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Posts Tagged ‘Big Data

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

The IT world continues to sprint forward at an unrelenting pace and these are its five hottest trends so far in 2012. Let’s count them down.

5. The projectization of IT

Projects have always been a major part of IT, but in the past there were also a lot of IT resources dedicated to keeping the lights on and keeping the world running. Companies now take those operational aspects of IT for granted and want that existing infrastructure automated as much as possible and for as cheaply as possible. There’s little glory or job security in keeping the company’s existing systems on life support.
That’s why outsourcing and the cloud are such hot commodities. They allow companies to offload IT operational costs and focus their IT staff on the next project to upgrade systems, streamline business processes, and launch new IT projects to transform the business. More than ever, IT is all about the projects. It’s about the vendors that can help support IT projects (and there are infrastructure jobs for IT pros there). It’s about the business analysts and project managers who can organize people and resources to pull off projects on time and on budget. It’s about the CIOs who now base their budget and staffing decisions largely on projects rather than just the cost of keeping the server room running.

4. PC/Mobile convergence

Employees are more mobile than ever. There are a lot of factors driving that, from increased telecommuting to work/life balance where people leave early to pick up their kids and then work the rest of the afternoon from a cafe or the stands at the soccer field. There are also industries such as transportation and health care that have always had lots of non-desk employees and have had to shoe-horn computing solutions into their work environments.
The growing capabilities of smartphones and tablets are filling many of these needs as these mobile devices become more able to do the tasks of a full PC. Still, there are times when workers can be even more productive when working with a full keyboard and mouse. That’s why we’re beginning to see the rise of products like Motorola Webtop (a Smartphone docking solution), Ubuntu for Android (desktop OS embedded in a Smartphone), and Microsoft Surface (a tablet with a kickstand and keyboard cover). The lines between traditional PCs and mobile devices continue to blur.

3. Desktop thinning

Let’s be honest. The proliferation of mobile devices and the Bring Your Own Device trend has created a lot of headaches and nightmare scenarios for the IT department. For companies that need stronger security and more control over the employee environment, one of the easiest answers to the problem is to move to solutions like desktop virtualization or terminal services from vendors like VMware, Citrix, and Microsoft.
That allows the IT department to create a standard environment with all the company apps that employees can access from a company PC, their home PC or laptop (over VPN), or even a tablet or Smartphone. The environment looks and feels like a traditional PC but the apps and all the data remain on the company servers which are more secure and easier for the IT department to manage and troubleshoot. This technology has been around for years, as “thin clients.” But there are three factors driving it forward in 2012: 1.) BYOD, 2.) mobile devices, and 3.) it lets companies delay PC upgrades since it pushes all of the heavy lifting to the servers. So companies still aren’t going to thin clients in large numbers, but their desktop environments are getting a lot thinner.

2. Big Data

If “Cloud Computing” has been the overhyped and overused IT term of the last several years, the new buzz phrase of 2012 is “Big Data.” Like Cloud, Big Data gets abused by marketers. The main thing you need to understand when it comes to Big Data is that it’s about bringing together the “structured” internal data that your company has always used for its reports and mixing it with public “unstructured” data like social media streams and freely available government data (on traffic, agriculture, crime, etc.).
The act of combining these two types of data can give you new insights into how your customers feel about your products versus your competitors (from the social media streams), it can help you anticipate changes in product demand, it can help you use government trending data to anticipate the growth or decline of markets, and more. That’s why Big Data is such a big deal. But, don’t be fooled. It’s still in its infancy. There aren’t a ton of great commercial tools yet that can help you harness Big Data. It takes the right IT pros who know how to work some data magic and they are in high demand.

1. Cloud, cloud, and cloud

There are essentially three types of clouds — the full Internet cloud (some call it the “public cloud”), the private cloud (which looks a lot like a traditional data center, but with lots of virtualization), and the hybrid cloud (an integrated mix of public and private clouds). Make no mistake; all three types of clouds are thriving in 2012. The public cloud is the one that most people think of when they hear “cloud” and it’s mostly about hosted apps like Salesforce.com and Workday.com as well as Internet-hosted infrastructure like Rackspace and Amazon AWS. But, we’re increasingly seeing traditional IT players like Microsoft, IBM, and HP quietly become big players in the cloud as well.
The private cloud and the hybrid cloud are for larger companies and organizations that need stronger security or have legacy apps that are not easily moved or migrated to the cloud. Both of these types of cloud solutions are picking up steam, especially in companies that have already moved their easy stuff to the cloud and are now digging in and dealing with some of the big, expensive, entrenched stuff.

What are the hottest IT trends in your world so far in 2012?

Best Regards,

Kristina Kozlova

Marketing Manager

 

altabel

Altabel Group

Professional Software Development

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

There is no doubt that 2012 will be another big year for BI and information management. In the article we`ve tried to gather what we suppose are the top BI trends for near future

Big Data → Need for Speed

The rise in volume (amount of data), velocity (speed of data) and variety (range of data) gives way to new architectures that no longer only collect and store but actually use data: on-demand or real-time BI architectures will replaces traditional datawarehouses. Successful business intelligence projects will need to consider Big Data as part of their data landscape for the value that it delivers. More and more organizations will look toward statistics and data mining to set strategic direction and gain greater insights to stay ahead of the pack.At the same time the BI user is expecting faster answers from their BI environment disregarding the fact that the size of data is increasing.

Shift from analytical BI to operational BI

Increased adoption of cloud and mobile BI encourage individuals to access their KPI dashboards (key performance indicators), more often. An operational dashboard works much like a car’s dashboard. As you drive, you monitor metrics that indicate the current performance of your vehicle and make adjustments accordingly. When the speed-limit changes, you check your speedometer and slow down, or when you see you are out of gas you pull over and fill-up. Likewise, an operational dashboard allows you to make tactical decisions based on current performance, whether it is chasing a red-hot lead or ordering an out-of-stock product.

Data democracy

Latest surveys showed that only 25% of employees in businesses that adopted BI had access to that tool. And that is not because they didn`t want to or didn`t need information, but because traditional BI tools have been too bulky and technical for that other 75% of employees to use.
As now organizations more and more are adopting cloud and mobile BI dashboards, this situation is likely to change. Business intelligence is heading towards simpler, more straightforward methods and tools..

Agile

An Agile approach can be used to incrementally remove operational costs and if deployed correctly, can return great benefits to any organization. Agile provides a streamlined framework for building business intelligence/data warehousing (BIDW) applications that regularly delivers faster results using just a quarter of the developer hours of a traditional waterfall approach.

It allows you to start a project after doing 20 per cent of the requirements and design that deliver 80 per cent of the project’s value. The remaining details are filled in once development is underway and everyone has a good look at what the challenges actually are.

BI going mobile

In a survey conducted by Gartner, it was found that by 2013 one-third of all BI usage will be on a mobile device, such as a smart-phone or tablet. BI users want to access their data anytime and anywhere. This puts a demand on both the backend of any BI solution (like datawarehouse appliances) but also on the frontend where information access and visualization must be possible.

BI going up to the Cloud

As Cloud computing continues to dominate the whole IT landscape, so BI also dominates in the Cloud . Throughout next few years adoption of cloud BI tools will be driven by a number of important factors. First, cloud-based solutions offer the advantage of being relatively simple and convenient to deploy. Second, cloud tools are more easily scalable to provide access to key performance indicators (KPIs) to everyone in your organization, no matter where they are or what device they are using. Lastly, continually improving security measures will put to rest any reservations businesses have with storing their sensitive data in the cloud.

We believe these above enumerated areas will grow over the next few years. Organizations will embrace the Agile approach, utilizing new tools and technologies to decrease delivery times and demonstrate substantial business value. As we put more data into the Cloud, big data will become standard. Data itself will be delivered to satisfy the desires of users, so access from mobile devices will dominate desk-based consumption. The businesses that embrace these new business intelligence trends, and take steps to change and adapt the way data is hosted, analyzed, utilized and delivered, will be the ones that grow and prosper in the near future.

And what are your predictions for the big business intelligence trends in the next few years? Do you agree/disagree with our predictions?

Kind regards,
Anna Kozik – Business Development Manager (LI page)
Anna.Kozik@altabel.com
Altabel Group – Professional Software Development


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