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 Big Data which need to be taken care of with Agility.
Top 6 Big Data Challenges
1. Lack of knowledge Professionals
To run these modern technologies and large Data tools, companies need skilled data professionals. These professionals will include data scientists, data analysts, and data engineers to work with the tools and make sense of giant data sets.
One of the Big Data 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 traveled by professionals who aren’t data science experts but have the basic knowledge. This step helps companies to save lots of tons of cash for recruitment.
2. 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 could not keep the 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.
Big Data 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.
|Learn deeply about: What is Big Data|
3. 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.
Today, Data and Analytics leaders have to deal with delivering business outcomes from their data-driven programs and such leaders need to take ownership and develop a data and analytics strategy to meet these challenges.
Source: Gartner, Inc
Companies choose modern techniques to handle these large data sets, like compression, tiering, and deduplication. Compression is employed for reducing 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 is residing within the most appropriate space for storing. Data tiers are often public cloud, private cloud, and flash storage, counting on the info size and importance.
Companies also are choosing Big Data tools, like Hadoop, NoSQL, and other technologies.
4. 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 option for data analytics and storage?
These questions bother companies, and sometimes they’re unable to seek out the answers. They find themselves making poor decisions and selecting inappropriate technology. As a result, money, time, efforts, 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 provide a recommendation of the simplest tools supporting your company’s scenario. Supporting their advice, you’ll compute a technique then select the simplest tool for you.
5. 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
6. Securing Data
Securing these huge sets of knowledge is one of the daunting challenges of massive Data. Often companies are so busy in 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 a stolen record or a knowledge breach.
Companies are recruiting more cybersecurity professionals to guard their data. Other steps taken for Securing Big Data include:
Identity and access control
Implementation of endpoint security
Real-time security monitoring
Use Big Data security tools, like IBM Guardian.
Big Data Risks in Other Sectors
- Healthcare Challenges
- Security Management Challenges
- Hadoop-Delta Lake Migration Challenges
- Cloud Security Governance Challenges
Challenges for Building 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 uncovers 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.
Explore to know more about it: Healthcare Challenges[Download Use Case]
Security Management Challenges
Below are some common challenges –
- Vulnerability to fake data generation
- Struggles of 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
Explore to know more about it: Security Management Challenges
Hadoop-Data Lake Migration Challenges
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
Explore to know more about it: Hadoop-Delta Lake Migration Challenges
Cloud Security Governance Challenges
Some of the challenges that Cloud Governance features help us in tackling are:-
- Performance Management
- Cost Management
- Security Issues
Explore to know more about it: Cloud Governance Challenges
Learn how to implement Big Data into managing and analyzing your business statistics; check out our Big Data Solutions and Services to transform your business information into value, thereby obtaining competing advantages.
Implement this technique of handling the high volume of information in your business to achieve incredible results.
To know more, explore 8 Latest Trends in Big Data Analytics That You Should Know in 2020