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Major Big Data Challenges and Top Ways to Overcome Them

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Big Data includes the vast amount data, coming from various sources, and technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems. The challenges associated with Big Data are varied and impact almost all segments. One of the biggest Big Data challenges is getting real-time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, applications, and processing the data for use. This is all the more important because of the wide variety of data sources involved and security issues that come with it. In this post, we will find out what are the challenges in handling Big Data.

What are the Challenges in Handling Big Data

According to the 2017 National Security Agency report, the Internet processes close to 1,826 Petabytes of data per day. In 2011, digital information has grown nine times in volume in just five years, and by 2020, data volume across the world will reach nearly 35 trillion gigabytes. This explosion of digital data brings forth big opportunities and transformative potential for various sectors such as enterprises, healthcare, manufacturing, and educational services. It has also led to a dramatic paradigm shift in our scientific research towards data-driven discovery.

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However, it is important to note that despite the several challenges, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D. Healthcare is another area which is fast adopting Big Data for conserving medical data. It has its own set of challenges too. Here, we will probe into the Big Data in healthcare challenges and opportunities.

What are the Challenges Associated with Big Data?

Big Data in Healthcare: Challenges and Opportunities

Big Data has been of big help for the healthcare sector, in more ways than one, such as to collect, store, analyze, process and present the data to its stakeholders in a meaningful manner. Healthier patients, lower care costs, more visibility into performance, and higher staff and consumer satisfaction rates are some of the benefits of turning data assets into data insights.

However, the road to meaningful healthcare analytics is pitted with challenges. Big Data ensures a steady flow of information for healthcare providers. However, the data does not always come from a reliable source and requires to be processed for use.  Capturing clean, complete, accurate, and formatted data for use in multiple systems is a major Big Data challenge for Healthcare Providers.

Data Quality: In a recent study at an ophthalmology clinic, EHR data matched patient-reported data in just 23.5%of records.  When patients reported having three or more eye health symptoms, their EHR data did not respond, as expected. Poor EHR usability, convoluted workflows, and an incomplete understanding of Big Data processes contribute to quality issues in Big Data usage.

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Healthcare providers can start improving their data capture routines by prioritizing valuable data types for their specific projects, enlisting the data governance and integrity expertise of health information management professionals, and developing clinical documentation improvement programs that coach clinicians about how to ensure that data is useful for downstream analytics.

Security:  Security is another major Big Data challenge for Healthcare. With the recent series of high profile breaches, hackings, and ransomware episodes, healthcare data is subject to a nearly infinite array of vulnerabilities.

The HIPAA Security Rule includes a long list of technical safeguards for organizations storing protected health information (PHI), including transmission security, authentication protocols, and controls over access, integrity, and auditing. These safeguards include using security procedures such as using up-to-date anti-virus software, setting up firewalls, encrypting sensitive data, and using multi-factor authentication.

However, data centers are quite vulnerable to attacks, given that staff members tend to prioritize convenience over lengthy software updates and complicated constraints on their access to data or software. Healthcare organizations, therefore, must inform the staff members of the critical nature of data security protocols and consistently review who has access to high-value data assets to prevent malicious parties from causing damage.

Big Data Challenges: Deep Learning Application and Challenges in Big Data Analytics

Of late Deep learning has gained tremendous impetus in the active research area in machine learning and pattern recognition society. Unlike conventional learning methods, Deep Learning does not limit itself to shallow-structured learning architectures but relates to advanced machine learning techniques that use supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification.

Successful implementation of Deep Learning includes face recognition, computer vision, and natural language processing. With the huge data volumes available today, Big Data brings big opportunities and transformative potential for a number of sectors. However, it is facing challenges relating to data and information.

The significant challenges posed by Big Data, often characterized by the three V’s model: volume, variety, and velocity, which refers to a large scale of data, different types of data, and the speed of streaming data, respectively.

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Big Data, coming from various sources, often possesses a large number of examples (inputs), large varieties of class types (outputs), and very high dimensionality (attributes). These properties directly lead to running-time complexity and model complexity. The sheer volume of data makes it often difficult to train a deep learning algorithm with a central processor and storage. Having a distributed framework with parallelized machines is a better option.

Improved techniques have made it possible for us to mitigate the challenges related to high volumes. The novel models utilize clusters of CPUs or GPUs in increasing the training speed without having to worry about the accuracy of Deep Learning algorithms. Strategies for data parallelism or model parallelism or both have also been developed. Data and models, for instance, can be divided into blocks that fit with in-memory data; the forward and backward propagations can be implemented effectively in parallel, although deep learning algorithms are not trivially parallel.

Data today is procured from all types of formats from a variety of sources, probably with different distributions. For example, the rapidly growing multimedia data coming from the Web and mobile devices include images, video and audio streams, graphics and animations, and unstructured text. The key to high variety is data integration. Deep Learning is particularly known for representation learning. It can use either supervised or unsupervised methods to learn good feature representations for classification. It can discover intermediate or abstract representations with unsupervised learning.

Big Data poses several challenges for Deep Learning, including large-scale, heterogeneity, inconsistent labels, and non-stationary distribution. We need to address these technical challenges with dynamic thinking and transformative solutions to realize the full potential of Big Data for Deep Learning.

Other Big Data Implementation Challenges

Data Growth: Dealing with an ever-increasing data growth is one of the major Big Data challenges for data professionals. In a recent Digital Universe report, IDC estimates that the amount of information stored in the world IT systems is doubling about every two years, and much of this data is unstructured. Coming from disparate data sources like documents, photos, audio, videos, and other unstructured data is difficult to analyze. The voluminous data coming from undisclosed sources may be duplicated or may contain wrong data.

The best way to overcome the issue of data quality is to compare data to the single point of truth (for instance, compare variants of addresses to their spellings in the postal system database) and then match records and merge them if they relate to the same entity. Organizations are turning to technologies like compression, deduplication, and tier-wise break up to reduce the amount of space and the costs associated with Big Data storage.

Generating Data Insights: According to the NewVantage Partners survey, the challenges associated with Big Data include establishing a data-driven culture, creating new avenues for innovation and disruption, and upscaling with new capabilities and services. Enterprises need to gain insights from their Big Data and then act on those insights to overcome these challenges.

Enterprises are also using tools like NoSQL databases, Hadoop, Spark, Big Data analytics software, business intelligence applications, artificial intelligence and machine learning techniques to help them find useful insights.

Securing Data: Security is one of the biggest Big Data challenges organizations with Big Data stores face. The Big Data tools used for analysis and storage utilizes data from disparate sources. Most Big Data stores are soft targets for hackers or advanced persistent threats (APTs). It involves the potential risks associated with Big Data when it comes to the privacy and security of the data.

Unfortunately, most organizations live with the wrong perception that their existing data security methods are sufficient for their Big Data needs. As per the IDG survey, only 39% of the enterprises surveyed said that they were using additional security measure for their Big Data repositories or analyses. The pitifully low percentage of diligent organizations use additional measures such as identity and access control (59%), data encryption (52%), and data segregation (42%). This shows a lack of awareness on the part of business organizations for Big Data implementation challenges. To overcome these Big Data challenges companies are arranging for corporate training programs in Big Data.

Insufficient Upscaling: Adopting new processing and storing capacities is useless unless you upscale your systems to match the standards. Your solution design may be well thought and performing at its best. But it might not be well equipped to adapt to the new processes and storing capacities. It lies in the complexity of scaling up so that your system’s performance does not decline and you stay within budget.

Having a robust architecture of your Big Data solution is probably the best solution for this challenge. As long as your Big Data solution is capable enough to scale up with the newer processes and technologies, problems are less likely to occur. Designing your Big Data algorithms while keeping future upscaling in mind is another important step you can take.

In addition, you need to plan for system maintenance and support so that any changes related to data growth are properly attended to. Most important of all, holding systematic performance audits can help identify weak spots and timely address them.

Lack of Knowledgeable Big Data Resources: The exponential rise of data has led to an unprecedented demand for Big Data scientists and Big Data analysts. Enterprises must hire data science professionals with a strong knowledge of deep learning and Big Data applications. However, there is a sharp shortage of data scientists in comparison to the massive amount of data being produced. This makes hiring difficult and more expensive than usual.

To deal with the challenge of talent shortages, organizations may hire data science trainers to educate their existing resources to scale up with the competition and also make better use of the Big Data applications. Second, they can increase their budgets and invest wisely in their recruitment and retention efforts for hiring the best talents in the market. Third, enterprises may also buy analytics solutions with self-service and/or machine learning capabilities. These tools may help organizations achieve their Big Data goals even if they do not have a lot of Big Data experts on staff.

Big Data market is replete with career opportunities. Organizations are looking for skilled resources. You may start as a Data Analyst, go on to become a data scientist with some years of experience, and eventually turn out to be a Big Data evangelist. A strong knowledge of data management, machine learning, and natural language processing solutions and leadership skills are some of the essential requirements for a career in Big Data or Data Science.

Read my earlier post on key skills required for Data Scientist to prepare better for winning positions.

You might be a programmer, a mathematics graduate, or simply a bachelor of Computer Applications. Students with a master’s degree in Economics or Social Science can also be a data scientist. Take up a Data Science or Data Analytics course, to learn Data Science skills and prepare yourself for the Data Scientist job, you have been dreaming of.

You may also read my post on How to Create a Killer Data Analyst Resume for creating CVs that leave an impression in the mind of the recruiters.

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A self-starter technical communicator, capable of working in an entrepreneurial environment producing all kinds of technical content including system manuals, product release notes, product user guides, tutorials, software installation guides, technical proposals, and white papers. Plus, an avid blogger and Social Media Marketing Enthusiast.

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