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Big Data Challenges, Big Data Solutions

With Big Data challenges, there are a number of important solutions to know in order for businesses to be able to use the data effectively.
With Big Data challenges, there are a number of important solutions to know in order for businesses to be able to use the data effectively.

With Big Data challenges, there are a number of important solutions to know in order for businesses to be able to use the data effectively.

Big Data challenges include knowing the best approach to handle large amounts of data. This also includes the process of storing and analyzing large amounts of data across multiple data repositories.

Challenge #1. Professionals with Insufficient Expertise

Companies require trained data specialists to run the latest big data technology and tools. Therefore, only trained professionals will be working with the technologies and making sense of massive data sets. These experts include data analysts, data scientists, and data engineers.

A lack of Big Data professionals is one of the Big Data challenges that any company faces. This is sometimes due to the fact that data processing tools have advanced rapidly. However, most experts have not. To close the gap, concrete efforts must be taken.

Solution #1. Big Data Training

Companies are devoting more resources to the recruitment of talented workers.

They must also provide training programs for current employees in order to get the most out of them. Acquiring analytics solutions backed by AI and ML is a smart move made by many businesses.

These Big Data Tools are frequently used by professionals who aren’t data scientists but have a rudimentary understanding of the subject. This stage allows businesses to save a significant amount of money on recruitment.

Big Data Architecture aids in the creation of a data pipeline that meets the needs of either a batch processing system or a stream processing system.

Challenge #2. Lack of Understanding of Big Data Challenges

Companies fail to succeed in their Big Data projects due to a lack of understanding.

Employees may not understand what big data is, how it is processed, stored, or even where it comes from. Others may not have a clear picture of what’s going on, even if data professionals do.

Therefore, employees who do not understand the need for knowledge storage, for example, may not be able to preserve a backup of sensitive material. It’s possible that they are not able to correctly save data in databases. As a result, when this critical information is needed, it is difficult to locate.

Solution #2. Awareness Workshops

Workshops and seminars on big data should be held at companies for everyone. Training sessions must be created for all employees who handle data on a regular basis. In addition, it must be available for those who operate in the vicinity of significant data projects. It is, therefore, important to instill a basic awareness of big data concepts at all levels of the business.

Challenge #3. Data Growth and Storage

The correct storage of these vast amounts of knowledge is one of the most important concerns of big data.

The amount of data being saved in data centers and company databases is continually expanding. Therefore, it becomes difficult to manage large data sets as they increase rapidly over time.

The majority of the data is unstructured and comes from a variety of sources. These sources include text files, documents, audio, movies, and other media. This means, however, that they can be difficult to find in the database.

Solution #3. Tiering, Compression, and Deduplication

Data and analytics are the lifeblood of digital business and are critical to firms’ long-term existence around the world.

Therefore, tiering, compression, and deduplication are some of the latest approaches in use dealing with big data volumes.

Challenge #4. Perplexity When Choosing a Big Data Tool

When it comes to selecting the simplest tool for huge projects, businesses sometimes don’t know which to choose. They often have these difficult problems.

In addition, they are sometimes unable to find answers. Therefore, they are prone to making poor selections and using ineffective technology. As a result, they squander resources such as time, money, work hours, and effort.

Solution #4. Hiring a Specialist

One solution to the perplexity challenge is to engage seasoned specialists who are significantly more knowledgeable about these instruments.

There are those who travel around and offer big data consulting.  Consultants will make recommendations for the most basic equipment that will assist your company’s circumstances.

Challenge #5. Bringing Data from a Variety of Sources Together

In a company, data comes from a variety of places. It might be from ERP software, social media pages, financial reports, customer logs, presentations, e-mails, or employee-created reports.

Therefore, combining all of this information to create reports can be a difficult undertaking. This is a place that many businesses overlook.

However, it’s a great place since data integration is critical for analysis, business intelligence, and reporting.

Solution #5. Getting the Right Tools

Companies must get the appropriate tools to handle their data integration issues. The following are some of the most basic data integration tools:

  • Centerprise Data Integrator
  • ArcESB
  • Talend Data Integration
  • IBM InfoSphere
  • Informatica PowerCenter
  • CloverDX Xplenty
  • Microsoft SQL QlikView

Challenge #6. Data Protection

One of the most difficult challenges of big data is security.

Companies are sometimes so preoccupied with preserving, comprehending, and analyzing their data sets that data security is forgotten. Unprotected data storage can be an open door for hackers. A stolen record or a hacking breach can cost a company millions of dollars.

Solution #6. Hire Cybersecurity Specialists

To protect their data, businesses are hiring more cybersecurity specialists. However, other measures taken to protect Big Data include:

  • encrypting data;
  • separation of data;
  • control of identity and access;
  • endpoint security implementation;
  • security monitoring in real-time; and
  • making use of Big Data security software.
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