Archive for the ‘Big Data’ Category
The demand for healthcare services is growing at rapid pace due to constantly increasing number of people with chronic diseases. These days approximately every one of two individuals has one or more chronic diseases, and one of four has two or more chronic conditions. At the same time, there are more medical information today about different diseases and their treatment options than ever before.
According to IBM, healthcare data doubles every 2 years. It is also calculated that doctors would have to read 29 hours each workday to keep up with new professional insights. Obviously while dealing with this huge information flow, doctors don’t have enough capacities to decide how appropriate an option might be for a specific patient.
Additionally, the most expensive part of healthcare is the human resources, which adds to the supply-and-demand issues. I guess no one will doubt the fact that professional healthcare is costly.
These insights bring up several questions. How can we benefit from explosion of information in healthcare industry? Is it possible to cut the costs for people who seek healthcare treatment without sacrificing the quality of such services? Or even improving it? How do we find a balance after all?
The answer lies in two words: cognitive computing. It is a system that can handle massive amounts of unstructured data to enable a new class of data interpretation and learning systems. Cognitive systems process information by comparing it to a teaching set of data. So that the more data such a system can analyze, the more it learns, and therefore the more accurate it becomes with the course of time. To mimic the way the human brain works cognitive systems use data mining, pattern recognition and natural language processing.
The main advantage of these machine-learning systems is their ability to find patterns in datasets too large and complex for human brains to embrace. For doctors this means assistance of paramount importance in keeping track of records and making accurate clinical decisions. IDC predicts that by 2018 somewhat 30 percent of healthcare systems will be running cognitive analytics against patient data and real-world evidence to personalize treatment regiments. What’s more, IDC projects that during the same year physicians will tap cognitive solutions for nearly half of cancer patients and, as a result, will reduce costs and mortality rates by 10 percent.
For patients the ability of cognitive computing to act as an advisor and give an additional opinion allows an extra level of assurance in the service provided by the healthcare sector. Eventually the patients will have more confidence in the service they are receiving. Besides, involving cognitive computing into healthcare means availability of remote check-ups, including areas with relatively little healthcare provision. It is predicted that in the U.S., for example, in the nearest future 40% of primary care encounters will be delivered virtually, which will be possible thanks to cognitive systems.
Summing up, cognitive computing can help:
- Healthcare specialists to manage all the data that is available to make more precise conclusions over the patients’ conditions
- Patients by advising, and providing answers to the questions they have
- Decrease costs for healthcare services
As data becomes more complex and diversified, cognitive computing will have an incredible impact on the healthcare industry.
In conclusion, let me give you one single real-life example. Watson (famous IBM cognitive system used to diagnose patients) was able to determine a rare form of leukemia in an old woman, while oncologists at the University of Tokyo had puzzled for about a year over her illness. After analyzing 20 million research papers Watson came up with the proper diagnosis. It took the system no more than ten minutes. Impressive, isn’t it?
Business Development Manager
Professional Software Development
When a technical term is used more and more frequently the exact definition becomes “blurred” and its true meaning is usually greatly distorted.
This what happened to the term ‘business intelligence’ or BI. Ever since, when the term had only appeared, the development of technologies has substantially expanded our understanding of BI and of what advantage and benefit the company can retrieve from their available data.
So, what does ‘business intelligence’ mean today? How it could be useful for companies and how to apply its underlying ideas correctly to ensure the steady growth of efficiency and profitability of a business?
What is business intelligence? Why is it important?
BI consists of two completely diverse, but at the same time complementing one another aspects.
- Value for the business.
Implies how companies can use the available information in order to multiply profit and efficiency and bring new products and services to the market successfully.
- IT strategy.
Includes the idea of what technological solutions to apply in order to achieve greatest possible utility of BI.
Presentation of data in a specific format for efficient usage by the company has always been a challenging task. For many organizations, it is quite complex to determine what particular information is required for a specific use.
Such business analysis requires certainty in methodologies and goals.
Earlier BI resources were limited by the lack of available data collection technologies. Nevertheless, modern technologies such as big data, analytics, mobile services and cloud computing in their combination allow obtaining a continuous flow of detailed information quite fast and with no serious investments.
Still, the current bottom line lies in extracting some valuable sense from these data and, in many respects, it is much more complicated than collecting information itself.
Five efficiency criteria of BI-system (and BI-strategy)
1. While selecting a BI-system one should be guided by the real needs of a particular company
The most common and at the same time the most dangerous mistake is when the BI-systems dictate the strategy of their usage. As a result, the company gets plenty of non-synchronized applications, awkward interface and the infrastructure that is already out of date, yet so entrenched in the IT system that could be barely substituted.
2. Be flexible
Flexible model of the integration of the appropriate software involves constant repetition of certain operations with the gradual development of the system. This allows companies to evaluate the success of the project at any point of time, to determine at what stage it is and towards what it moves.
As a rule, creating, testing and integration of BI-technologies goes much more smoothly when the company receives real-time feedbacks from all the running processes and is able to make required adjustments on the fly. It is vital for BI-systems!
3. User-friendly interface
BI-solutions focus on collection, visualization and management of the data.
Usually, when it comes to large amounts of numeric information companies face a risk to get exceptionally technical, inconvenient and incomprehensible data for the “illiterate” users of the system. This information is highly functional, but impractical, especially when it is badly integrated with other applications.
Integration is a key point in deploying BI-technologies. In case the interface is non-intuitive, complex and inconvenient for the end users, BI-system will definitely work inefficiently.
There is a tendency to allocate significant resources for the integration of the latest technologies promising unprecedented results. However, such investments potentially may do more harm than good. Intelligent, targeted and smooth integration is the key to avoid serious errors during implementation.
4. BI is a tool available to everyone
BI has been long used by completely different users, not only by experts with appropriate education and experience. BI-system should be simple and easy to understand to everyone.
For this purpose, companies have to attain the convenience of analytics and the reports drawn on its basis; it should be simple and demonstrative. The collected data should be presented in the way so that any user could easily make definite conclusions.
5. Centralize your data
The desire to achieve the result, based on useful information implies proper data handling. Receiving data from multiple sources and storing it in a centralized information DB, capable of filtering, sorting and removing the unnecessary is critical for the deployment of the applications involved into making business decisions. Apart from that, risk management also becomes more effective through transparency and structure.
General excitement over BI is evident
The role that IT plays in the world has significantly changed over the past few years thanks to the information ‘boom’. Still, construction of a technological infrastructure is not enough for successful data management.
That is why, ‘business intelligence’ it is not just a fashionable term it is a concept that demonstrates the need to move beyond the paradigm of a separate, isolated existence of data analysis and business goals.
In fact, BI reminds us that technologies and business must be closely linked, so that the business goals and business guidelines predetermine the choice of software and, the software in return would provide useful information leading business to success.
Business Development Manager
Professional Software Development
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.
Professional Software Development