The growth of Big Data
Data is not just the new oil, but the new land too. In short, data is perhaps the most important resource to have in this century. With billions of data points and information being collected across the world every second through the internet and other avenues, the data size is increasing manifold. The upcoming technology is focusing on how to organize and sort this huge amount of data to derive insights and read patterns.
This, in effect, is referred to as Big Data Analytics Courses. Every major or minor firm, big or small player, in the consumer retail sector to healthcare and financial series, is using insights generated out of this big data to shape and grow their businesses. The lending business is no exception and can benefit immensely from the use of data. Fin-tech is changing the way the banking industry operates and making banking operations smoother, automated and more cost-effective. From fraud mitigation to payment solutions, Fintech is changing the way we think about banks.
Data in lending business
From the origination of the role to its continuation and life cycle management data can drive decision making in lending business. The patterns that can be read out of consumer data can predict the loans requirement, the capability of repayment of loans, the frequency of late payments or defaults and even the need for the consumers to refinance their loans. The fin-tech start-ups have already begun using the data in such a way, and hence the alternative lending businesses have bloomed over the last few years. Many banks are either merging with such alternative business lenders or taking the help of third party service providers to help boost their capabilities and skills to use big data analytics in business.
The areas of thrust
The major areas where lending business can be aided through the use of big data analytics are the portfolio risk assessment, stress tests, default probabilities and predicting the loan patterns of consumers. Credit card business already uses such technology extensively in assessing and evaluating their consumers.
For example, the credit card issuers tracked the repayments data of the users and based on the profession or the region; they may at times predict if the balances are going to be resolved or if they are going to be paid up front. They then design their marketing strategies keeping the results of analytics in mind in those areas or regions or regarding those specific consumers.
In the bygone years, the only way banks used to evaluate the creditability of a prospective borrower was to assess his or her records of past loans and repayment history. However, with new real-time data points, banks can study behavioural patterns and take appropriate decisions. Refinancing loans is another important area where technology and finance have come together to make life easier for consumers and banks alike.
The algorithms can predict when a borrower may need to refinance his loans and can credit the amount in his account within seconds without all the paperwork and unnecessary delays. Another area that has transformed with the advent of big data and technology is the internal auditing of banks. With a digital record of every transaction or decision-making process, compliance rules and regulations are now easier to adhere to and track.
Lastly, and perhaps most importantly, customer feedbacks have become important in this industry like never before. The algorithms can sift through loads and loads of data in the form of feedbacks and can implement solutions to enhance customer experiences on a real-time basis. Technology has changed almost everything around us and the lending operations to are no exception to the rule. In the years to come, banking may undergo a drastic transformation with elements that at this time, we may even be unable to imagine.
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How Machine Learning Is Saving The Indian Vernacular ?
In a nation riddled with countless cultures, unending dialects and infinite separations, the term ‘melting pot’ comes to mind. It’s common for the typical Indian being confused with the local tongues when treading into unfamiliar territories.
Fortunately for the millions of Indians beguiled by such problems, machine learning courses and a number of data science tools is proving to be a much-needed relief for preserving and keeping those languages intact.
Connecting Data To Language
Big Data
This has significantly boosted the outlook for interdisciplinary research that has allowed researchers across the country to link the aspects of linguistics and fragment all dialects to a condensed format that can be edited easily.Until now, several companies have taken to using an aggregator system to create a platform that translates the language into any other without sacrificing minor details. Several years ago, a research project under the name Technology Development for Indian Language was created by the government to scrape all the major Indian languages for data science purposes.
- One such platform that has been making strides is the e-Bhasha platform that is making content available for citizens in their language. It was created as a big data project in 2015 and has become a starting point for many linguistic researchers.
- As the number of internet users in India grew more than 28 per cent and is expected to be a $6.2 billion industry per year, international groups are jumping on the bandwagon to appeal to the common man.
Playing With The Locals
Seeing the enormous benefits of tapping into local consumers, big groups like Google set out to create the Google Brain which is essentially an extensive neural network to develop human language from the get-go.
- Aspects of this have been incorporated into Google Assistant as well, having translated content from more than 500 million monthly users and 140 billion words per day in as many 158 languages.
- The craze began by the year 2013 when e-commerce was still taking root in the country and was challenged by the numerous languages that consumers had in the country.
- Websites like Flipkart and Snapdeal dealt with local language content for mobile websites as far back as 2015.
- Reports suggest that Marathi, Gujarati, Tamil, Punjabi and Malayalam represented over 75 per cent of searches on Google in the very same languages. What’s even more interesting is that more than 73% of people surveyed are willing to go completely digital if the system communicates in their own language.
- Facebook has raised the number of Indian languages for posting to almost 12 but still lacks regional pages that use the same kind.
- Small firms in India are collecting as much textual Corpus for languages available using translation services like Reverie, Process9 and IndusOS.
The Technology Used
- Most companies would confess to the use of neural networks for developing such programs, but the primary machines behind such global endeavors has been some rather sophisticated algorithms.
- The newest additions to the industry happen to be some enhanced versions of the Hadoop MapReduce extension. A significant feature of the software is the ability to find linguistic linkers between similar words and compound phrases which makes translations more concrete. Some stellar packaged additions to the SPSS Modeler system too have taken place that is helping companies handle large corpuses.
- At the same time, marketing groups are using modified techniques to feed invoice data collected from average consumers which are being sent into what’s being called a ‘global corpus data set.’
- Likewise, teams across the country in data collection firms are hiring data collection engineers to converse and accumulate conversational audio recordings both in rural and urban areas.
- The main subject remains heavily invested in cross-directional neural networks many of which are using data analysis tools and machine learning tools like Tensor Flow from Google and IBM Watson.
10 High Value Use Cases for Predictive Analytics in Healthcare
Healthcare organisations are having their moment when it comes to Big Data and the potential it offers through its analytical capability. From basic descriptive analytics, organisations in this sector are leaping towards the possibilities and its consequent perks of predictive insights. How is this predictive analysis going to help organisations and patients? What are the top roles a Data Analyst look for?
Let’s break this down into ten easy pointers:
- Predicting Patient Deterioration
Many cases of infection and sepsis that are being reported among the patients, can be easily predicted via predictive insights that Big Data offers. Organisations can use big data analytics to predict upcoming deteriorations by monitoring the changes in the patient’s vitals. This helps in the recognition and treatment of the problem even before there are visible symptoms.
- Risk Scoring for Chronic Diseases
Based on lab testing, claims data, patient-generated health data and other relevant determinants of health, a risk score is created for every individual. What this does, is that it leads to early detection of diseases and a significant reduction in treatment costs.
- Avoiding Hospital Re-admission Scenarios
Using predictive analysis, one can deduce risk factor(s) indicating the possibility for re-admission of the patient to the hospital within a certain period. This helps the hospitals design a discharge protocol which prevents recurring hospital visits, making it convenient for the patients.
- Prevention of Suicide
The Electronic Health Records (EHR) provides enough data for predictive algorithms to find the likelihood of a person to commit suicide. Some of the factors influencing this score are substance abuse diagnose, use of psychiatric medications and previous suicide attempts. The early identification helps in providing the mental health care potential risk-patients will need at right time.
- Forestalling Appointment Skips
Predictive analysis successfully anticipates ‘no-shows’ when it comes to patients and this helps prioritise giving appointments to other patients. The EHR provides enough data to reveal individuals who are most likely to skip their appointments.
- Predicting Patient Utilization Patterns
Emergency departments of regular clinics have varying staff strength according to the fluctuations in patient flow. In this case, predictive analysis helps to forecast the utilization pattern and the requirements of each department. It improves patient wait-time and utilisation of facilities.
- The Supply Chain Management
Predictive analysis can be used to make efficient purchasing which in turn has scope for massive cost-reduction. Such data-driven decisions can also help in optimizing the ordering process, negotiate the price and reduce the variations in supplies.
- Development of New Therapies and Precision Medicine
With the aid of predictive analysis, providers and researchers can reduce the need of recruiting patients for complex clinical trials. The Clinical Decision Support (CDS) systems have started to predict the patient response to treatments by analysing genetic information and results of previous patient cohorts. It enables the clinicians to select treatments with more chances of success.
- Assuring Data Security
By using analytic tools to monitor the data access and utilization pattern, it is possible to predict the chances of a potential cyber threat. The system detects the presence of intruders by analysing changes in these patterns.
- Strengthen Patient Engagement and Satisfaction
Insurance companies encourage healthy habits to avoid long-term high-cost diseases. Here, predictive analyses help in anticipating which communication programmes would be the most effective in each patient by analysing past behavioural patterns.
These are possible perks of using tools like predictive analyses in healthcare that optimise processes, increase patient satisfaction, enable better care mechanisms and reduce costs. The role of Big Data is clearly essential as demonstrated and a targeted use can show high-value results!
Healthcare’s Top 10 Challenges in Big Data Analytics
Healthcare’s Top 10 Challenges in Big Data Analytics
There are multiple perks to Big Data analytics. Specifically, in the domain of healthcare, Big Data analytics can result in lower care costs, increased transparency to performance, healthier patients, and consumer satisfaction among many other benefits. However, achieving these outcomes with meaningful analytics has already proven to be tough and challenging. What are the major issues slowing down the process and how are they being resolved? We will discuss the top 10 in this article.
Top 10 Challenges of Big Data Analytics in Healthcare
- Capturing Accurate Data
The data being captured for the analysis is ideally expected to be truly clean, well-informed, complete and accurate. But unfortunately, at times, data is often skewed and cannot be used in multiple systems. To solve this critical issue, the health care providers need to redesign their data capture routines, prioritise valuable data and train their clinicians to recognise the value of relevant information.
- Storage Bandwidth
Typically, conventional on-premises data centres fail to deliver as the volume of healthcare data once reaches certain limits. However, the advancement in cloud storage technology is offering a potential solution to this problem through its added capacities of information storage.
- Cleaning Processes
Currently, the industry relies on manual data cleaning processes which takes huge amounts of time to complete. However, recently introduced scrubbing tools for cleaning data have shown promise is resolving this issue. The progress in this sector is expected to result in automated low-cost data cleaning.
- Security Issues
The recurring incidents of hacking, high profile data breach and ransomware etc are posing credibility threats to Big Data solutions for organisations. The recommended solutions for this problem include updated antivirus software, encrypted data and multi-factor authentication to offer minimal risk and protect data.
- Stewardship
Data in healthcare is expected to have a shelf life of at least 6 years. For this, there is a need an accurate and up-to-date metadata of details about when, by whom and for what purposes the data was created. The metadata is required for efficient utilisation of the data. A data steward should be assigned to create and maintain meaningful metadata.
- Querying Accesses
Biggest challenges in querying the data are caused by data silos and interoperability problems. They prevent querying tools from accessing the whole repository of information. Nowadays, SQL is widely being used to explore larger datasets even though such systems require cleaner data to be fully effective.
- Reporting
A report that is clear, concise and accessible to the target audience is required to be made after the querying process. The accuracy and reliability of the report depend on the quality and integrity of data.
- Clear Data Visualization
For regular clinicians to interpret the information, a clean and engaging data visualization is needed. Organisations use data visualization techniques such as heat maps, scatter plots, pie charts, histogram and more to illustrate data, even without in-depth expertise in analytics.
- Staying Up-to-Date
The dynamic nature of healthcare data demands regular updations to keep it relevant. The time interval between each update may vary from seconds to a couple of years for different datasets. It would be challenging to understand the volatility of big data one is handling unless a consistent monitoring process is in place.
- Sharing Data
Since most patients do not receive all their care at the same location, sharing data with external partners is an important feature. The challenges of interoperability are being met with emerging strategies such as FHIR and public APIs.
Therefore, for an efficient and sustainable Big Data ecosystem in healthcare, there are significant challenges are to be solved, for which solutions are being consistently developed in the market. For organisations, it is imperative to stay updated on long-term trends in solving Big Data challenges.