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Tag: Big Data & Analytics

Predictions For The Future Of Big Data

Posted on by Imarticus Learning

While a lot of experts believe that there’s some great stuff in store for the future of big data, it is also true that technology will be greatly advancing throughout 2017. This is why there are a number of complex facets of big data, which are increasing by the day. Various attributes of big data, such as artificial intelligence and cloud computing, are believed to have a huge impact on big data analytics. There are a number of factors that exhibit the potential to change or more likely determine the direction at which big data is moving. For instance, there will soon be a number of customers who would replace the businesses, in demanding various amounts of data, to look for the cheapest hotels and understanding climate issues and similar concepts. There is a very acceptable idea today, of a reality where it would be the customers, the common man, if you may, who would be demanding personalized, tailored artificial intelligence technology, to suit their particular needs and demands.
While these seem like mere examples, with a tinge of realism, there are absolute chances of these becoming a reality very soon.
Ten years ago, all the data that was ever generated and accumulated, made up the highest denominations of storage space, which was namely Gigabytes or Terra-bytes, but the recent few years have made an explosion of data, into what is known as exabytes; this term roughly refers to billions of Gigabytes of data. This is where we derive the term ‘big data’ from, it is to denote the humongous amounts of data that has been generated, all over the world, in such a short amount of time. Regardless of whatever happens in other aspects of this field, one thing we can be absolutely certain of. That is, that data will be continuously growing, which means that soon there will come a time, when we will be talking about Zettabytes, which roughly amounts for a trillion Gigabytes.
Artificial Intelligence began its advent, as just a buzzword which was continuously used by sci-fi movie enthusiast and was mainly used to refer to technology only seen in sci-fi movies and the likes. Today, this term is no longer reserved for those, who are obsessed with technological gizmos, or those involved in science. It has very well become a part of our everyday lives, through various examples, like Google’s Allo, Microsoft’s Cortana and Apple’s Siri. There are absolute indicators that AI has full potential of transforming, from something nice to have to very essential technology to have. There are so many changes and futuristic developments that big data can make today, as well as in the future.
One of the biggest prediction is the fact that big data can result in various advanced applications for fields of national security, customer behavior tracking, weather forecasting, HR, sports, health and so on.

One prediction is definitely going to happen, which is that big data will have a better, smarter and a huge impacting role to play in the future.

Loved this blog? Try these blogs as well –
What’s Machine Learning All About?
Is Big Data Really Changing The World?

10 Most Popular Analytics Tools In Business

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The increasing importance and demand for data analytics have opened up new potential in the market. Each year, new tools and programming languages are being launched aimed at easing up the process of analyzing and visualizing the data.

While many such advanced business intelligence tools come up in paid versions, there are great free and open-source data analytics courses and tools available in the market too. Read on to find out about the 10 best and most popular data analytics tool for business right now.

1. R Programming
R is the most popular programming language cum tool widely used by experts for the purpose of data analytics and visualization. The tool is free and open-source in nature and allows the users to alter its code set for clearing bugs and updating the software on their own.
2. PYTHON
Python is an open-source and free OOP based scripting language popular in the data analytics market since the start of the 90s. Python supports both structured and functional programming methods and is very easy to learn and operate upon. Python is expert in handling text-based data.
3. Tableau Public
Tableau Public is another free software and business intelligence tool which is capable of connecting all kinds of data source be it Excel-based data, Data Warehouse or web-based data. Tableau creates maps, graphs and dashboards with real-time updates presenting on the web. The data can be shared over social networks too.
4. SAS
Sas is a leading analytics tool and programming language specifically developed for the purpose of interacting with and manipulating data by the SAS institute in 1966 with updates presented during the 80s and 90s. Data present in SAS can be accessed, analyzed and managed easily from any sources and is capable of predicting behaviors of customers and prospects along with recommending optimized communication models.
5. Excel
One of the most popular and underrated data analytics and visualization tool in the market, Excel was developed by Microsoft as part of their MS Office and is one of the most widely used tools in the industry. All kinds of data analytics tools still require Excel to work in some kind of way and it is very easy to be learnt and operated.
6. KNIME
KNIME is a leading open source and integrated analytics tool developed by a team of software engineers from the University of Konstanz in January 2004. KNIME allows the users to analyze and model the data through visual programming integrating components of data mining and machine learning via its modular data pipelining concept.
7. Apache Spark
Developed in 2006 by the Berkeley’s AMP Lab of University of California, Apache is a fast large-scale data processing, analysis, and visualization tool capable of executing applications around 100 times faster in memory and 10 times faster on disk. It is popular for data pipelining and machine learning models development allowing it to double up as business intelligence tool.
8. RapidMiner
RapidMiner is another powerful data analytics tool which can double up as business intelligence tool owing to its capability to perform predictive analysis, behavioral analysis, data mining, etc. The tool can incorporate with any other data source types such as Excel, Microsoft SQL, ACCESS, Oracle, Ingres, IBM SPSS, Dbase, etc.
9. Google Analytics
A freemium and widely recommended product for data analytics, Google Analytics is a perfect offering from Google for the Small and Medium-scale enterprises who don’t possess the technical knowledge or the means to gather that knowledge in the present course.
10. Splunk
Splunk is an analytics tool mostly directed to searching and analyzing machine-generated data. The tool pulls up all text-based log data and provides the means to search through it for gathering any relevant or required data.

Why do Corporates Need Big Data Analytics Training?

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Why do Corporates Need Big Data Analytics Training?

Data is an invaluable organizational asset in modern times since big-data is being exploited for its value in providing business foresight and predictions based on analytics.

ML, AI, and deep learning algorithms are applied to massive data volumes to provide corporations with the opportunity to use their data to tweak their measurable efficiency, and the decision-making process, and also reach out to a larger number of employees at the same time.

And, data analytics training on big data is no single program. It is a technology combination that helps the corporations extract the best value from data and analytics tasks. 

Corporate advantages:
The important plus factors of data analytics for corporations are explained briefly below.

Database Management: Data can be from different sources and in various formats. Its quality and organizations are of prime importance before going in for big data analytics. 

Since in most large corporations the data travels from department to department, and various subsets may be added or deleted as the case may be, it becomes necessary to have an established repeatable process and a master management program format to maintain the quality of the organizational data. This ensures that the management of data is in synchronization across the organizations.

Mining and modeling of financial data: The technology for this task allows the examination of several petabytes of data produced by the moment. This task will enable you to sift through the data for relevance, and then use the subset for predictions and forecasts which hasten the decision process and in turn impact informed decisions being taken for critical and strategic decisions by the management.

Pricing and modeling using ML: ML trains the machines and AI to help it learn to recognize the patterns involved. This hastens its self-learning process and allows the algorithm to automatically move through more complex data models and still deliver accurate and desired outcomes.

This Big Data Analytics Training capacity is invaluable, especially where unknown tasks and risks are involved or where models need to be continuously auto-generated.

Storage and engineering of Big-data: Hadoop is a commonly-used, free and open-source framework which uses clustering of information on hardware to store larger amounts of data. Since data continually increases in volumes, types, and sources, the Hadoop models used for computation handles very big-data volumes and needs no license. It thus allows for using demographics, sensor data, driver data and market information all on the same platform. The best example here is of the 2000 crisis in Ford and how it overtook the competition in Asian and European markets.

Product development and analytics in-memory: Instead of using the hard disk, such a facility allows the access of data from the system memory instead. This allows quick decisions, analytics and predictable outcomes from the organization’s data. One of the most significant advantages of the system memory is that it is iterative, agile, removes latencies in processing and data preparation while providing for quicker and better decisions and analysis that is interactive especially in product development and modeling.

The task of Predictive Analysis: Here, the technology used consists of algorithms based on statistical modeling and ML techniques. Large corporates use their data for gainful business outcomes based on Big Data Analytics to make the best decision in any given scenario.

Marketing, risk assessment, and fraud detection are just some of the areas that benefit from such analysis. Were you aware that Singapore based OCBC used such insights to achieve a new customer increase rate of 40 percent?

HR capabilities and mining texts: The latest improvements are used to analyze data from text messages drawn from the surveys, comments, Twitter, web posts, emails, blogs, books, and such text-using sources. Such analytics is beneficial in strategizing for competitive leadership, the introduction of new products, newer areas for development, and establishing loyal customer relationships both within and outside the organizations.

Parting notes:
Training and cleaning of data are very important to organizations to take quick and effective decisions at the right time, especially when it comes to strategic and critical business decisions. Since data analytics comprises of a series of technological programs executed in systematic models, it is essential to do a data-analytics course before one makes a career in this field. The scope for such jobs is indeed never-ending because of the sheer volumes of data being generated and available for analysis.

Doing your course with the reputed Imarticus Learning ensures you are job-ready, proficient in data analytics and get a chance to hone your presentation skills too through the soft-skills modules. Top this with certification, and you are all set to start a great lucrative career. Do you have any more doubts? Get in touch with Imarticus today.

Big Data Analytics With Hadoop

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Hadoop has been around forever; right from the early days of data analytics and the big data analytics, Hadoop has been an integral part and well-known name in the IT and data analytics industry. Formally known as Apache Hadoop, it is an open source software developed partly in partnership with Apache Software Foundation. 

Today, the software is known across the globe and is used in managing data processing as well as storage for big data applications which run on clustered systems. Hadoop being a well-known name in the data analytics industry is at the center of a dynamic market whose need for big data analytics is constantly increasing. The main factor that contributes to the wide use of Hadoop in data analytics is its ability to handle and manage various applications like predictive analytics, data mining as well as machine learning. 

A feature that distinguishes Hadoop from all other tools available in the market is its ability to handle both structured and unstructured data types, thus giving users increased flexibility for collecting, processing and analyzing big data, which conventional systems like data warehouses and relational databases can’t provide. 

Hadoop and Data Analytics 

As mentioned in the introductory paragraphs, Hadoop is essentially an analytics software for big data and can run on massive clusters of servers, thus providing the user with the ability to support thousands of nodes and humongous amounts of data. Since its inception in the mid-2000s, Hadoop has become an integral part of all data analytics operations mainly because of its significant features like managing nodes in a cluster, fault tolerance capabilities and many more. 

Hadoop due to its wide range of capabilities is a very good fit for any big data analytics application. Due to its capacity to handle any form of data, be it structured or unstructured, Hadoop can handle it all. One of the most notable applications of Hadoop includes its use in customer analytics. With Hadoop, users can predict anything, be it customer churn, analyze click-stream data or analyze and predict the results of an online ad. 

Top Big Data Analytics Tools

Although Hadoop is at the center of big data analytics, there are many notable tools in the market that are definitely worth checking out. Some of the most significant ones are as mentioned below. 

  • R Programming

After Hadoop, R is the leading data analytics tool in the market today. Available in Windows, Mac as well as Linux, R is most commonly used in statistics and data modelling. 

  • Tableau Public

 Tableau Public is an open source, free data analytics tools that have the capability to seamlessly connect data warehouses, Excel or any other source and display all the data on a web-based dashboard with real-time updates. 

  • SAS

SAS is the global leader in data analytics for many years and is widely known for its easy accessibility and manipulation capabilities. 

Conclusion

Hadoop and Big Data Analytics are terms that are synonymous with each other. With Hadoop and the right source, a user can analyze any type of data imaginable. 

Analytics and Agriculture

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Agriculture drives the Indian economy with a whopping population of nearly 70% in rural areas and 40% being part of the agricultural workforce. However, it has many issues and hurdles in realizing its full potential and leveraging analytics and technology for it. The sector lacks banking, financial, disaster management, and water inadequacy facilities and infrastructure. Also due to lack of education migration to cities is a major issue. Though in the early stages the policymakers were quick to realize the potential of analytics and technology in mitigating the hardships of farmers and slowly but steadily the combination is appearing to slow down and address the agriculture segment pressing issues.
Use of Big Data Analytics:
Data is the life breath of all activities in modern times and in agriculture too. Leveraging the potential of analytics and Big Data can bring about immense changes in agriculture and its productivity. The frequent news-releases on droughts, crop failures, farmer suicides and such acute results of backward farming and agriculture stresses the need for the involvement of technology and big data in improving the lot of the farmers and agriculture segment. Be it crop patterns, wind directions, crop loss mitigation, soil adequacy, and fertility, it is Big Data analytics that has offered solutions using technologies like

  • Cloud and Nanocomputing
  • Big data, digitalization and visualization use.
  • AI, IoT and ML use.
  • Saas Platforms, cloud services, and web-based apps.

Role of data and the data analyst:

Agriculture is interdisciplinary and combines concepts of business management, chemistry, mathematics, statistics, physics, economics, and biology. Like all interdisciplinary sectors, the need for data and its use is crucial for growth, change, and development. This means that like in other segments the data analyst role is both well-paying, has an unending scope and relies on a variety of latest futuristic technologies and smart apps.
Knowledge of sciences, agriculture methods, biotechnology, animal and soil sciences, etc will definitely aid the analyst. The analyst will also need proficiency in analysis techniques, data prepping and predictive analysis.
Analytical technologies in the agriculture sector can be used effectively in 

  • Capturing data: using the IoT, biometrics, sensors, genotyping, open and other kinds of data, etc.
  • Storage of Data: using data lakes, Hadoop systems, Clouds, Hybrid files and storage, etc.
  • Transfer of Data: via wireless and wifi, linked free and open source data, cloud-based solutions, etc.
  • Analytics and Transformation of data: through ML algorithms, normalization, computing cognitively, yield models, planting solutions, benchmarks, etc.
  • Marketing of data and its visualization.

What is Smart Farming?

Smart Farming uses analytics, IoT, Big Data and ML to combine technology and agriculture applications. Farming solutions also offer

  • ML and data visualization techniques.
  • App-based integration for data extraction and education.
  • Monitoring through drones and satellites.
  • Cloud storage for securing large volumes of data.

Smart Farming technologies and analytics can thus be efficiently used for forecasts, predictions for better crop harvests, risk mitigation, and management, harvest predictions, maximizing crop quality, liaising and interconnectivity with seed manufacturers, banks, insurers, and government bodies.

What is Precision Agriculture?

This methodology is about Crop Management which is site-specific and also called ‘Farming using Satellites’. The information from satellites helps distill data regarding topography, resources, water availability, the fertility of the soil, nitrogen, moisture and organic matter levels, etc which are accurately measured and observed for a specific location or field. Thus an increase in ROI and optimization of resources is possible through satellite aided analytics. Other devices like drones, image files from satellites, sensors, GPS devices, and many more can prove to be helpful aids and are fast becoming popular.

Concluding with the challenges:

Though the technologies are the best the implementation and applications to the agriculture sector are lacking. Education and training of the farmers is the best solution but involves a lot of man-hours, uninterrupted power, use of data efficiently, internet connectivity, and finance to help these measures succeed and develop to their full potential. Right now it is in the nascent stage and the need for data analysts is very high.  To get the best skill development training courses in data analytics do try Imarticus Learning which is a highly recommended player with efficient, practical skill-oriented training and assured placements as a bonus. Where there is a will the way will show up on its own. Hurry and enroll.

What jobs can you get with a Data Analytics degree?

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The Data Analytics industry is one of the fastest growing sectors, proving to be a job provider to thousands of potential professionals every year.

Therefore, upon the successful completion of a Data Analytics degree, there are various job options that you can explore. Some of these have been deeply detailed in the following paragraphs. Let’s have a look. 

  1. Gaining a Big Data Analytics course or degree can give you a winning career as a Business Analyst – As a Business Analyst, you will be handling responsibilities such as Database management, cleaning up of data sets and organizing them.

    Creation of data visualizations, that convey information in an engaging visual manner to the audience. You will also be responsible for building models that explain the interaction of various variables, and this will be used for companies future references. 

  2. Operations Research Analyst – An Operations Research Analyst methodically uses data mining, data modeling, data optimization, and statistical analysis to help companies, corporates and organizations run cohesively and efficiently. Their major responsibility also includes streamlining of operations processes, minimizing waste and optimizing source models. Operations Research Analysts are also called as Operations Analysts, Operations Business Analysts, and Business Operations Analysts. 

  3. Quantitative Analyst – A Quantitative Analyst usually handles the finance department using, applying and implementing trading strategies and assesses risk factors and help/guides in generating maximum profits.

    They are deeply involved in the usage and designing of mathematical models that give financial firms and organizations to price and trade securities accordingly.

    You will require skills such as a great aptitude in mathematical statistics and finances, calculus, and machine learning. These will be the basics of your career as a successful Quantitative Analyst.

  4. Market Research Analyst – Studying market trends and conditions, and observing them carefully to forecast the profitability and revenue of a certain new product or new service is a job role carried out by Market Research Analysts.

    With their skill sets, tools and techniques they research and are able to predict market trends, market downfalls, measure the precise market success of various products and services, and thus identify potential markets where the said product/service can become a future success. 

    This helps organizations, corporates, and global companies understand market trends and make a fruitful profit for themselves, while making a positive impact on the society at large, with their respective product/service. 

Through individual coaching, guidance, and mentorship, you can explore many career advantages through a valid degree course in Data Analytics. These degrees usually have strategic career partnerships with industry relevant global companies and organizations (data analytics course with placement) that will help you mold you step by step process as a Data Analyst. 

You will also gain deep practical learning through internships and first-hand exposure in a corporate set up. Networking and socializing for career connects is an important task which you will be able to do through interacting with professionals and experts during your internship.

This will then help you walk down your successful path as a Data Analysts under whatever focus/stream you may later choose to focus on. 

What is Data Wrangling and Why is it Important?

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Data has changed the digital landscape drastically in the past few decades. From analyzing and providing insights real-time to enhance one’s life, data is integral to everything we do. 

It is impossible today to live in a world where we do not encounter data. Whether it is watching recipes on YouTube to adding friends on social networking sites, data is everywhere. Due to the abundance of data, there is also an abundance of knowledge and insights which we never had before.

However, if the data is outdated or irrelevant, it serves no purpose.  This means that there is a real need today for data wrangling. Data wrangling is the art of providing the right information to business analysts to make the right decision on time. It aids organisations by sorting through data and access them for further processing and analytics.

Apart from this data wrangling also involves removing unnecessary data, organising them in a consumable fashion.
Data wrangling also provides organisations with the right information in a short span of time to access the right information thereby helping make strategic decisions for the business. It also helps business perform all these tasks at a reduced cost and more efficiently with minimal human intervention.

Here are the top reasons why data wrangling should be everyone’s priority

Credibility of data
When large amounts of data are processed for interpretation chances are all of it is not relevant or outdated. Although data wrangling is a tedious process, conducting it will ensure that the data secured is not outdated or irrelevant.  Therefore, data wrangling provides credibility to data analytics courses. It picks the right data required in order to provide the necessary solutions to a problem. 

Build trust amongst stakeholders
When valuable information is extracted and presented to the stakeholders involved it build trust. Data should not only be presented in a simple format, but it also must add value to the circumstances. This means that any data that is extracted must be able to benefit the organisation or individual one way or another. This can be achieved through data wrangling, making it an important activity to carry out in an organisation.

Aid Machine Learning
Machines of today have the ability to create, process and understand data to arrive at plausible solutions thereby aiding a company’s overall growth and success. In order to optimise the vast volumes of data obtained from various sources, data wrangling becomes an important task.

It is not possible for a machine to scale and learn from new information if the data itself is corrupt or unnecessary.  Data which is historic in nature which allows the machine to learn and adapt can only be procured through data wrangling. If the quality of data that is fed into an AI is useless, the results which it will produce will also be irrelevant.

Conclusion
Data wrangling is extremely relevant today due to the large amounts of data that gets proceeded every day.  We will not be able to do thorough analytics if we do not have a strong infrastructure of data storage and hence companies are investing heavily in data wrangling tools.

10 High Value Use Cases for Predictive Analytics in Healthcare

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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

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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. 

Big Data in Risk Management

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We all know that over 90% of the world’s data has been generated only in the last two years. Forward-looking organizations, especially e-commerce, have already begun capitalizing on this gold mine. But what does the Big Data revolution mean for financial services, particularly the risk management function?
Risk management faces new demands and challenges. In response to the spate of recent financial crises, regulators are insisting on ever more detailed data and increasingly sophisticated reporting. Banks are now required to conduct regular, comprehensive bottom-up stress tests for a variety of scenarios across all major asset classes.

Put simply, Big Data represents the future of risk management. Why? Big Data technologies can help Risk teams gain better intelligence, drawn from a variety of data sources, in almost real-time. Within the financial services industry, Big Data can enable asset managers, banks and insurance companies to proactively detect potential risks, react much faster and kimore effectively, and make better decisions on the back of insights from thousands of risk variables.
Time is a critical factor in reacting to risks, and if you can react faster to dynamic risk factors, you have a competitive advantage.
Worried about fraud on the trading floor? Rather than manually track staff trading actions, data lakes enable you to retrieve an instant snapshot of an activity, including data from chat room sites, mobile phones, and swipe in/ out records. Suspicious transactions can be identified and stopped as they are happening in real time, before incurring huge fines and damaging your firm’s reputation.
Big Data Analytics has already proved its mettle within e-commerce, and will surely be a game changer for risk professionals. And don’t worry – this new technology is merely one more tool in a risk manager’s arsenal. It does not, and should not, replace the human element — Identifying what’s a signal and what’s merely noise, what you react to and what you ignore is still a judgment you need to make.
Learn more about the applications of Big Data in Risk Management in our next executive development program, which will be conducted on 21st and 22nd September in Mumbai. Click here to learn more.