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Introduction to Big Data Challenges
Big Data Challenges include the best way of handling the numerous amount of data that involves the process of storing, analyzing the huge set of information on various data stores. There are various major challenges that come into the way while dealing with it which need to be taken care of with Agility.
Top 6 Big Data Challenges
The below listed are the challenges of big data:
Lack of knowledge Professionals
Companies need skilled data professionals to run these modern technologies and large Data tools. These professionals will include data scientists, analysts, and engineers to work with the tools and make sense of giant data sets. One of its challenges that any Company face is a drag of lack of massive Data professionals. This is often because data handling tools have evolved rapidly, but in most cases, the professionals haven't. Actionable steps got to be taken to bridge this gap.
Companies are investing extra money in the recruitment of skilled professionals. They even have to supply training programs to the prevailing staff to urge the foremost out of them. Another important step taken by organizations is purchasing knowledge analytics solutions powered by artificial intelligence/machine learning. These Big Data Tools are often suggested by professionals who aren't data science experts but have the basic knowledge. This step helps companies to save tons of cash for recruitment.
Lack of proper understanding of Massive Data
Companies fail in their Big Data initiatives, all thanks to insufficient understanding. Employees might not know what data is, its storage, processing, importance, and sources. Data professionals may know what's happening, but others might not have a transparent picture. For example, if employees don't understand the importance of knowledge storage, they cannot keep a backup of sensitive data. They could not use databases properly for storage. As a result, when this important data is required, it can't be retrieved easily.
Its workshops and seminars must be held at companies for everybody. Military training programs must be arranged for all the workers handling data regularly and are a neighborhood of large Data projects. All levels of the organization must inculcate a basic understanding of knowledge concepts.
Data Growth Issues
One of the foremost pressing challenges of massive Data is storing these huge sets of knowledge properly. The quantity of knowledge being stored in data centers and databases of companies is increasing rapidly. As these data sets grow exponentially with time, it gets challenging to handle. Most of the info is unstructured and comes from documents, videos, audio, text files, and other sources. This suggests that you cannot find them in the database.
Data and analytics fuels digital business and plays a major role in the future survival of organizations worldwide.
Source: Gartner, Inc
Companies choose modern techniques to handle these large data sets, like compression, tiering, and deduplication. Compression is employed to reduce the number of bits within the data, thus reducing its overall size. Deduplication is the process of removing duplicate and unwanted data from a knowledge set. Data tiering allows companies to store data in several storage tiers. It ensures that the info resides within the most appropriate storage space. Data tiers are often public cloud, private cloud, and flash storage, counting on the info size and importance. Companies also are choosing its tools, like Hadoop, NoSQL, and other technologies.
Confusion while Big Data Tool selection
Companies often get confused while selecting the simplest tool for giant Data analysis and storage. Is HBase or Cassandra the simplest technology for data storage? Is Hadoop MapReduce ok, or will Spark be a far better data analytics and storage option? These questions bother companies, and sometimes they cannot seek the answers. They find themselves making poor decisions and selecting inappropriate technology. As a result, money, time, effort, and work hours are wasted.
You'll either hire experienced professionals who know far more about these tools. Differently is to travel for giant Data consulting. Here, consultants will recommend the simplest tools supporting your company’s scenario. Supporting their advice, you'll compute a technique and select the simplest tool.
Integrating Data from a Spread of Sources
Data in a corporation comes from various sources, like social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. Combining all this data to organize reports may be a challenging task. This is a neighborhood often neglected by firms. Data integration is crucial for analysis, reporting, and business intelligence, so it's perfect.
Companies need to solve their Data Integration problems by purchasing the proper tools. A number of the simplest data integration tools are mentioned below:
- Talend Data Integration
- Centerprise Data Integrator
- IBM InfoSphere
- Informatica PowerCenter
- Microsoft SQL QlikView
Securing these huge sets of knowledge is one of the daunting challenges of massive Data. Often companies are so busy understanding, storing, and analyzing their data sets that they push data security for later stages. This is often not a sensible move, as unprotected data repositories can become breeding grounds for malicious hackers. Companies can lose up to $3.7 million for stolen records or knowledge breaches.
Companies are recruiting more cybersecurity professionals to guard their data. Other steps to Securing it include Data encryption, Data segregation, Identity, and access control, Implementation of endpoint security, and Real-time security monitoring. Use its security tools, like IBM Guardian.
What are the challenges in the healthcare industry?
The challenges for its implementation in the healthcare industry are:
Challenges for Building a Healthcare Analytics Platform
- Enhance the efficiency of diagnoses.
- Prescribing Preventive medicine and health.
- Providing results to doctors in a digital form.
- Using predictive analysis to uncover patterns that couldn’t be previously revealed.
- Providing Real-Time Monitoring
- To develop data exchange and interoperability architecture to provide personalized care to the patient.
- To develop the AI-based Analytical platform for integrating multi-sourced data.
- To propose a Predictive and Prescriptive Modelling Platform for physicians to reduce the semantic gap for an accurate diagnosis.
Analyzing healthcare data will allow physicians to recognize the patterns that are still uncovered in the data. Click to explore about, Cloud Governance: Solutions for Building Healthcare Analytics Platform
What are the challenges in Security Management?
Below are some common challenges –
- Vulnerability to fake data generation
- Struggles with granular access control
- Often “points of entry and exit’ are secured, but data security inside your system is not secure.
- Data Provenance
- Securing and protecting data in real-time
Have securities issues and attacks happening every single minute, these attacks can be on different components of Big Data, like on stored data or the data source. Click to explore about, Big Data Security Management: Tools and its Best Practices
What are the challenges in Hadoop-Delta Lake Migration?
Migration from Hadoop takes place because of a variety of reasons. Following are the common reasons why migration’s necessity comes up:
- Poor Data Reliability and Scalability
- Cost of Time and Resource
- Blocked Projects
- Unsupportive Service
- Run Time Quality Issues
Migration from Hadoop takes place because of a variety of reasons. Click here to access list
What are the challenges in Cloud Security Governance?
Some of the challenges that Cloud Governance features help us in tackling are:-
- Performance Management
- Cost Management
- Security Issues
Learn how to implement it into managing and analyzing your business; check out our Big Data Solutions and Services to transform your business information into value, thereby obtaining competing advantages.