In computing, the term “big data” refers to a collection of data that is both large and increasing at an exponential rate over time. It is the process of analyzing vast data sets to discover patterns, trends, and connections. Big Data strategies that are appropriately implemented may decrease operating costs, shorten time to market, and open the door to new product development.
Despite the unparalleled insights and possibilities that Big Data has to offer, there are specific concerns that you should be aware of and address as well. While implementing Big Data initiatives, organizations must overcome several obstacles for the success of such initiatives.
This article explores the challenges of Big Data and the strategies to combat it.
What are the Challenges of Big Data
Here are three trends that define the current state of Big Data today and hence the challenges.
- Data is massive in volume and is growing by the day
- Data is constantly changing and, at a fast pace
- Data is stored in multiple formats
Big Data is often erroneous, and when it is, it can create significant problems for an organization that relies on it to make business decisions, forecasts and uses this data to bolster relationships with customers. Consistency, privacy, security, correctness, and completeness of data are the primary challenges you would often encounter when working with Big Data.
In the sections that follow, we’ll examine these challenges.
Lack of Knowledge and Acceptance of Big Data
Inadequate Big Data knowledge and expertise is one of the reasons organizations fail in their Big Data initiatives. If the employees are not aware of the importance of Big Data and how to deal with it efficiently, they cannot handle Big Data efficiently.
Without a deep understanding of the subject, an effort to adopt big data will be unsuccessful. Organizations may spend a significant amount of time and money on technologies and tools they do not even understand how to utilize. Moreover, if the employees are unaware of what Big Data is and how to deal with it, they will oppose the initiative rather than welcoming it.
Quality of the Data
Big Data systems are often composed of unprocessed, raw data. They usually require raw, unprocessed data stored in a data lake, from which various techniques such as data reduction, scrubbing, and processing methods are applied as needed. However, one of the significant drawbacks of this approach is that there is hardly any control over the data quality kept in the data lake. In addition, data can come from diverse sources and in a variety of formats. This would make data integration a challenge.
For data analytics to be successful, you must have pertinent, timely, accurate, and trusted data. Companies can get the most out of big data by providing reliable, high-quality information to their customers. When companies process large amounts of data, data quality is one of the most difficult problems they have to deal with.
Lack of Skilled Data Professionals
Organizations worldwide have been facing an acute shortage of skilled professionals as far as Big Data is concerned. To successfully implement modern technologies like Big Data and the related tools, you need qualified data professionals. These include data scientists, data analysts, and data engineers aware of Big Data, its intricacies, making sense of massive data sets, and help the organization make real-time business decisions.
One of the biggest challenges of big data is human error. Such errors may be caused by various factors, including spelling problems, duplicate entries, erroneous inputs, and even inconsistencies in format. As a result, human mistakes must be addressed to guarantee that the data is consistent, clean, and trustworthy. You would often need manual intervention in data processing since it is impossible to automate everything. Instead, companies should put measures to reduce the likelihood of human mistakes in large data sets.
In recent years, there have been some of the most significant data thefts ever perpetrated. As a result, data security is one of the most pressing issues when it comes to using Big Data. Although keeping data on the cloud is cost-effective, there are worries about the security of your information. Despite the fact that cloud storage providers have implemented security precautions, you cannot be certain that your data is safe.
Data security and privacy are some of the major concerns for businesses worldwide. To fight the worst-case situations, it is necessary to take precautionary steps to protect data. Investing in appropriate anti-malware software and implementing appropriate security measures can help businesses guarantee that the data stored and presented is secure.
Data Completeness, Accuracy, and Validity
One of the most severe problems is the lack of completeness and accuracy of the data, which begins from the moment the data is collected. The recorded value can be an approximation and not the actual value. Even data acquired from the Internet of Things (IoT) can be approximate.
Data is often lost in the transfer process. Nevertheless, you will need judgment and human involvement to decide whether the data is correct and relevant, even if they are accurate and complete. One example is the inability to rely on social sentiment data as a reliable indicator of customer happiness.
Solutions to Big Data Problems
Now that we know the challenges faced by the organizations in the accuracy and quality of Big Data, let’s talk about the solutions to the problems stated in the previous section.
Defining the ROI
Businesses must determine the business value of Big Data, which means identifying the areas where Big Data and data analytics may bring value and estimate the effect of inadequate information quality. Furthermore, organizations should predict the amount of money needed to ensure Big Data is utilized appropriately in the future.
Maintaining data clean and accurate is always a challenge that requires constant monitoring. Due to the increasing reliance on information-based decisions, it is imperative that the data is clean and reliable. You should cleanse the data at regular intervals of time. As a result, businesses would have the ability to examine the data and verify that it is of the highest quality.
Impart Big Data Training
Organizations need to invest in the recruitment of qualified data professionals. Organizations should educate their employees on the significance of proper data collection and train them on how the captured data is utilized. Training employees and ensuring that they understand how to handle information responsibly are essential steps for organizations to follow. Organizations should provide training on data security to all employees that use big data for business analysis and predictive modeling. Sensitive data should be accessible only to users who need it; in other words, users should be granted access to just the data that they may require.
Better Governance and Transparency of Big Data
A well-thought-out strategy to solid governance is required, and business users must be aware of Big Data’s appropriateness and the limits of data accuracy before using it. Businesses should be mindful of the big data source used for decision-making. Knowledge of the known limitations, i.e., the types of decision-making that can and cannot be supported, should be made available.
Employees should get the required training from their employers to know what Big Data is and how to use it effectively. The analysis of Big Data should be carried out by professional data scientists who are well-versed in both the possibilities and the limits presented by the data.
Final Thoughts on Big Data Challenges and Solutions
Big Data will play a crucial role in the digital transformation and the creation of new business models for years to come. Businesses should put in place reliable processes and procedures in order to ensure that the data they gather is accurate and trustworthy. There is no doubt that big data has a bright future and will undoubtedly be of immense value to society in the years to come. However, it is critical not to overlook the implementation difficulties that might have to be encountered, regulatory concerns, and adverse consequences.