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 Toolsare 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.
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.
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.
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 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:
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 forSecuring Big Datainclude: Data encryption Data segregation 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
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.
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.