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Businesses are growing leaps and bounds since last few decades resulting in many heterogeneous systems getting added in the IT infrastructure and generating enormous amount of structured & unstructured data from various internal as well as external entities. This enormous data, known as Big Data, has imposed big challenges and there is a pressing need to develop applications to consolidate all the data on a single platform and analyze it to get the insights that can truly drive the businesses towards success.

According to a report by Forbes, 38.2% of all the big data and analytics applications in use today are for customer-facing departments, while 42.6% of such applications are for internal departments. It is evident that with the help of modern EDW (Enterprise Data Warehouse) combined with advanced analytics, the combination of conventional and unstructured data sources are contributing to the development of new business insights.

But, in spite of all these facts and figures, there is some resistance in adopting Big Data Solutions. The CXOs seems to be facing some challenges in moving on to this much needed new age framework. What are those challenges? Let’s check out.

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Data privacy, governance and compliance

When we talk about big data privacy efforts, the points to be considered are the source of data and the way it is used. As we all understand, discussion always goes in the direction of how to use or exploit data. However, there is a tendency to avoid the sensitive subject of how to maintain privacy of an individual and how to protect data in an increasingly digital world. The complicating factor is how to keep a balance between:

• The value received by end users
• Maintaining the level of privacy and data protection

This issue needs to be addressed if we want our digital business practices to be seen as credible to our customers as providers of big data content.

Integrating legacy systems with big data technology

Legacy systems often hold valuable data too important to be lost in the process of an upgrade or transition to a new platform. With vendor-supported lifetimes often exceeded, and newer technology or more efficient methods backing, many organizations have legacy systems that need conversion, but integrating the new with the old can be difficult. Many customers fear about losing their valuable data and thus they are not ready to accept the integration.

Lack of big data skills

Skilled Big Data analytics workers are becoming hard to find as more and more companies are vying for these jobs. There are few relevant information findings like – Education is a big indicator for big data workers, majority of big data workers are relatively new, the big data workforce is pretty seasoned, big data workers labor in bigger organizations and data analysis is the top specialization. Majority of the solutions are revolving around Hadoop and more and more tools are getting launched in its ecosystem. It becomes difficult for big data developers to be on top of all the tools and trends.

Big data projects are being held back by the high cost of setting up infrastructure to support the capturing of potentially hundreds of millions of data points each day. A lack of a business case and the need to integrate data sources are also holding back adoption, according to the Australian Big Data and Analytics study, published by Telsyte. The research firm spoke to 324 CIOs and senior IT executives in 30 industries for the study, which also found that 25 percent of organization’s with more than 20 staff are using big data applications and services. Telsyte analyst Rodney Gedda said “it can be costly to set up the required infrastructure for projects aimed at deriving business intelligence and analytics from high volume data sets. The perceived high cost of big data tools, the market is primed for low up-front cost options such as those offered by cloud providers and pay-per use options.”

There are major challenges/issues on the radar of CXO team of decision makers but then are they going to live with it forever? Isn’t there any satisfactory solution? Stay tuned and you will know more in the next writing.

No organization can function without data these days. With huge amounts of data being generated every second from business transactions, sales figures, customer logs, and stakeholders, data is the fuel that drives companies. All this data gets piled up in a huge data set that is referred to as Big Data. It comes with its own Big Data challenges.

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This data needs to be analyzed to enhance decision making. But, there are some challenges of Big Data encountered by companies. These include data quality, storage, lack of data science professionals, validating data, and accumulating data from different sources.

We will take a closer look at these challenges and the ways to overcome them.

Read: Check out the scope of a career in big data.

Challenges of Big Data

Many companies get stuck at the initial stage of their Big Data projects. This is because they are neither aware of the challenges of Big Data nor are equipped to tackle those challenges. The challenges of conventional systems in Big Data need to be addressed. Below are some of the major Big Data challenges and their solutions.

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Let us understand them one by one –

1. Lack of proper understanding of Big Data

Companies fail in their Big Data initiatives due to insufficient understanding. Employees may not know what data is, its storage, processing, importance, and sources. Data professionals may know what is going on, but others may not have a clear picture.

For example, if employees do not understand the importance of data storage, they might not keep the backup of sensitive data. They might not use databases properly for storage. As a result, when this important data is required, it cannot be retrieved easily.

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Solution

Big Data workshops and seminars must be held at companies for everyone. Basic training programs must be arranged for all the employees who are handling data regularly and are a part of the Big Data projects. A basic understanding of data concepts must be inculcated by all levels of the organization.

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2. Data growth issues

One of the most pressing challenges of Big Data is storing all these huge sets of data properly. The amount of data being stored in data centers and databases of companies is increasing rapidly. As these data sets grow exponentially with time, it gets extremely difficult to handle.

Most of the data is unstructured and comes from documents, videos, audios, text files and other sources. This means that you cannot find them in databases. This can pose huge Big Data analytics challenges and must be resolved as soon as possible, or it can delay the growth of the company.

Solution

In order to handle these large data sets, companies are opting for modern techniques, such as compression, tiering, and deduplication. Compression is used for reducing the number of bits in the data, thus reducing its overall size. Deduplication is the process of removing duplicate and unwanted data from a data set.

Data tiering allows companies to store data in different storage tiers. It ensures that the data is residing in the most appropriate storage space. Data tiers can be public cloud, private cloud, and flash storage, depending on the data size and importance.

Companies are also opting for Big Data tools, such as Hadoop, NoSQL and other technologies.

This leads us to the third Big Data problem.

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3. Confusion while Big Data tool selection

Companies often get confused while selecting the best tool for Big Data analysis and storage. Is HBase or Cassandra the best technology for data storage? Is Hadoop MapReduce good enough or will Spark be a better option for data analytics and storage?

These questions bother companies and sometimes they are unable to find the answers. They end up making poor decisions and selecting inappropriate technology. As a result, money, time, efforts and work hours are wasted.

Learn: Mapreduce in big data

Solution

The best way to go about it is to seek professional help. You can either hire experienced professionals who know much more about these tools. Another way is to go for Big Data consulting. Here, consultants will give a recommendation of the best tools, based on your company’s scenario. Based on their advice, you can work out a strategy and then select the best tool for you.

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4. Lack of data professionals

To run these modern technologies and Big Data tools, companies need skilled data professionals. These professionals will include data scientists, data analysts and data engineers who are experienced in working with the tools and making sense out of huge data sets.

Companies face a problem of lack of Big Data professionals. This is because data handling tools have evolved rapidly, but in most cases, the professionals have not. Actionable steps need to be taken in order to bridge this gap.

Solution

Companies are investing more money in the recruitment of skilled professionals. They also have to offer training programs to the existing staff to get the most out of them.

Another important step taken by organizations is the purchase of data analytics solutions that are powered by artificial intelligence/machine learning. These tools can be run by professionals who are not data science experts but have basic knowledge. This step helps companies to save a lot of money for recruitment.

5. Securing data

Securing these huge sets of data is one of the daunting challenges of Big Data. Often companies are so busy in understanding, storing and analyzing their data sets that they push data security for later stages. But, this is not a smart 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 data breach.

Solution

Companies are recruiting more cybersecurity professionals to protect their data. Other steps taken for securing data include:

• Data encryption
• Data segregation
• Identity and access control
• Implementation of endpoint security
• Real-time security monitoring
• Use Big Data security tools, such as IBM Guardian

Read: Big data jobs and its career opportunities.

6. Integrating data from a variety of sources

Data in an organization comes from a variety of sources, such as social media pages, ERP applications, customer logs, financial reports, e-mails, presentations and reports created by employees. Combining all this data to prepare reports is a challenging task.

This is an area often neglected by firms. But, data integration is crucial for analysis, reporting and business intelligence, so it has to be perfect.

Solution

Companies have to solve their data integration problems by purchasing the right tools. Some of the best data integration tools are mentioned below:

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

In order to put Big Data to the best use, companies have to start doing things differently. Addressing these Big Data challenges as soon as possible is crucial. This means hiring better staff, changing the management, reviewing existing business policies and the technologies being used. To enhance decision making, they can hire a Chief Data Officer – a step that is taken by many of the fortune 500 companies.

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Big Data Analytics Challenges in Different Industries

Big Data challenges are there in every industry and are very common. Here are some of the challenges of conventional systems in big data and their solutions.

Big Data Challenge in Healthcare

• Boost effectiveness of diagnosis.
• Predictive Analysis can be used to find trends that were previously classified.
• Delivering digitised findings to medical professionals.
• Providing healthcare and preventative medicine.
• Real-time monitoring can become prominent.
• To suggest a Prospective and Prescriptive Modeling System for doctors in order to close the complexity for a precise diagnosis.
• To create a data transfer and interchange framework to give the patient individualised treatment.
• To create an appropriate technology powered by AI for combining data from several sources.

Solution

• Prescriptive and Predictive Analysis

Utilising the information gleaned from the patient’s records, the transmission of data and accessibility were developed to offer the patient individualised treatment. AI can store all medical records in the same place. It can also increase the rate of accurate diagnosis.

• Text Analysis

The General Health Records (GHR) database, compiled by gathering medical reports, is utilised to develop the algorithm. These reports are then digitalised so that the analysis can be considered.

• Genomic Data Analysis

Genomic data analysis thoroughly explains the connections among various genetic tags, alterations, and states. It has the potential to significantly aid in developing many genetic medicines to treat diseases.

Big Data Challenge in Security Management

• Sensitivity to generating fake data.
• While “points of access and exit” are frequently guarded, your system’s internal security may not be.
• Granular Access control challenges.
• Protecting and securing data.

Solution –

• Centralised Management

Centralised key management is more efficient than distributed or application-specific key management. Security keys and audit logs can be accessed from a single point in centralised management systems. Companies handling sensitive data need reliable key management systems.

• User Access Control

Basic network security tools include user access control. Big data systems can suffer a great deal from improper access control measures. Role-based settings and policies are the foundation of a robust user control policy. With policy-driven access control, complex levels of user control, such as multiple administrator settings, are automatically managed to prevent insider threats.

• Encryption

Several big data encryption tools can help in handling large volumes of data. This is the reason why companies encrypt their data, both machine-generated and manual.

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Conclusion

But, improvement and progress will only begin by understanding the challenges of Big Data mentioned in the article.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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