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Posts Tagged ‘Big Data

As The Internet of Things continues to grow, huge amount of data is going to be generated. How huge is the “huge”? Really huge. I do mean that.

Physical devices across the globe are consuming and creating data to drive a continuously connected world. David Booth, CEO at BackOffice Associates believes that currently we are at the tipping point of the Internet of Things. He says, “It was not a big leap for the industry to realize that an IoT global network of continuously connected devices would mean that data would not only be created at geometric rates, but that it would become one of the most valuable commodities in the world.”

Alongside the fact that year 2016 was declared to be the year of the first Zettabyte in internet traffic, Cisco report says the number will reach 2.3 ZB by 2020. Before long we will be transferring this much data annually.

If it does not say anything to you, imagine a byte equals 1 character of text – a zettabyte would cover War And Peace by Leo Tolstoy(which is about 1,250 pages) at least 325 trillion times. Or if 1 gigabyte can store 960 minutes of music – technically a zettabyte would be able to store just over 2 billion years of music. If that still isn’t illustrative enough, let’s measure in cups of coffee. Cisco states that if the 11oz coffee on your desk equals to one gigabyte, a zettabyte would have the same volume as the Great Wall of China. This amount of information is mind-blowing. Zettabyte transformed Big Data into enormously Big Data.
 

The Internet of Things (IoT) is expanding rapidly and relentlessly. And as IoT grows, so do the volumes of data it generates. Ignoring this fact is not an option, and companies will do so at their own peril and risk.

Though there are many new start-up companies storing, analyzing and integrating massive amounts of big data created from the IoT, not many of them have actually considered how the IoT can and will transform organization thinking by implementing data quality and information governance.

With so much data being created, companies must understand what they want to do with it, what are their data requirements and ensure that they have access to the right data. Unless a company can find a way to accumulate, manage and, most important, monetize their data storage, data hoarding can be a real issue for them. Put simply, while the value IoT brings is in the information it creates, innovation gold lies in the filtered data an organization has extracted from the intermediate layer between the devices and the cloud (so called “fog”).

Obviously, data provides powerful potential for boosting analytics efforts. And analyzing the amount of data that is going to be created by the Internet of Things requires new, advanced analytic techniques. The good news is, artificial intelligence and cognitive computing are maturing at a fast pace.

When used properly analytics can help organizations translate IoT’s digital data into knowledge that will contribute to developing new products, offerings, and business models. IoT can provide useful insights into the world outside company walls, and help strategists and decision-makers understand their customers, products, and markets more clearly. It can drive so much more — including opportunities to integrate and automate business processes in ways never imagined before.

Rowan Trollope, Senior Vice President and General Manager of Cisco’s Internet of Things (IoT) and Applications, told participants at the Cisco Live conference, “One of the biggest mistakes you could make now is to underestimate the Internet of Things. This is a life or death issue for most of our customers. They have seen what has happened with Uber and taxi companies and with Netflix and Blockbuster”.

The bottom line is that IoT and Big Data can either disrupt your business or help you become more competitive compared to other businesses that are about to be disrupted.

 

alexandra-presniatsova

Alexandra Presniatsova

Business Development Manager

E-mail: Alex.Presniatsova@altabel.com
Skype: alex.presniatsova
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altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

As computers (and sensors) get smaller, smarter and connected, our everyday objects, from clothing to lavatories to cars, get more intelligent. By so doing embedded software is essential to the operation of today’s smart devices.
 

Embedded systems control many devices in common use today. Ninety-eight percent of all microprocessors are manufactured as components of embedded systems. Manufacturers ‘build in’ embedded software in the electronics of e.g. cars, telephones, modems, robots, appliances, toys, security systems, pacemakers, televisions and set-top boxes, and digital watches, for example.

Embedded systems are not always standalone devices. Many embedded systems consist of small parts within a larger device that serves a more general purpose.

 
Specifics of embedded development:

  • The development of embedded systems requires a good combination of industry knowledge, up-to-date technology expertise and excellent quality and project management skills.
  • Code is typically written in C or C++, but various high-level programming languages, such as Python, JavaScript and even the Go programming language, are now also in common use to target microcontrollers and embedded systems. However the complexity is not in the lines of code, most of the times, since embedded software is more focused towards controlling and managing the system (or hardware).
  • Programmers spend nearly all of their time using their embedded software development environment, which is an integrated collection of software development tools that manage the entire embedded software development process: analyzing, designing, documenting, writing, compiling, debugging, testing, optimizing, and verifying software. The choice of an embedded software development environment is the most important determinant of the productivity and effectiveness of programmers.
  • Today’s embedded systems development spans sensor, device, gateway, and cloud. This dramatically increases the complexity of development, troubleshooting, and fault isolation.
  • Unlike smartphones and personal computers, which sells in millions, most embedded products such as ECG machines, PoS machines, Laboratory and Test equipment, Ticket vending machines, etc. have low sales volume.
  • Furthermore, the product life of embedded devices ranges to 7+ years in contrast to the 15-18 months life for smartphones and to 4-6 years life for laptops. Due to this limited sales volume and long product life, custom or chip-based development of embedded devices adds significant overheads in terms of supply chain inefficiencies, platform obsolescence, non-optimal cost structure, and barriers to adopt latest technologies.

 
Embedded vs. application software development
 

Embedded software development

Application software development

Embedded software is physically part of a device, loaded by the manufacturer, and cannot be changed or removed by the user.

Application software is an optional program that the user chooses, installs and can remove.

It’s important to consider not only algorithm performance, but also the overall system robustness, reliability, and cost in the architecture and design. It’s closely associated with hardware manufacturing. You can’t write embedded software in your bedroom and unleash it on the world. Either you make a device yourself, or you work for someone who does.

Application software is similar and different. You can do it for yourself or for The Man, with the difference that no manufacturing is involved so there is much less capital outlay.

Embedded software however is often less visible, but no less complicated. Unlike application software, embedded software has fixed hardware requirements and capabilities, addition of third-party hardware or software is strictly controlled. To manage quality risk, as well as to meet tighter standards for software certification, embedded software engineers need to leverage software simulation tools and certified code generators.

Application software is usually less complex than embedded devices. It has more flexible requirements and solutions.

Embedded systems often reside in machines that are expected to run continuously for years without errors and in some cases recover by themselves if an error occurs. Unreliable mechanical moving parts such as disk drives, switches or buttons are avoided.

Therefore the application software for personal computers is usually developed and tested less scrupulously.

Embedded software may use no operating system, or when they do use, a wide variety of operating systems can be chosen from, typically a real-time operating system. This runs from small one-person operations consisting of a run loop and a timer, to LynxOS, VxWorks, BeRTOS, ThreadX, to Windows CE or Linux (with patched kernel).

Standard computers generally use operating systems such as OS X, Windows or GNU/Linux.

 

Hot trends for Embedded s/w development: Big Data, Internet of Things, Connected Cars and Homes

The amount of data that’s being created and stored on a global level is almost inconceivable, and it just keeps growing, yet only a small percentage of data is actually analyzed.

The importance of BD doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable cost and time reductions, new product development and optimized offerings, and smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

  • Determining root causes of failures, issues and defects in near-real time.
  • Generating coupons at the point of sale based on the customer’s buying habits.
  • Recalculating entire risk portfolios in minutes.
  • Detecting fraudulent behavior before it affects your organization.

Big data affects organizations across practically every industry, from Banking, Education and Government to Health Care and Retail industry, etc.

The Internet of Things is yet another ubiquitous word in the world of embedded technologies. The core of IoT is the availability of the application or thing and its data to be a connectable ecosystem.

– For example, the Connected Home also known as the Smart Home, uses modern automation systems to provide a practical way of controlling electronic devices in the home. Connected Homes technology can include but is not limited to the scheduling and automatic operation of heating, security systems and lighting. This advanced technology allows these vital home functions to be controlled remotely from anywhere in the world using an internet connected device.

– The race to build the fully Connected Car, and ultimately the completely Autonomous vehicle, is also under way. Drivers around the world are getting used to the increasing amount of digital technology in their cars. Many of the normal features of the car such as monitors of performance data like speed, fuel efficiency, and gas tank levels; heating and air conditioning; and the audio system — all have been digitized in hopes of providing the driver with easier operation and better information. And the car, including smartphones and other devices carried onboard by drivers and passengers now reaches out to the surrounding world for music streamed from the cloud, real-time traffic information, and personalized roadside assistance. Recent innovations allow automobiles to monitor and adjust their position on the highway, alerting drivers if they are drifting out of their lane, and slowing down if they get too close to the car in front of them.

Naturally, smart homes, smart cars, and other connected products won’t just be aimed at home and private life. They’ll also have a major impact on business.

 
Conclusion

We’re just beginning to imagine the possibilities of embedded systems. Innovations in sensors, big data, and machine learning now make it possible for engineering teams to develop smarter and more autonomous systems that have the potential to dramatically improve designs and create new categories of products and services previously unimaginable.

Embedded software engineers develop embedded hardware and software solutions, custom-made for applications in various target markets. With capabilities that span the complete system and software lifecycle, Altabel Group is placed to manage entire projects from start to finish, working closely with customers to understand their needs and deliver excellent results. For more information on our work in the industry, please click here.

Thank you! And you’re always welcome with your questions.

 

Victoria Sazonchik

Victoria Sazonchik

Business Development Manager

E-mail: victoria.sazonchik@altabel.com
Skype: victoria_sazonchik
LI Profile: Victoria Sazonchik

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

As the Internet of Things begins to revolutionize businesses, economies and our society, IoT platforms are coming up being the core basis in the overall IoT infrastructure. IoT platforms, in simple words, are just about connecting the sensors to data networks and integrating with back-end applications to provide insight into huge volumes of data.

However developing for the Internet of Things is a complicated undertaking, and almost nobody chooses to do it from scratch. IoT data platforms provide a starting point by integrating many of the tools needed to operate a deployment from device control to data prediction and grasp into one service. Ready-built IoT platforms can meet the needs of any company and smoothly accommodate constant growth and change. In the light of the possibilities offered by IoT, many high tech companies started taking advantage of it. For the time being there are more than 300 hundred various IoT platforms on the market and the number is continuing to grow. So, let’s see what features of IoT platforms take into consideration while choosing one for your business.

Before selecting an appropriate solution which may be suitable for your organization, you must determine:

1. Three different types of IoT platforms. Here they are listed from most complex to least complex:

  • Application enablement and development (AEP/ADP): This encompasses platforms that offer modules, widget-based frameworks or templates for producing (with minimal or no coding) actual end-user applications. These platforms are capable of turning data into either intelligence or action very quickly. The vivid examples of such platforms are Oracle, ThingWorx and etc.
  • Network/Data, and subscriber management (NM): In the wireless carrier and mobile virtual network operator (MVNO) space, this kind of platforms try to streamline connecting cellular M2M data, so you don’t have to build much of the data infrastructure behind it. For instance Cisco and Aeris do network management as well as device management, while Jasper and Wyless do more sheer network management.
  • Device management (DM): These platforms are more about monitoring device statuses, troubleshooting issues, configuring embedded device settings and administrating the provisioning and health of the endpoints. Usually in the IoT space this fairly elementary software is provided by hardware vendors. Like both Digi and Intel provide pure device cloud management.

While these platforms can be found as distinct standalone products, it is becoming increasingly common to find vendors that combine two or all three types in a single offering.

2. Implementation, integration support and device management. Device management is one of the most significant features expected from any IoT software platform. The IoT platform should maintain a number of devices connected to it and track their proper operation status; it should be able to handle configuration, firmware (or any other software) updates and provide device level error reporting and error handling. Ultimately, users of the devices should be able to get individual device level statistics.

To make implementation smooth, the provider should possess convincing manuals, blogs and feasibly lively developer-community around the IoT platform.

Support for integration is another vital feature expected from an IoT software platform. The API should provide the access to the important operations and data that needs to be disclosed from the IoT platform. It’s typical to use REST APIs to achieve this aim.

3. Comprehensive Information Security. There are four main technological building blocks of IoT: hardware, communication, software backend and applications. It’s essential that for all these blocks security is a must-have element. To prevent the vulnerabilities on all levels, the IoT infrastructure has to be holistically designed. On the whole, the network connection between the IoT devices and the IoT software platform would need to be encrypted and protected with a strong encryption mechanism to avoid potential attacks. By means of separation of IoT traffic into private networks, strong information security at the cloud application level, requiring regular password updates and supporting updateable firmware by way of authentication, signed software updates and so on can be pursued to enhance the level of security present in an IoT software platform. Nonetheless while security ought to be scalable, it is unfortunately usually a trade-off with convenience, quick workflows and project cost.

4. Flexible Database. There are four major “V” for databases in IoT space:

  • Volume (the database should be able to store massive amount of generated data)
  • Variety (the database should be able to handle different kind of data produced by various devices and sensors)
  • Velocity (the database should be able to make instant decisions while analyzing streaming data)
  • Veracity ( the database should be able to deal with ambiguous data in some cases produced by sensors)

Therefore an IoT platform usually comes with a cloud-based database solution, which is distributed across various sensor nodes.

5. Data analytics.

A lot of IoT cases go beyond just action management and require complicated analytics in order to get the most out of the IoT data-stream. There are four types of analytics which can be conducted on IoT data:

  • Real-time analytics (on the fly analysis of data),
  • Batch analytics (runs operations on an accumulated set of data),
  • Predictive analytics (makes predictions based on different statistical and machine learning technologies)
  • Interactive analytics (runs numerous exploratory analysis on either streaming or batch data)

While choosing the right IoT platform, it’s better to keep in mind that the analytics engine should comprise all dynamic calculations of sensor data, starting from basic data clustering to complex machine learning.

6. Pricing and the budget. The IoT platform market features a diversity of pricing methodologies underlying various business strategies. And sometimes providers’ costs aren’t always transparent. Thus it’s very important to check out all the nuances of your provider’s pricing pattern, so you are not plainly bought into introductory teaser rates or into the prices for the base model.

Further you should bear in mind that you licensing cost for the chosen platform is just the beginning. The major expense can turn out to be the integration itself, as well as hiring consultants (if you are not able to do it on your own) to support the system.

Therefore, it’s extremely vital to brainstorm what your entire IoT system will look like at scale and choose which features are most critical to you chiefly — and only afterwards decide what sort of platform you need.

A lot of companies do this backward. They get the IoT platform and believe they’re getting the complete necessary solution—then realize the mistake half a year into development. Thus it’s critical to be aware of this before you get started.

Also it should be mentioned that some companies don’t use IoT platforms—they’re developing their own platforms in-house. Yet, depending on how you want to go to market, it may be clever to research pre-built options. Depending on your situation, you may save a lot of time and money by partnering with one of these platforms.

Have you ever faced the difficulties of choosing the IoT platform for your business? If yes, can you please let me know what kind of difficulties? And what do you think is it better to use a ready-built IoT platform or develop your own from the scratch? Looking forward to getting your ideas and comments.

 

Anastasiya Zakharchuk

Anastasiya Zakharchuk

Business Development Manager

E-mail: anastasiya.presnetsova@altabel.com
Skype: azakharchuk1
LI Profile: Anastasiya Zakharchuk

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

If the experts’ estimates regarding IoT are correct, it means that in 5-10 years there will be more than 50 billion interconnected devices in the world. And they all will generate zettabytes of data, which can be and should be collected, organized and used for various purposes. Hence the tight correlation between IoT and Big Data is hard to ignore, because IoT and Big Data are like Romeo and Juliet – they are created for each other. The unprecedented amount of data produced by IoT would be useless without the analytic power of Big Data. Contrariwise, without the IoT, Big Data would not have the raw materials from which to model solutions that are expected of it.

What are the impacts of IoT on Big Data?

The IoT revolution means that almost every device or facility will have its own IP address and will be interconnected. They are going to generate a huge amount of data, spewing at us from different sides – household appliances, power stations, automobiles, train tracks and shipping containers etc. That’s why the companies will have to update technologies, instruments and business processes in order to be able to cope with such great amount of data, benefit from its analysis and finally gain profit. The influence of Big Data on IoT is obvious and it is conducted by various means. Let’s take a closer look at the Big Data areas impacted by IoT.

Methods and facilities of Data Storage

IoT produces a great and stable flow of data, which hits companies’ data storage. In response to this issue, many companies are shifting from their own storage framework towards the Platform as a Service (PaaS) model. It’s a cloud-based solution, which supports scalability, flexibility, compliance, and an advanced architecture, creating a possibility to store useful IoT data.

There are few options of models in the modern cloud storage: public, private and hybrid. Depending on the specific data nature, the companies should be very accurate while choosing a particular model. For instance, a private model is suitable for the companies who work with extremely sensitive data or with the information which is controlled by the government legislation. In other cases, a public or hybrid option will be a perfect fit.

Changes in Big Data technologies

While collecting the relevant data, companies need to filter out the excessive information and further protect it from getting attacked. It presupposes using highly productive mechanism that comprises particular software and custom protocols. Message Queue Telemetry Transport (MQTT) and Data Distribution Service (DDS) are two of the most widely used protocols. Both of them are able to help thousands of devices with sensors to connect with real-time machine-to-machine networks. MQTT gathers data from numerous devices and puts the data through the IT infrastructure. Otherwise, DDS scatters data across devices.

After receiving the data, the next step is to process and store it. The majority of the companies tend to install Hadoop and Hivi for Big Data storage. However there are some companies which prefer to use NoSQL document databases, as Apache CouchDB and others. Apache CouchDB is even more suitable, because it provides high throughput and very low latency.

Filtering out redundant data

One of the main challenges with Internet of Things is data management. Not all IoT data is relevant. If you don’t identify what data should be transmitted promptly, for how long it should be stored and what should be eliminated, then you could end up with a bulky pile of data which should be analyzed. Executive director of Product Marketing Management at AT&T, Mobeen Khan, says: “Some data just needs to be read and thrown away”.

The survey carried out by ParStream (an analytical platform for IoT) shows that almost 96 % of companies are striving to filter out the excessive data from their devices. Nevertheless only few of them are able to do it efficiently. Why is it happening? Below you can see the statistics, depicting the main problems which most of the companies are facing with the data analysis procedure. The percentage figure points out the percentage of the respondents to the ParStream survey confronting the challenge.

• Data collection difficulties – 36%
• Data is not captured accurately – 25%
• Slowness of data capture – 19%
• Too much data to analyze in a right way – 44%
• Data analyzing and processing means are not developed enough – 50%
• Existing business processes are not adjustable to allow efficient collection – 24%

To perform the action of filtering out the data effectively, organizations will need to update their analysis capabilities and make their IoT data collection process more productive. Cleaning data is a procedure that will become more significant to companies than ever.

Data security challenges

The IoT has made an impact on a security field and caused challenges which can’t be resolved by traditional security systems. Protecting Big Data generated from IoT arouses complications as this data comes from various devices, producing different types of data as well as different protocols.

The equally important issue is that many security specialist lack experience in providing data security for IoT. Particularly, any attack can not only threaten the data but also harm the connected device itself. And here is the dilemma when a huge amount of sensitive information is produced without the pertinent security to protect it.

There are two things that can help to prevent attacks: a multilayered security system and a thorough segmentation of the network. The companies should use software-defined networking (SDN) technologies combined with network identity and access policies for creating a dynamic network fragmentation. SDN-based network segmentation also should be used for point-to-point and point-to-multipoint coding based on the merger of some software-defined networking and public key infrastructure (SDN/PKI). In this case data security mechanisms will be keeping pace with the growth of Big Data in IoT.

IoT requires Big Data

With the emerging of IoT step by step many questions arises: Where is the data coming from IoT going to be stored? How is it going to be sorted out? Where will the analysis be conducted? Obviously, the companies which will be able to cope with these issues the next few years are going to be in prime position for both profits and influence over the evolution of our connected world. The vehicles will become smarter, more able to maintain larger amounts of data and probably able to carry out limited analytics. However as IoT grows and companies grow with IoT, they will have many more challenges to resolve.

What do you think about the evolving of Big Data in IoT? Have you already experienced the challenges of Big Data in IoT? And do you have any ideas about the progressive solutions to these challenges? I’ll be happy to hear your opinion in the comments below. Please, feel free to share your thoughts.

 

Anastasiya Zakharchuk

Anastasiya Zakharchuk

Business Development Manager

E-mail: anastasiya.presnetsova@altabel.com
Skype: azakharchuk1
LI Profile: Anastasiya Zakharchuk

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

The new trend for many medical practices is obtaining an EHR (Electronic Health Record) system. While there are many practitioners still using files and travel cards, EHR provides better efficiencies for billing, reimbursements, audits etc. Admittedly, there are more systems then doctors but acquiring an EHR allows better practice efficiencies and perhaps more money for the practice.
In this post we highlighted the most important EHR trends to see unfold this year. Thus, we expect wearables, telemedicine and mobile medicine to continue to advance. They’ll be joined by cloud computing, patient portals and big data.

Telemedicine and wearables plus EHR

The telemedicine market is forecasted to exceed $30 billion in the next five years, as providers increasingly see the need to reach seniors and patients in rural areas. Telemedicine offers tons of value to seniors. It improves care by getting it to remote patients who live far from hospitals. It also enables homebound patients to get high-quality care. It makes care cheaper, and allows seniors to stay at home longer. It benefits providers by making their jobs more flexible. And it also eliminates picking up new illnesses in a clinical care setting.

Wearables’ mass adoption has made store-and-forward telemedicine much easier. Devices like Fitbits automatically collect valuable health data. Store-and-forward telemedicine just means that data goes to a doctor or medical specialist so they can assess it when they have time.

EHRs are going mobile

More and more providers want to provide medical care from their smartphones, and more patients want to access data through mobile devices. Contributing factors to the popularity of mobile devices include their affordability, ease of use and portability (meaning they are easy to carry between patient exams to access electronic patient information). One of the other drivers of mobile technology in healthcare is the availability of myriad apps for smartphones and tablets. For each of the major smartphone operating systems, there is now an app for almost every conceivable healthcare need, ranging from drug dose calculators to fully functioning electronic medical records. Healthcare apps play a pivotal role in changing the utility of mobile devices. They’re transforming smartphones or tablets to medical instruments that capture blood test results, medication information, glucose readings, medical images, enabling physicians and patients to better manage and monitor health information. Healthcare apps are clearly taking on more mainstream health IT functions and have moved beyond sporadic use by early adopters.
From these facts we may conclude that EHRs will offer better mobile design and functionality.

More EHRs will move to the cloud

Start-up costs for EHRs can prove burdensome for some institutions, while cloud-based tools offer minimal start-up costs and can make better use of providers’ current resources. The cloud also enables better continuity of care. Cloud-based software means you can access records from outside the office. It makes mobile access possible. It makes transferring records a snap. And it makes updating software seamless for providers.

In the coming year, more and more EHRs will offer cloud services.

More EHRs will provide patient portals

Though patient portal usage got off to a slow start in 2013, in last two years it grew in popularity.

While about half of physicians offer patient portals right now, almost another fifth of them plan to offer one in the next 12 months. In a 2015 survey of more than 11,000 patients, 237 physicians, and nine payer organizations representing 47 million lives, almost a third of patients said they were interested in using a patient portal to engage with their physician, track their medical history and receive educational materials and patient support.

More providers will both offer and promote patient portals. Some may even have patients use the portals during office visits to begin getting their data into the system. And patients will start to see their value. Educating patients on how and why to use portals will be the key to getting them to use it.

Big data will reveal more connections

Personalized medicine enabled by big data is an emerging trend in healthcare. Innovation will continue apace in 2016.

Personalized medicine focuses on analyzing a person’s genome, environmental, social, biometrical, and religious influencers, and determining a treatment for the individual based on that data. It’s about moving from a one-size-fits-all approach to instead creating micro-buckets of patients by analyzing their medical records and genome sequences, and treating patients based on the research and records of how other patients in similar situations have reacted. Big data is working to identify the behaviors, risk factors, and early indicators of disease so doctors can prevent it more effectively.

Big data is only the first step. That data must be cleaned and structured so it can reveal patterns in factors that influence outcomes.

Conclusion

Moving forward, technology will continue to transform the healthcare industry as it plays a key role in new healthcare delivery models. EMR/EHR, mHealth, telemedicine, and many others identified will continue to increase their footprint in this growing industry. Where do you see Healthcare IT over this year? What EHR trends are you most excited about and what trends did I miss? Let me know in the comments!

 

Svetlana Pozdnyakova

Business Development Manager

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

The stumbling block for many companies and the reason why organizations fall behind in the planning and pre-planning stages of big data, appears to be confusion on how best to make big data work for the company and pay off competitively.

With all the talk about rapid deployment and breakneck business change, there can be a tendency to assume that businesses are up and running with new technologies as soon as these technologies emerge from proof of concept and enter a mature and commercialized state. However, the realities of where companies are don’t always reflect this.

Take virtualization. It has been on the scene for over a decade-yet recent research by 451 Research shows that only 51 percent of servers in enterprise data centers around the world are virtualized. Other recent survey data collected by DataCore shows that 80 percent of companies are not using cloud storage, although cloud concepts have also been with us for a number of years.

This situation is no different for big data, as reflected in a Big Data Work Study conducted by IBM’s Institute of Business Value. The study revealed that while 33 percent of large enterprises and 28 percent of mid-sized businesses have big data pilot projects under way, 49 percent of large enterprises and 48 percent of mid-sized businesses are still in big data planning stages, and another 18 percent of large enterprises and 38 percent of mid-sized businesses haven’t yet started big data initiatives.

The good news is that the study also showed that of those organizations actively using big data analytics in their businesses, 63 percent said that the use of information and analytics, including big data, is creating a competitive advantage for their organization–up from 37 percent just two years earlier.

The stumbling block for many and the reason why organizations fall behind in the planning and pre-planning stages of big data, appears to be confusion on how best to make big data work for the company and pay off competitively.

Big data projects need to demonstrate value quickly and be tightly linked to bottom line concerns of the business if big data is to cement itself as a long-term business strategy.

In far too many cases when people plan to build out a complete system and architecture before using a single insight or building even one predictive model to accelerate revenue growth. Everyone anticipates the day when Big Data can become a factory spitting out models that finally divulge all manner of secrets, insights, and profits.

So how do you jump start your big data efforts?

Find big data champions in the end business and business cases that are tightly constructed and offer opportunities where analytics can be quickly put to use.

When Yarra Trams of Melbourne Australia wanted to reduce the amount of repair time in the field for train tracks, it placed Internet sensors over physical track and polled signals from these devices into an analytics program that could assess which areas of track had the most wear, and likely would be in need of repair soon. The program reduced mean time to repair (MTTR) for service crews because it was able to preempt problems from occurring in the first place. Worn track could now be repaired or replaced before it ever became a problem-resulting in better service (and higher satisfaction) for consumers.

Define big data use cases that can either build revenue or contribute to the bottom line.

Santam, the largest short-term insurance provider in South Africa, used big data and advanced analytics to collect data about incoming claims, automatically assessing each one against different factors to help identify patterns of fraud to save millions in fraudulent insurance payments.

Focus on customers

There already is a body of mature big data applications that surround the online customer experience. Companies (especially if they are in retail) can take advantage of this if they team with a strong systems integrator or a big data products purveyor with experience in this area.

Walmart and Amazon analyze customer buying and Web browsing patterns for help in predicting sales volumes, managing inventory and determining pricing.

 

Kristina Kozlova

Marketing Manager

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

WHAT

In today’s business and technology world you can’t have a conversation without touching upon the issue of big data. Some would say big data is a buzzword and the topic is not new at all. Still from my point of view recently, for the last two-three years, the reality around the data has been changing considerably and so it makes sense to discuss big data so hotly. And the figures prove it.

IBM reports we create 2.5 quintillion bytes of data every day. In 2011 our global output of data was estimated at 1.8 billion terabytes. What impresses it that 90 percent of the data in the world today was created in the past two years according to Big Blue. In the information century those who own the data and can analyze it properly and then use it for decision-making purpose will definitely rule the world. But if you don’t have the tools to manage and perform analytics on that never-ending flood of data, it’s essentially garbage.

Big data is not really a new technology, but a term used for a handful of technologies: analytics, in-memory databases, NoSQL databases, Hadoop. They are sometimes used together, sometimes not. While some of these technologies have been around for a decade or more, a lot of pieces are coming together to make big data the hot thing.

Big data is so hot and is changing things for the following reasons:
– It can handle massive amounts of all sorts of information, from structured, machine-friendly information in rows and columns toward the more human-friendly, unstructured data from sensors, transaction records, images, audios and videos, social media posts, logs, wikis, e-mails and documents,
– It works fast, almost instantly,
– It is affordable because it uses ordinary low-cost hardware.

WHY NOW

Big data is possible now because other technologies are fueling it:
-Cloud provides affordable access to a massive amount of computing power and to loads of storage: you don’t have to buy a mainframe and a data center, and pay just for what you use.
-Social media allows everyone to create and consume a lot of interesting data.
-Smartphones with GPS offer lots of new insights into what people are doing and where.
-Broadband wireless networks mean people can stay connected almost everywhere and all the time.

HOW

The majority of organizations today are making the transition to a data-driven culture that leverages data and analytics to increase revenue and improve efficiency. For this a complex approach should be taken, so called MORE approach as Avanade recommends:
-Merge: to squeeze the value out of your data, you need to merge data from multiple sources, like structured data from your CRM and unstructured data from social news feeds to gain a more holistic view on the point. The challenge here is in understanding which data to bring together to provide the actionable intelligence.
-Optimize: not all data is good data, and if you start with bad data, with data-driven approach you’ll just be making bad decisions faster. You should identify, select and capture the optimal data set to make the decisions. This involves framing the right questions and utilizing the right tools and processes.
-Respond: just having data does mean acting on it. You need to have the proper reporting tools in place to surface the right information to the people who need it, and those people then need the processes and tools to take action on their insights.
-Empower: data can’t be locked in silos, and you need to train your staff to recognize and act on big data insights.

And what is big data for your company? Why do you use it? And how do you approach a data-driven decision-making model in your organization?

Would be interesting to hear your point.

Helen Boyarchuk

Helen Boyarchuk
Helen.Boyarchuk@altabel.com
Skype ID: helen_boyarchuk
Business Development Manager (LI page)
Altabel Group – Professional Software Development


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