to answer a request (from another application or from a user), and thus enable the functionalities / features. All in all, this is a pretty fundamental requirement. And it pays off to build your app on top of a solid foundation. Because, if you built your application on a solid foundation, every feature you dream up, no matter when, and any next feature will be easier and faster to implement.

Functional and non-functional requirements – the hidden challenges of your IoT project

While you need data in any application, most often no one will write down where and how to handle it as a user story or requirement. As opposed to features, e.g. “being able to search for names in the address book”, data persistence, database speed, and often even data synchronization are “hidden requirements”. Data is just expected to be available where needed when needed. Whether the data you need really will be available when you need it, depends strongly on the database the application is using and and where this database runs. On top, the mechanisms you employ to exchange data between different devices (end devices, servers, ….) matter.

Hidden requirements are one of the major reasons why the Industry 4.0 dream is still in many respects a dream and not a reality – in Europe at least. Despite it being a topic for more than 10 years. [2]

Database performance

What is a database?

A database is a piece of software that allows the storage and systematic use of digital information. A database typically allows developers to store, access, search, update, query, and otherwise manipulate data in the database via a developer language or API. These types of operations are done within an application, in the background, typically hidden from end users. Most applications need a database as part of their technology stack.

What is database performance?

We like and therefore use the following definition from Craig Mullins (2002): “Database performance can be defined as the optimization of resource use to increase throughput and minimize contention, enabling the largest possible workload to be processed.” [3]

Why does it matter if the database runs on the edge or in the cloud?

An edge database holds data on the (end) devices, where the data is used – and typically additionally sends some parts of the data to a central place like an on-premise server or the cloud. As opposed to this, a server / cloud-based database holds all data on the server / in the cloud. Where the data sits, determines from where, when and how it can be accessed. If all data is on a central server or the cloud, the prerequisite to accessing this data is a working network connection.

Online

Offline

It follows that edge applications are based upon a distributed computing paradigm, allowing edge devices to be autonomous. On the other hand, cloud-based applications are based on the centralized computing paradigm, where one central instance is in charge, with all other devices being dependent upon this central instance. This significantly affects the response time of the application, the availability of the application, and last not least the bandwidth needed for the application, which also translates into cloud costs.

Location matters: while a fast database gives you fast response times, if the database sits in the cloud and needs to be called from edge devices, you need to factor in the duration it takes to request the data and get a response. And with any networking you cannot guarantee response times or ensure it is always available. While this is not the database performance itself, it highly affects application performance.

The impact of database performance on your business

Database performance matters. Whether your solution needs the speed, because of the necessity to re-act in (near) realtime, or to keep your users (customers, employees, …) happy, productive, buying, or just to save costs for stronger edge hardware and the cloud. “Considering that even a single moment of latency or downtime can cost companies thousands of dollars, the speed advantages of edge computing cannot be overlooked.” [4]

The necessity of database speed for mission-critical, security relevant, (near) real-time functionalities

If you need near real time functionalities, every piece in the tech stack matters, but the database has a particularly strong impact on the response rates of your application. Consider autonomous driving, healthcare and security applications, or IIoT solutions for production lines: Any application supporting such a scenario needs to respond reliably with speed. “This is not the same as a lag in loading your favorite cat pictures. A lag in a moving vehicle scenario is a matter of life and death.” [5]

Accordingly, if end devices like cars, smartphones, health trackers, machines on the factory floor are involved, a purely cloud-based application is not an option. Data needs to be stored and used on the devices directly. Thus, an edge database is necessary. Ideally, an extremely fast one.

Examples of use cases with a need for database speed

Anything running on a car really needs to be highly ressource efficient and fast. Ressources on the car are highly limited and database speed translates into ressource-efficency. Autonomous driving capabilities are a special case requiring significant compute power to run the algorithms in real-time within the control unit of the car. As can be easily deducted from first-hand driving experience, during this kind of constant information processing and instantaneous decision making, every fraction of a millisecond counts. Information processing speed and reliability (guaranteed QoS parameters) is of the essence for driver assistance and autonomous driving.

Moving to a purely monetary example, let’s consider roadside tolling. In roadside tolling, the edge devices on the side of the road need to process the information from a moving vehicle in order to identify the car, bill according to usage, and detect violators. Ideally, it even informs the car owner of the result. As the car is constantly moving and can be going fast, all of this needs to happen in a very short amount of time. A super fast database lookup on the edge is key to avoid money loss and deliver good customer service.

For a final example, let us look at an Industrial IoT (IIoT) application: Additive manufacturing. 3D printers use layering techniques with a variety of materials to quickly create custom designed parts. During the layering process, the controller needs to quickly and efficiently incorporate small changes in the environment (e.g. an increase in temperature) to ensure quality and accuracy of the part. Faster and more precise manufacturing is currently limited by the I/O throughput. With a fast database, the I/O throughput is higher, allowing for more complex and finite production.

In short: A superfast database is not a nice to-have, it is a must-have. The database speed a database brings out-of-the-box is critical for such an application.

The impact of database speed on Sales, Conversions, Retention (or at least, nerves)

There is a reason Google forces companies to optimize their websites and mobile applications for performance: There is a wealth of research and evidence that suggests response rates of websites and mobile applications impact user behavior significantly.[6] Even more, there are several studies providing evidence that response rates impact actual buying behavior. [7] While there is less research on other digital applications like e.g. a desktop app or workplace software, some studies have shown that needing to work with slow applications decreases employee satisfaction and productivity. [8]

The impact of database speed on battery, CPU, hardware and related resources

Another hidden requirement typically is resource-efficiency with regards to CPU, RAM, Disc space and battery / electricity. For any application running in the cloud, these requirements are balanced in the backend as the cloud scales vertically. It “only” adds to cloud costs (and is a waste of energy – not to mention all the infrastructure / hardware enabling that waste).

On the edge, you typically work with restricted devices, meaning you can only use the devices’ resources, which can be pretty limited. Therefore, inefficient applications can push a device to its limits, leading to e.g. slow response rates, crashes, and battery drain. Security is a very necessary cross-the-stack functionality that often impacts performance. While data that stays on the edge is challenging to hack, edge data needs to be protected just like data in the cloud.

How database performance impacts the business value of your IoT application

All applications on one device share the available hardware capabilities; resource allocation is managed by the operating system. Accordingly, the more resources an application or the database uses, the less resources are available for other uses. The faster a database executes its operations, the less CPU it uses, the less battery / electricity, and typically also memory. In practice that means there are more resources available on the device to run e.g. Edge AI or Edge ML applications.

From a business value perspective that means:

You can save on hardware costs (CPU, RAM, Disc, Memory, …): either do more on existing / chosen hardware, upgrade hardware later or choose smaller and thus less expensive hardware.
You can save on energy and cloud costs: The more efficient, the less electricity, the less cloud costs. This can add up tremendously as projects scale.
You can add more features, deliver more functionalities, make your application more secure within a given environment.
You can deliver a smooth, fast user experience, enabling applications that deliver in near-realtime.
In sum, it clearly impacts the cost structure and value you can deliver.

Database performance impacts business value, directly and indirectly

As projects scale in size and scope, hidden requirements like database performance often become clear. At scale, small issues like delayed data, or data volumes, become big headaches. Ideally, these sorts of requirements would be at the heart of the design stage of any project – and budgeted for at the beginning. The choice of database clearly has a huge impact on the business success of IoT applications.

[1] See https://www.weforum.org/agenda/2018/01/effect-technology-sustainability-sdgs-internet-things-iot/ for IoT impact on Sustainable Development Goals (SDG)
[2] https://restart-project.eu/much-know-industry-4-0/
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=13&cad=rja&uact=8&ved=2ahUKEwiGidSA6trnAhVQY8AKHTpSDUIQFjAMegQICBAB&url=https%3A%2F%2Fwww.mdpi.com%2F2076-3387%2F9%2F3%2F71%2Fpdf&usg=AOvVaw3cx44OOMfNzJ_BJlCG8Gfj
[3] Database Administration: The Complete Guide to Practices and Procedures By Craig Mullins 2002
[4] https://www.vxchnge.com/blog/the-5-best-benefits-of-edge-computing
[5] https://www.zdnet.com/article/why-autonomous-vehicles-will-rely-on-edge-computing-and-not-the-cloud/
[6] https://developers.google.com/web/fundamentals/performance/why-performance-matters https://www.thinkwithgoogle.com/intl/en-154/insights-inspiration/research-data/need-mobile-speed-how-mobile-latency-impacts-publisher-revenue/
https://www.machmetrics.com/speed-blog/how-does-page-load-time-affect-your-site-revenue
https://datadome.co/bot-management-protection/website-performance-how-to-increase-your-business-by-blocking-bots/
[7] https://developers.google.com/web/fundamentals/performance/why-performance-matters
https://www.thinkwithgoogle.com/intl/en-154/insights-inspiration/research-data/need-mobile-speed-how-mobile-latency-impacts-publisher-revenue/
https://www.machmetrics.com/speed-blog/how-does-page-load-time-affect-your-site-revenue
https://datadome.co/bot-management-protection/website-performance-how-to-increase-your-business-by-blocking-bots/
[8] https://drum.lib.umd.edu/handle/1903/1233
https://www.tandfonline.com/doi/abs/10.1080/01449290500196963

Add an Edge to Your Project Today

Why Edge Computing is More Relevant in 2021 Than Ever

by Vivien | Jan 14, 2021 | Edge Computing, Insights, IoT

The world has been forced to digitize more quickly and to a greater extent in 2020 and 2021. COVID has created the need to remodel how work, socializing, production, entertainment, and supply chains function. Despite decades of digitization efforts, with the pandemic upon us, digitization challenges have become transparent. Many companies and countries realize now, they have fallen behind. And those that have not yet digitized were hit hardest by the pandemic. [1] With people leaning heavily on online digital solutions, internet infrastructure is at its capacity limit. [2] Accordingly, users are seeing broadband speeds drop by as much as half. [3] In Europe, governments even requested to reduce the quality of Netflix, Amazon Prime, Youtube and other streaming services to improve network speed. [4]

These challenges demonstrate the growing need for an alternative to cloud computing. Cloud computing is an inherently centralized computing paradigm. Edge Computing is a decentralized topology that is based on keeping data local, at the ‘edge’ of the network, as close to the source as possible. Edge Computing is ideal for applications that are data-intensive, have high latency-requirements, or need to work offline, independant from a cloud connection. Using data on the edge, directly on or near the source of the data, not only increases the efficiency and speed of data use, but it reduces unecessary network burden and data traffic waste.

Coronavirus accelerates the need to digitize

It was clear even before the outbreak that internet infrastructure was struggling to keep up with growing data volumes. However, the pandemic has made broadband limitations more apparent to everyday users.

Projections estimate that by 2025 there will be 20 million IoT devices [5] and 1.7MB of data created per second per person. It is slow, expensive, and wasteful to send all of this data to the cloud for storage and processing. This practice overburdens bandwidth and data center infrastructure. It makes projects expensive and unsustainable. Working with the data, locally, on the edge, where it was produced and is used, is more efficient than sending everything to the cloud and back. It brings reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data and heightened data privacy – and even reduces CO2. Indeed, prior to the pandemic, edge computing was on the strategic roadmap for over 50% of mobility decision makers. [6]

As the world begins to recover from the coronavirus pandemic, digitization efforts will no doubt increase. We will see intelligent systems implemented across industries and value chains, accelerating innovation and alongside: data volumes and subsequent strain on network bandwidth. Edge computing is a key technology to ensure that this digitalization is both scalable and sustainable.

Edge Computing takes the ‘edge’ off bandwidth strain

What is Edge Computing?

With edge computing, data is stored and used on devices at the “edge” of the network – away from centralized cloud servers. Computing on the edge means that data is stored and used locally, on the device, e.g. a smart phone or IoT device. Edge computing delivers faster decision making, local and offline data processing, as well as reduced data transfer to the cloud (e.g. filtered, computed, extra- or interpolated data), which saves both bandwidth and cloud storage costs.

The Edge complements the Cloud

Although some might set cloud and edge in competition, the reality is that edge computing and cloud computing are both useful and relevant technologies. Both have different strengths and ideal use cases. Together they can provide the best of both worlds: decentralized local storage and processing, making efficient use of hardware on the edge and central storing and processing of some data, enabling additional centralized insights, data backups (redundancy), and remote access. To combine the best of both worlds, relevant and useful data must be synchronized between the edge and cloud in a smart and efficient way.

Edge computing is an ideal technology to reduce the strain on data centers, so those functions that need cloud connection have adequate bandwidth; while those use cases that benefit from reduced latency and offline functionality are optimized on the edge.

The Edge: interface between the Physical and the Digital World

Edge devices handle the interface between the physical world and the cloud, enabling a whole set of new use cases. “Data-driven experiences are rich, immersive and immediate. But they’re also delay-intolerant data hogs”. [8] And therefore need to happen locally, on the edge. We may see edge computing enabling new forms of remote engagement [9], particularly in a post-corona environment.

Edge devices can be anything from a thermostat or small sensor to a fridge or mobile phone or car – and they are part of our direct physical world and use data from their local environment to enable new use cases. Think self-stocking fridges, self-driving cars, drone-delivered pizzas. In the same way, Edge Computing is the key to the first real world search engine. I am waiting for it every day: “Hey Google, where are my keys?” Within a location like a house, the concepts and technologies to enable such a real-world search engine are all clear and available – it is just a matter of time and ongoing digitization. The basis will need to be a fast and sustainable edge infrastructure.

Sustainability on the Edge

Centralized data centers consume a lot of energy, produce a lot of carbon emissions and cause significant electronic waste. [10] While data centers are seeing a positive trend towards using green data centers, an even more sustainable approach is to cut unnecessary cloud traffic, central computation and storage as much as possible by shifting computation to the edge. Edge Computing strategies that harness the power of already deployed available hardware (like e.g. smartphones, machines, desktops, gateways) make the solution even more sustainable.

Intelligent Edge: AI and Edge advance hand in hand

The growth of Artificial Intelligence (AI) and the Edge will go hand in hand. As more and more data is generated at the edge of the network, there will be a greater demand for intelligent data processing and structured optimization to reduce raw data loads going to the cloud. [11] Edge AI will have the power to work with data on local devices, keeping data streams more useful and usable. In the near future, Machine Learning applications will have the ability to learn and create unique, localized, decentralized insights on the edge – based on local inputs.

“With Edge AI, personalization features that we want from the app can be achieved on device. Transferring data over networks and into cloud-based servers allows for latency. At each endpoint, there are security risks involved in the data transfer”. [12] Which is part of the reason why the Edge AI Software market is forecasted to reach 1.12 trillion dollars volume by 2023. The development of AI accelerators, which improve model inferencing on the edge, namely from NVIDIA, Intel and Google are helping to make AI on the edge more viable. [13] A fast edge database is a necessary base technology to enable more AI on the edge.

Edge Computing – an answer to Data Privacy concerns and a need for Resilience

As data collection grows in both breadth and depth, there is a stronger need for data privacy and security. Edge computing is one way to tackle this challenge: keeping data where it is produced, locally, makes data ownership clear and data less likely to be attacked and compromised. If compromised, the data compromised is clearly defined, making notification and subsequent actions manageable. ObjectBox, in its core and as an edge technology, is designed to keep data private, on those devices it was created on, and only share select data as needed.

The more our private and working lives as well as the larger economy depend on digitalization, the more important it is that systems, underlying computing paradigms as well as networks have strong resilience and security. In computer networking, resilience is the ability to “provide and maintain an acceptable level of service in the face of faults and challenges to normal operation.” [14]

ing initEdge Computing shifts computer workloads – the collection, processing, and storage of data – from central locations (like the cloud) to the edge of the networks to many individual devices such as cell phones. Accordingly, any strain is distributed to many devices. Therefore, the risk of a total breakdown is reduced: If one device does not work anymore, the rest is still working. Depending on the setup, the individual devices could even compensate for devices that have a problem.

The same applies to security risks: Even if data from one device is compromised, all other data sets are still safe; the loss is thus very limited and clear. Overall, as a complement to the cloud, edge computing provides improved strength and security in local networks around the world. These local infrastructures can relieve the pressure on the existing complex dependencies, and in turn make the wider system more resilient and flexible. With Edge Computing crisis response can therefore in all likelihood be faster, better informed, and more effective. [15]

Why Corona-Tracking-Apps need to work on the edge

There was initially quite some debate about taking a centralized versus decentralized approach to Corona-Tracking-Apps. [16] Many people were worried about their data. Edge Computing – storing most parts of the data locally, on the user’s device – is a great way to avoid unnecessary data sharing and keep data ownership clear. At the same time, data is by and large much more secure and less likely to be attacked and hacked, as the data to be gained is very reduced. An intelligent syncing mechanism like ObjectBox Sync ensures that the data which needs to be shared, is shared in a selective, transparent and secure way.

The next few years will see big cultural changes in both our personal and professional lives – a portion of those changes will be driven by increased digitalization. Edge computing is an important paradigm to ensure these changes are sustainable, scalable, and secure. Ultimately, we have the chance to rise from this crisis with new insights, new innovation, and a more sustainable future.

Discover Edge Use Cases

1. https://www.netzoekonom.de/2020/04/11/die-oekonomie-nach-corona-digitalisierung-und-automatisierung-in-hoechstgeschwindigkeit/
2. https://www.cnet.com/news/coronavirus-has-made-peak-internet-usage-into-the-new-normal/
3. https://www.nytimes.com/2020/03/26/business/coronavirus-internet-traffic-speed.html
4. https://www.theverge.com/2020/3/27/21195358/streaming-netflix-disney-hbo-now-youtube-twitch-amazon-prime-video-coronavirus-broadband-network
5. https://www.gartner.com/imagesrv/books/iot/iotEbook_digital.pdf
6. https://www.forbes.com/sites/forrester/2019/12/02/predictions-2020-edge-computing-makes-the-leap/#1aba50104201
7. https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/
8. https://www.iotworldtoday.com/2020/03/19/ai-at-the-edge-still-mostly-consumer-not-enterprise-market/
9. https://www.accenture.com/us-en/insights/high-tech/edge-processing-remote-viewership
10. https://link.springer.com/article/10.1007/s12053-019-09833-8
11. https://www.forbes.com/sites/cognitiveworld/2020/04/16/edge-ai-is-the-future-intel-and-udacity-are-teaming-up-to-train-developers/#232c8fab68f2
12. https://www.forbes.com/sites/cognitiveworld/2020/04/16/edge-ai-is-the-future-intel-and-udacity-are-teaming-up-to-train-developers/#232c8fab68f2
13. https://www.forbes.com/sites/janakirammsv/2019/07/15/how-ai-accelerators-are-changing-the-face-of-edge-computing/#2c1304ce674f
14. https://en.wikipedia.org/wiki/Resilience_(network)
15. https://www.coindesk.com/how-edge-computing-can-make-us-more-resilient-in-a-crisis
16. https://venturebeat.com/2020/04/13/what-privacy-preserving-coronavirus-tracing-apps-need-to-succeed/

Digital Healthcare – Market, projections, and trends

by Vivien | Dec 6, 2020 | Edge Computing, Insights, IoT

Note to Readers (2024 Update)
This white paper, originally published in 2020, explores key projections, market dynamics, and trends in digital healthcare. Since its publication, the field has evolved significantly, and the accompanying detailed report has been updated to reflect the state of digital healthcare in 2024. For the most current insights and analysis, please refer to the updated report (also linked at the end of this blog).

If you work in the healthcare industry, you are likely familiar with some uses of IoT devices. According to Gartner (2020), 79% of healthcare providers are already successfully employing IoT solutions.[1] However, this is just the beginning. While before COVID-19, the growth of digital health adoption had stalled [2], the market is picking up speed again. Indeed, Q3 2020 was a record year for investments in healthcare companies [3] and the market expects rising investments in healthtech for next years [4]. Today, underutilized data plays a major role in healthtech innovation [17] and the growing importance of healthcare data for future offerings is evident [5]. Take a look how analyts from Gartner to Accenture and Forrester expect the market to grow:

The digital healthcare market 2020 and beyond

digital-healthcare-market-trends-2020-edge-iot

Analysts expect Artificial Intelligence in healthcare to reach $6.6 billion by 2021 (with a 40% CAGR). [6]
The Internet of Medical Things (IoMT) market is expected to cross $136 billion by 2021. [11]
Analysts expect the healthcare wearable market to have a market volume of $27 billion by 2023 (with a 27.9% CAGR). [7]
The IoT industry is projected to be worth $6.2 trillion by 2025 and around 30% of that market (or about $167 billion) will come from healthcare. [8]
Analysts expect the global Medical Health Apps market to grow to $236 billion by 2026, reflecting a shift towards value based care. [9]
The projected global digital health market is estimated to reach $510.4 billion by 2026 (with a 29% CAGR). [10]

The Healthcare industry has been struggling with shrinking payments and cost optimizations for years. [18] Fueled by the need to adopt in light of the COVID pandemic, digital technologies bring extensive changes quickly to this struggling industry now. Data is moving to the center of this changing ecosystem and harbors both risks and opportunities in a new dimension. [21] The basic architecture and infrastructure to have the data reliably, securely and quickly available where they are needed will be decisive for the success or failure of digital healthcare solutions. [17] [21]

We recommend keeping an eye on the following five trends

The 5 biggest digital healthcare trends to watch

Artificial Intelligence (AI)

Accenture estimates that AI applications can help save up to $150 billion annually for the US healthcare economy by 2026. [6] Therefore, it is no wonder that the healthcare sector is expected to be among the top five industries investing in AI in the next couple of years. [19] The top three greatest near-term value AI applications in healthcare are: 1. robot-assisted surgery ($40 billion), 2. virtual nursing assistants ($20 billion), and 3. administrative workflow assistance ($18 billion).

Big Data / Analytics

The goal of big data analytic solutions is to improve the quality of patient care and the overall healthcare ecosystem. The global healthcare Big Data Analytics market is predicted to reach $39 billion by 2025. [12] The main areas of growth are medical data generation in the form of Electronic Health Records (EHR), biometric data, sensors data.

internet-of-medical-things-digital-healthtech

Internet of Medical Things (IoMT)

IoMT is expected to grow to $508.8 billion by 2027. [13] According to Gartner, 79% of healthcare providers are already using IoT in their processes. [27] During COVID, IoMT devices have been used to increase safety and efficiency in healthcare, i.e. providing and automating clinical assistance and treatment to the infected patient, to lessen the burden of specialists. Future applications, like augmented reality glasses that assist during surgery, are leading to a focus more on IoMT-centric investments. [14]

Telehealth / Telemedicine

Telecommunications technology enables doctors to diagnose and treat patients remotely. Consumer adoption of telehealth has skyrocketed in 2020 and McKinsey believes that up to $250 billion of current US healthcare spend could potentially be virtualized. [25] Also, many patients view telehealth offerings more favorable and – having made good experiences – are planning to continue using telehealth in the future. [26] Not astonishingly, telemedicine stocks also grow rapidly. [14]

Edge Computing

Edge computing is a technological megashift happening in computing. [23] Instead of pushing data to the cloud to be computed, processing is done locally, on ‘the edge’. [15] Edge Computing is one of the key technologies to make healthcare more connected, secure, and efficient. [22] Indeed, the digital healthcare ecosystem of the future depends on an infrastructure layer that makes health data accessible when needed where needed (data liquidity). [21] Accordingly, IDC expects the worldwide edge computing market to reach $250.6 billion in 2024 with a (12.5% CAGR) [24] with healthcare identified as one of the leading industries that will adopt edge computing. [16]

The healthcare market is in the middle of a fast digital transformation process. Drivers such as COVID, growing IoT adoption in healthcare, and underlying social mega-trends are pushing digital healthcare growth to new heights. Therefore, the digital healthcare industry faces many challenges, both technical and regulatory. At the same time the healthcare market is offered a wealth of opportunities.

Read the full report

References

[1] https://www.computerworld.com/article/3529427/how-iot-is-becoming-the-pulse-of-healthcare.html / https://www.gartner.com/en/documents/3970072
[2] https://www.accenture.com/us-en/insights/health/leaders-make-recent-digital-health-gains-last
[3] https://sifted.eu/articles/europes-healthtech-industry-2020/
[4] https://www.mobihealthnews.com/news/emea/health-tech-investments-will-continue-rise-2020-according-silicon-valley-bank
[5] https://news.crunchbase.com/news/for-health-tech-startups-data-is-their-lifeline-now-more-than-ever/
[6] https://www.accenture.com/us-en/insight-artificial-intelligence-healthcare%C2%A0
[7] https://www.grandviewresearch.com/industry-analysis/wearable-medical-devices-market
[8] https://www.marketsandmarkets.com/PressReleases/iot-healthcare.asp
[9] https://www.grandviewresearch.com/press-release/global-mhealth-app-market
[10] https://www.globenewswire.com/news-release/2020/05/23/2037920/0/en/Global-Digital-Health-Market-was-Valued-at-USD-111-4-billion-in-2019-and-is-Expected-to-Reach-USD-510-4-billion-by-2025-Observing-a-CAGR-of-29-0-during-2020-2025-VynZ-Research.html
[11] https://www2.stardust-testing.com/en/the-digital-transformation-trends-and-challenges-in-healthcare
[12] https://www.prnewswire.com/news-releases/healthcare-analytics-market-size-to-reach-usd-40-781-billion-by-2025–cagr-of-23-55—valuates-reports-301041851.html#:~:text=Healthcare%20Big%20Data%20Analytics%20Market,13.6%25%20during%202019%2D2025
[13] https://www.globenewswire.com/news-release/2020/11/25/2133473/0/en/Global-Digital-Health-Market-Report-2020-Market-is-Expected-to-Witness-a-37-1-Spike-in-Growth-in-2021-and-will-Continue-to-Grow-and-Reach-US-508-8-Billion-by-2027.html
[14] https://www.nasdaq.com/articles/iomt-meets-new-healthcare-needs%3A-3-medtech-trends-to-watch-2020-11-27
[15] https://go.forrester.com/blogs/predictions-2021-technology-diversity-drives-iot-growth/
[16] https://www.prnewswire.com/news-releases/state-of-the-edge-forecasts-edge-computing-infrastructure-marketworth-700-billion-by-2028-300969120.html
[17] https://news.crunchbase.com/news/for-health-tech-startups-data-is-their-lifeline-now-more-than-ever/
[18] https://www.gartner.com/en/newsroom/press-releases/2020-05-21-gartner-says-50-percent-of-us-healthcare-providers-will-invest-in-rpa-in-the-next-three-years
[19] https://www.idc.com/getdoc.jsp?containerId=prUS46794720
[20] https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-great-acceleration-in-healthcare-six-trends-to-heed
[21] https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-next-wave-of-healthcare-innovation-the-evolution-of-ecosystems
[22] https://www.cbinsights.com/research/internet-of-medical-things-5g-edge-computing-changing-healthcare/
[23] https://siliconangle.com/2020/12/08/future-state-edge-computing/
[24] https://www.idc.com/getdoc.jsp?containerId=prUS46878020
[25] https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality
[26] https://go.forrester.com/blogs/will-virtual-care-stand-the-test-of-time-if-youre-asking-the-question-its-time-to-catch-up/
[27] https://www.computerworld.com/article/3529427/how-iot-is-becoming-the-pulse-of-healthcare.html

ObjectBox Recognized as a Sustainable Profitable Tech Solution by the Solar Impulse Foundation

by Alyssa | Nov 30, 2020 | About us, Insights, IoT

ObjectBox is proud to be officially recognized as a sustainable and efficient solution by the Solar Impulse Foundation. Although we have self-identified as a #sustainabletech company since our induction, we’re proud to be recognized as an “efficient, clean and profitable solutions with a positive impact on environment and quality of life,” after taking part in an in-depth technical and business evaluation with the team at the Solar Impulse Foundation.

Empowering tech innovation

This label recognizes that ObjectBox empowers innovation with a highly efficient and sustainable technology. The Solar Impulse Efficient Label identifies sustainable tech solutions from around the world to help companies choose their tech stack responsibly.

solar-impulse-foundation-label-sustainable-software-for-the-edge

UN Sustainable Development Goals

All Solar Impulse awardees contribute to one or several of the UN Sustainable Development Goals; ObjectBox received the globally recognized label for supporting three of the Solar Impulse focused initiatives:

Affordable and Clean Energy: ObjectBox
Clean Water and Sanitation
Industry, Innovation and Infrastructure : ObjectBox
Sustainable Cities and Communities: ObjectBox
Responsible Consumption and Production

How is ObjectBox sustainable?

ObjectBox enables scalable and sustainable digitalization with a high performance edge database solution and synchronization solution. The ObjectBox database empowers local data storage, while ObjectBox Sync reduces unnecessary data traffic. ObjectBox is therefore ideally suited for efficient, useful, and sustainable Edge Computing.

Comparing the transmission of the same data sets, ObjectBox saves 20-60% on transmission data volume. By combining delta syncing with efficient compression based on standard and proprietary edge compression methods to keep data small, ObjectBox can reduce device energy consumption and thus CO2 emissions for data transmissions.

As our digital world grows, we all need to do what we can to structure these digital environments in an efficient and sustainable way. ObjectBox helps reduce digital waste. Digital waste unnecessarily burdens bandwidth infrastructure and fills cloud servers, forcing the expansion of cloud farms and in turn, contributing to the pollution of the environment. Therefore, we are excited to be part of the 1000solutions program.

Dr. Vivien Dollinger

CEO and Co-founder, ObjectBox

What does it mean to get a Solar Impulse Label?

The Solar Impulse Label: a label focused on both the environment and profitability

The first label to assess the economic profitability of products or processes that protect the environment. The Solar Impulse Efficient Solution Label is attributed following a strict selection process performed by external independent experts. By ensuring high standards of sustainability and profitability, this internationally recognized label is considered as a credible marker of quality for solution seekers in business and governments, facilitating their sourcing of solutions to reach environmental commitments.

About the Solar Impulse Foundation

The Solar Impulse Foundation aims to identify clean, efficient and profitable solutions in order to accelerate their implementation and the transition to a sustainable economy. Thanks to the awarding of a label with high standards of sustainability and profitability, the Foundation can support political and economic decision-makers in their efforts to achieve their environmental targets and encourage them to adopt more ambitious energy regulations, necessary for implementation at large-scale of these solutions on the market. A way to take the success of the first round-the-world solar flight further.

Interesting in finding out how ObjectBox can make your edge computing project more sustainable?

Get in Touch with our Team

What are Time Series Database Use Cases?

by Alyssa | Oct 20, 2020 | Edge Computing, Insights, IoT, Time Series

What do self-driving cars, smart homes, autonomous stock/crypto trading algorithms, or energy sensor systems have in common? These applications are all based on a form of data that measures how things change over time. It’s called time-series data and it plays a very important role in our lives today.

Accordingly, time-series databases also became a hot topic.

What is a time-series database?

A time-series database (TSDB) can be defined simply as a database optimized for storing and using time-stamped or time-series data. You don’t need to use a TSDB to work with time-series data. Any relational or NoSQL database or a key-value-store will do, e.g. MongoDB or redis. However, when dealing with time-series data (e.g. temperature, air pressure or car velocity data), a TSDB makes your life as a developer a hell of a lot easier.

Indeed, the two main reasons why TSDBs is the fastest-growing category of databases, are usability and scalability. A purpose-built time-series database typically includes common functions of time-series data analysis, which is convenient when working with time-series data. Because time-series data typically continually produces new data entries, data grows pretty quickly, and with high-frequency data or many time-series data sources, data ingestion quickly becomes a challenge. Time-series databases are optimized to scale well for time-series data with time being a common denominator and outperform any other database without specific time-series optimizations. This is why more and more people are adopting time-series databases and using them for a variety of use cases.

What are time-series database use cases?

Monitoring sensor data

One of the use cases is the monitoring of sensor data for safety measurements, predictive maintenance, or assistance functions. E.g. a car stores and uses all kinds of sensor data like tyre pressure, surrounding temperature and humidity for driver assistance and maintenance support. An aircraft monitors gravity and aerodynamic principles to reassure pilots that everything is alright – or to alert them that something has gone wrong. In fact, a Boeing creates on average half a terabyte of data per flight, most of which is time-series data. [1]

Tracking assets

Tracking assets is ideal for a time-series database as you constantly want to monitor where assets are, e.g. the cars of a fleet or any goods you might be stocking or shipping. These applications typically include unique vehicle or asset IDs, GPS coordinates, and additional metadata per timestamp. Apart from keeping track of the assets in realtime, you also can use the data for logistics and optimize e.g. your stocking and delivery processes.

Analyzing and predicting shopping behavior

Or, many e-commerce systems store all information of an item from product inventory, logistics data and any available environmental data to transaction amount, all items of the shopping cart purchased, to payment data, order information etc. In this case, a TSDB will be used to collect these large amounts of data and analyze them quickly to determine e.g. what to recommend to customers to buy next or optimize the inventory or predict future shopping behavior.

What are the most popular time series databases?

Well, here is our list of popular / established time series databases to use in 2020 to get you started:

InfluxDB: an open-source time series database, written in Go and optimized for high-availability storage and retrieval of time series data for operations monitoring, application metrics, IoT sensor data, and real-time analytics
KairosDB: a fast distributed scalable time series database written on top of Cassandra.
Kdb+: is a column-based relational time series database with a focus on applications in the financial sector.
Objectbox TS: superfast object persistence with time-series data on the edge. Collect, store, and query time-series data on the edge and sync selective data to / from a central location on-premise or in the cloud as needed.
TimescaleDB: an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL and packaged as a PostgreSQL extension with full SQL support.

For an overview of time-series databases currently available for productive use, see DB Engines. The database of databases is also a good resource if you are deeply interested in the database landscape; it is more extensive, but it includes any DB available independent of the level of support or if it is still maintained, also hobby projects.

What do you do when you have more than just time-series data?

Typically, a time-series database is not well suited to model non-time-based data. Therefore, many companies choose to implement two databases. This increases overhead, disk space, and is especially impractical when you deal with edge devices.

Time Series + Object-Oriented Data Persistence

Storing and processing both time series data and objects, developers can collect complex datasets and combine them with time-series data. Combining these data types gives a more complete understanding and context to the data – not just what happens over time, but also other factors that affect the results.

The best option is a robust object-oriented database solution that lets you model your data as it reflects the factual use case / the real world in objects and on-top is optimized for time series data. You can model your world in objects and combine this with the power of time-series data to identify patterns in your data. If this is indeed a database optimized for restricted devices and Edge Computing, you can even use this data in real-time and on the device. By combining time series data with more complex data types, an object time-series edge database can empower new use cases on the edge based on a fast and easy all-in-one data persistence solution.

Still have questions? Feel free to contact us here!

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[1] Time Series Management Systems: A Survey Søren Kejser Jensen, Torben Bach Pedersen, Senior Member, IEEE, Christian Thomsen

What Drives Edge Computing?

by Vivien | Aug 20, 2020 | Edge Computing, Insights, IoT

Data is exploding in every respect: in data volume, data velocity, and data variety (the 3 v’s). One driver of this phenomenon is the growing number of Mobile and IoT devices and thus, data sources. Making this data useful is one of the driving forces behind the adoption of Edge Computing. New use cases don’t only rely on using this data, but also upon the usability and speed of usability of this ever growing data. There are several practical challenges with this growing data volume that drive the adoption of Edge Computing:

New Use Cases Drive Edge Computing

Bandwidth Limitations

The existing network infrastructure cannot support sending all the data to the cloud. Particularly in urban areas there is a concentration of devices and data overburdens existing infrastructure. While 5G promises some relief, it is no hailbringer. First of all, if you want to implement your IoT project now, 5G is not yet available and many questions about 5G remain, e.g. pricing. But moreover, as the number of devices and data is growing ever faster, it is already clear that data volumes will outpace what 5G can support. Edge Computing will be an important technology alongside 5G to enable IoT.

Fast Data Requirements

Response time requirements are growing at the same time as data volumes are increasing. Sending data to the cloud for computation and storage means applications’ response times have a higher latency and depend on the network, which cannot guarantee response rates. Edge computing guarentees significantly faster data. Use cases that need speedy response times or guaranteed responses cannot rely on cloud computing. For example, in driver assistance, where every millisecond counts or in factory floors, where downtimes are too costly.

Sustainability

Sending data to the cloud and storing it there is inefficient and therefore costly – not only in plain €, but with regards to CO2 emissions too. The distance the data needs to travel needs hardware, connectivity and electric power. Therefore, sending data unnecessarily back and forth is wasteful beaviour and burdens the environment unnecessarily. With growing data volumes, that burden is growing. In fact, analysts predict that cloud computing data centers will consume as much as 21% of the total global energy by 2030. [1]

To scale your prototype, you need to move to the edge

At the start of IoT projects, quick prototyping, testing and piloting on early iterations of an application’s functionalities, can effectively be done in the cloud. However, in productive environments when applications scale it is often hard or impossible to keep cloud costs at scale, making the business not viable. Then it is time to move to the edge.

At the same time, decreasing hardware costs and hardware sizes are enabling more complex local computing, meaning there is less need for additional cloud usage. E.g. increasingly AI and ML is done at the edge, including model training.

data-volume-edge-computing-solution-iot-mobile

Data accessibility and Smart Syncing

Today’s successful businesses require a smarter approach to data management and integration. Data synchronization increases operational efficiencies, saving time and resources by eliminating redundant data transfer. With data synchronization, only predefined, useful parts of a data set are sent to a central instance. This means that while large volumes of data can be collected and analyzed locally, not all of this data is sent to and saved in the cloud. This reduces the impact on bandwidth, utilizes the local hardware resources for fast guaranteed response times, and keeps project cloud costs low – ultimately creating a more sustainable and efficient model of data architecture, enabling long term project scalability.

ObjectBox’ current database technology is enabling companies to persist and use data on edge devices, faster than any alternative on the market. It enables networks of edge devices working without a central instance, enabling even more new use cases.

[1] https://www.nature.com/articles/d41586-018-06610-y