Category: Big Data

Big Data Analytics is the method of extracting from Big Data sets and mining for an understanding, information, insight or knowledge mining. Big Data extracts helpful information from a computation pipeline that transfers stores and analyzes data for an entire application.

In the below section we highlight the challenges with respect to Big Data Analytics in Healthcare.

Data aggregation into large volumes:

The most frequently used technique of aggregating and transferring massive amounts of data is to copy / transfer data to a storage device, but its effectiveness decreases as volume rises. Big Data typically requires various organisations, geographic places and various computers to aggregate over a structure; thus, creating big data sets by replication from production should minimize the continuing usage of network facilities and database assets, allowing the production system to make the system running.

Furthermore, it is very difficult to manage the transfer of data between organizations and databases; therefore, generally a secondary database is developed, external to processing technologies. Another aggregation strategy is to move data across a network. However, large quantities of data need to be transferred, aggregated and indexed over a significant period. A third alternative is to replicate and iteratively produce data from sources across cases and various nodes, as Hadoop does when replicate and saving file blocks through distributed batch procedures.

Data is segmented and siloed:

While there are numerous challenges in implementing big data & analytics to its full extent. The biggest and the foremost challenge is data. First and foremost, the data used in healthcare is usually segmented and siloed.

For instance, finance data is only available with administrative team such as claims, reimbursement and cost information. This data set qualifies as the business side data of the healthcare which has nothing to do with patient care and treatment. And not used for patient care or treatment.

EHR (Electronic Health Record) consist of patient history, vital signs, progress note and the result of diagnostic tests. This all data can be summed up as clinical data which is accessed and maintained by healthcare practitioners, nurses and doctors and serves as a purpose for treatment.

Data on quality and outcomes such as surgical site infections, surgical return rates, patient drops, and value-based purchasing measures of Medicare and Medicaid Services Centres (CMS) are in the departments of quality or risk management. These data are collected and typically used to measure the provider’s performance retrospectively. According to study conducted in USA, 43% of healthcare data remains in silos.

For effective and efficient use of data analytics, there is a prodigious need to combine all such data. Experts are working hard towards this effort and are coming up with solutions such as data warehouses and decision support databases which is enacting as an enabler to combine such data sets.

Data maintenance:

Since Big Data, by definition, comprises of massive amounts of information, it is very tricky for continuing queries to be stored and maintained, particularly with continuous data batch handling. With smaller organization, time and cost is constraint in dealing with large amount of data. Another healthcare industry concern is that there is a need for constant updating of actual patient data, metadata and data profiles; otherwise the analytics will become ineffective.

Many solutions are available to address this concern viz. – NoSQL / NewSQL and other storage systems (MonogoDB, HBase, Voldemort DB, Cassandra, Hadoop Distribution File System, and Google’s BigTable. With data maintenance, legalities and ethics are significant problems. Data sets are usually governed by security, confidentially and privacy which holds those responsible for information retention accountable.

As per HIPA requirements, 18 critical information is to be removed from the patient information. By applying the appropriate software and database technologies, privacy concerns can be addressed, such as key value storage services. In an extremely secure building most hospitals house their data in server racks and providers are not generally permitted to use cloud facilities.

Unstructured Data Sets:

The quantity of unstructured data may be the most important task in aggregating and evaluating large health care information. Structured or discrete information involves information that in a relational database can be collected and recovered.

In the EHR of patients, unstructured health care information includes – test result outcomes, scanned records, pictures and progress reports. While standards such as the Clinical Documentation Architecture allow EHR data to be interoperable and shared, the contents of the defined fields are often free text and therefore unstructured data.

As free-text search technology matures and natural linguistic handling technology is incorporated into these, unstructured information is probable to be one of the most precious parts of the large information image of health care.

Patient’s Privacy:

A second major task in taking full advantage of the large information of healthcare is to protect the privacy of the patient. The exchange of health care data between organisations is often indicated as an objective and organisations such as national health information organisations have been specifically created to bring together health care data from stakeholders including suppliers, payers, and government health organisations.

There is certain regulatory requirement as well such as Health Insurance Portability and Accountability (HIPA) Act. After de-identification, patient information may be communicated, but it is difficult to protect the patient from either immediate or indirect identification while preserving data’s usefulness.

Covered organizations, including health service suppliers and health insurance firms among others, often mistake on the conservative hand and only publish aggregate information or information with removal of all prospective identifiers. Removing these information components and fulfilling the “secure harbour” de-identification requirements of the Health Insurance Portability and Accountability Act makes it almost difficult to use information for trend or longitudinal care research.

When surveys contain a time element such as those examining readmission prices or morality rates, removing date components is difficult. Even if the patient’s privacy can be guaranteed, due to industry rivalry, many health service suppliers are unwilling to disclose information. Many doctors don’t want their rivals to understand precisely how many processes they have carried out and where. The combination or demographics of patient insurance may provide an economic benefit over another clinic.

Although most clinics are run as non-profit organizations, they are still a company and follow all the laws governing the operation of a sustainable company. There are a range of data sets that are openly accessible that may enable rivals to gather comparable information, but these sources are typically historical or restricted to public payers.

The patients themselves are becoming more and more an information source. In formulating a solid information management scheme, the compilation of this information and the effect of its incorporation in the health care record are critical. This information can be gathered via surveillance devices linked to an offsite computer via mobile computing or downloaded from the computer periodically during an office visit.

The data must be validated in either scenario to ensure that the patient used the monitoring device and did not transfer it to another household person. With this patient information obtained, the danger of impaired information integrity is much greater than with sources under the clinician’s immediate command.

Conclusion:

A significant task to be recognized in information analytics for health care is that the assessment is often a secondary use of the information. For example, administrative information is mainly gathered for the billing of rendered services and deposit collection. The primary purpose of EHR information is to monitor patient advancement, therapy, and clinical status. When this information is then used to evaluate quality and results, the initial use of the information must be recognized as a prospective restriction and may compromise any subsequent models ‘ accuracy and legitimacy.

Comprehensive data and information management programs can be used within and across providers to tackle many of these challenges. A data management program involves guidelines on data format and the suitable use of data sources and data areas. Rigorous information management strategies guarantee coherent information material and format and support the technical elements of mapping and combining information from different sources. An information management program deals with data handling, evaluation and security.

Information management strategies will guide information consumers in determining whether a secondary use of the information is suitable, as well as the amount of information that can be published while preserving the identity of the patient. Data and information management operations must be cross-departmental for inner data sets to one organization and cross-organizational for data sets drawn from various organisations in order to be most efficient. This sort of framework will assist both inner and external information silos to be broken down.

Leveraging big data as an insight-generating engine has driven the demand for data scientists across all industry verticals. As the demand for data scientists advances, it extends an enticing career path for students as well as professionals. While a fruitful career choice, it requires a deep understanding of the business world and a set of critical traits in order to become successful data scientists in today’s competitive marketplace. Following are some of the skills that companies look out for in a data scientist:-

• Critical Thinking

Being a critical thinker is one of the most imperative skills as it allows data scientists to perform objective analysis on the facts of a particular subject matter or problems, before offering the right solution.

• Expertise in Mathematics

Data scientists engage with clients who are looking to develop operational and financial models for their companies, and this involves the analysis of a large amount of data. They leverage their expertise in math to formulate accurate statistical models that further serve as the basis for developing important strategies and facilitating approvals on decisions.

• Proficiency in Coding

Data Science Course Using R, Excel, Python enables data scientists to write code and efficiently deal with complex tasks associated with coding. To be a successful data scientist, one must have programming skills that include computational aspects (cloud computing, unstructured data, etc.) and statistical aspects (regression optimization, clustering, random forests, etc).

• Understanding of AI, Machine Learning, and Deep Learning

Owing to advanced connectivity, computing power, and collection of enormous data, companies are increasingly leveraging technologies like AI, machine learning, and deep learning. To be a successful data scientist, one must have extensive knowledge of these technologies and possess the ability to identify which technology to apply in order to avail the most effective results.

Comprehending Data Architecture

From interpretation to the decision-making process, it is important that data scientists understand how the data is being used. Not understanding the data architecture can seriously impact the interpretations that might result in businesses making inaccurate decisions.

• Good Business Intuitions

Data scientists must look at the business world from various perspectives to understand what needs to be done, and consecutively build strategies in achieving the end result. Therefore, good business intuition and a problem-solving approach are the two common skills that every company looks out for when hiring a data scientist.

• Ability to Analyze Risk 

A skilled data scientist should be able to understand the concepts of business risk analysis, how systems engineering works and needs to make improvements in the existing process. Risk analysis in the initial stages of the model development allows businesses to mitigate any unforeseen risks and make profitable decisions with care.

Data science is a multi-disciplinary domain that requires professionals to hold a strong knowledge base (through data science course) and domain-specific expertise. According to a recent study by IBM, the demand for data scientists will increase by 20% by 2020. Above are some of the imperative skills that data scientists must possess in order to carve a successful career path in the corporate domain.

Today’s world runs on data. Almost every rideshare application, food order app, retail or shopping site, and even all e-commerce sites require consumer data to provide an optimally satisfying customer experience. As every aspect of the web and applications are becoming experience-driven, every corporation and company are thinking about monetizing their data. Unfortunately, with the rise of mobile computing and multi-device access, gargantuan volumes of data keep flowing in from all directions. The traditional database architecture is no longer sufficient to hold enormous amounts of data or organize it appropriately.

Why is dealing with Big Data a significant challenge?

Big Data usually flow into a heterogeneous environment that data scientists typically refer to as a data lake. They are different from data warehouses. The traditional warehouses of data have a comparatively uniform architecture that is either wholly definite or rigid. Some companies define their data lakes as modern data warehouses, primarily since they use Hadoop. Hadoop makes data collection, storage, and management quite straightforward even for the small businesses that are new to the world of Big Data.

Here are the currently available technologies that deal with Big Data technologies –

• Traditional RDBMS including SQL databases
• NoSQL database systems
• Hadoop and other massively parallel computing technology

What are SQL databases?

RDBMS or relational database management system has been the standard response to all data storage and collection challenges people have faced in the near past. However, SQL databases are usually appropriate for a definite volume of data that has defined structure. Relational databases have been losing popularity in recent times as the age of Big Data dawns upon us. Big Data has massive volume, and it flows in at a tremendous velocity. It is highly variable that a traditional RDBMS database cannot tackle. It is not the primary scalable solution that meets every need for Big Data.

What are NoSQL databases?

NoSQL databases are taking over the data management landscape thanks to the rise of Big Data. Nonetheless, the much popular and time-tested structures are not enough to either store or analyze the ever-evolving nature of Big Data. Database admins now require something dynamic yet robust to tackle the management and analytical problems the new generation of data throws their way.

Unlike traditional SQL technology, NoSQL is flexible, and it is highly scalable. Most NoSQL database leaves room for the DBA to define and redefine data types and database structures. NoSQL allows the database admin to trade off rigid structures for agility and speed. It is the ideal requirement for Big Data management where the primary necessity is speed and not accuracy. Some of the most significant data warehouses including Google and Amazon now leverage the power of NoSQL to manage their unmeasurable bulk of data. Due to its incredible scalability, the users can continue to add more hardware as the data continues to explode.

What is Hadoop?

On the other hand, the state-of-the-art technological solutions that are capable of handling Big Data include the likes of Hadoop. It is not a database. It is a software ecosystem or framework of multiple software programs that support parallel computing. It does enable certain NoSQL database types to store and collect Big Data, like the HBase. It allows the expansion of data across multiple servers with little to no redundancy.

What is the role of MapReduce in the Hadoop framework?

MapReduce is a stable computational model of the Hadoop ecosystem. It plays a critical role in the determination of the intensive data processes from the ecosystem and spreads the computation throughout thousands (potentially endless) of servers. DBAs refer to this as a Hadoop cluster. Hadoop has standardized models that make data management a breeze for new companies and long-time running corporations. It comes with inherent fault tolerance. The data processing enjoys protection against hardware failure. Therefore, in case of a node malfunction, the job automatically goes to another node to ensure that the distribution computing remains continuous. In short, no matter how massive your data-load is, Hadoop has the solution.

Most companies that use Hadoop enjoy high flexibility of data types and scalable storage options at a low cost. Thanks to remote database management services the maintenance and updating of Hadoop enabled NoSQL databases has become a lot easier than it used to be. Users no longer require the presence of on-site DBAs for the optimization of database performance. Off-site database administration services can take care of updating, managing, caching and maintaining complete databases from remote locations. To know more about remote database management.

What are the most prominent uses of Hadoop right now?

Data analytics and predictive analytics — Most corporations and SMBs use Hadoop for analytics purposes. When there is a massive volume of data that require analysis, Hadoop is the primary choice for data scientists. It has the ability to store and process multiple data types simultaneously. That makes Hadoop the perfect fit for Big Data analytics and predictive analytics. Big Data environments are highly heterogeneous, and that consists of various information in structured, semi-structured and unstructured forms. Whether it is social media posts, social networking activities, clickstream records or customer emails, Hadoop has the agility and potential to store and sort it all.

Customer analytics — As a result, most companies use Hadoop for customer analytics purposes exclusively. One of its top functions is to predict customer behavior including conversion rates and track consumer emotions. Analysis like these utilizes information from social media activities of individual users and responses to corporate or promotional emails. E-commerce companies, healthcare organizations, and insurers often use Hadoop for analyzing promotional offers, treatment opportunities, and policy pricing respectively.

Predictive maintenance — Several manufacturers are now leveraging Hadoop in the maintenance of operations to determine equipment failure as they are about to happen. They are running real-time analytics applications including Apache Spark and Apache Flink along with Hadoop for improving their accuracy during prediction. The emergence of Hadoop as a robust and reliable prediction analytics tool has enabled the detection of online fraud, and cybercrime. It has also improved aspects of website and user interface (UI) design by gauging signs of customer satisfaction.

Hadoop has made its mark in the data management realm by attracting prominent IT vendors including Hortonworks, MapR, Cloudera and AWS. The Hadoop framework is attracting users and vendors from all across the globe. Its popularity is soaring along with the increasing importance of Big Data.

This article is contributed by Jack Dsouja, noted data analyst at RemoteDBA.com

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