12 Big Data Issues Growing Companies Face

Big data offers immense opportunities for businesses, but it also brings several pain points that can hinder growth and decision-making. One major challenge is managing the sheer volume of data collected from various sources. As businesses scale, the data they handle grows exponentially, making it difficult to store, process, and analyze effectively. Without the right infrastructure, companies can become overwhelmed, leading to slower response times, missed insights, and reduced operational efficiency. This can prevent them from staying competitive in a fast-paced market, where real-time decisions are critical.

Another significant issue lies in the complexity of extracting actionable insights from vast datasets. Data quality often becomes a concern, as businesses must ensure that the information they analyze is accurate, relevant, and up-to-date. Inconsistent or unreliable data can lead to flawed analyses, causing poor decision-making and wasted resources. For example, if a retailer’s big data system fails to properly segment customer data, it may result in poorly targeted marketing campaigns, misallocation of resources, and a weakened customer experience. This lack of clarity in data interpretation can ultimately erode trust in the company’s strategic decisions.

In the sections below, we’ll dive deeper into the specific challenges businesses face when dealing with big data. From data security to integration issues, we’ll explore how these obstacles can impact performance and decision-making.

Big Data Solutions: How Are They Implemented in Practice?

The global big data market promises to grow by 2025 to a record $68 billion, while in 2019 this figure was almost five times less. This means that companies increasingly introduce products based on this concept. There is a generally accepted definition of big data that was once proposed by IBM. According to it, big data is described by four parameters (4V):

• Volume: This data is generated constantly
• Velocity: You need to process them quickly
• Variety: Many sources and data types are used
• Veracity: Data must be of good quality

Big data is a broad concept. That’s why it involves the use of advanced digital solutions. Among them are:

• Machine learning, including artificial neural networks, network analyses, and optimization techniques
• Crowdsourcing, because often there is not enough data for analytics that the enterprise itself can collect, and it requires external data
• Data mining based on association rule learning, data classification, and analysis
• Heterogeneous data integration
• Simulation modeling and analysis
• Analytical data visualization using pictures, graphs, charts, tables, etc.

Together or separately, these solutions can collect and process colossal flows of unstructured data. However, they are not able to independently solve several big data concerns, which we will discuss below.

Big Data Issues and Their Solutions: 12 Examples You May Face

Let’s look at twelve of the most common big data problems and solutions.

Lack of Understanding of How to Work with Big Data

The lack of understanding of how to work with big data opens our list of big data problems. When companies start migrating to digital products that use big data, their employees may not be ready to work with such advanced solutions. As a result, implementation with untrained personnel can cause significant slowdowns in work processes, disruptions in familiar workflows, and numerous errors. Until your employees realize the full benefits of innovation and learn how to use them, there may be a decrease in productivity and even data loss.

To overcome the challenges of big data, it’s very important to connect qualified data scientists and data engineers or train existing ones to the current workflows along with the creation and adoption of new advanced digital solutions. As practice shows, the alternative option, “in its pure form,” is not always effective, because your employees will need some time to be trained. Moreover, new digital solutions will bring additional workloads to your IT department. Therefore, it’s much better to either mix training with hiring new specialists or find a fully-staffed, dedicated team provided by software development companies that would take responsibility for supporting new software.

Poor Data Quality and Data Silos

Another typical problem of companies dealing with big data is data silos or poor data quality: it may be unstructured, have different formats, contain duplicate records, etc. Thus, data can’t be accessed centrally, which means that even a simple calculation of quarterly expenses can be accompanied by serious errors since the numbers from different departments of the enterprise are not synchronized with each other. Note that as the complexity of big data software grows, the number and probability of errors will gradually increase.

There are several ways to solve these data quality issues. The first way is to practice data consolidation. In this case, you form a repository of key data that acts as a so-called “single source of truth.” Next, you’ll need to create a data directory in which all records will be structured and sorted. In this case, you’ll be able to eliminate duplicates. Obviously, this transformation of colossal amounts of data must happen incrementally, so it’s imperative to determine what data is used most often and is most important to your business.

Big Data Issues in Scaling

Most companies gradually increase their data volumes. Over time, existing capacity becomes inadequate, and companies must take decisive steps to optimize performance and ensure the resiliency of an expanded system. In particular, the main challenge is to acquire new hardware—in most cases, cloud-based—to store and process new volumes of data. Unfortunately, such straightforward solutions are not always cost-effective.

Here’s what to do in such a situation: First, you’ll need to thoroughly analyze the existing software and hardware architecture of the solution and make sure it’s scalable. Conduct stress tests to analyze the performance of the functioning system to identify its weakest points.

Otherwise, it will be much more efficient to spend money on refactoring at least some software modules, which in the future will be subject to increasing workloads. Finally, to manage data challenges, you need to think about a plan to maintain the updated system—if your staff is not enough for this, you may have to choose an existing SaaS solution.

Variety of Big Data Technologies

You can use different data solutions to implement big data—from machine learning to predictive analytics and business intelligence. If you have never dealt with any of them before, it can be difficult for you to decide on the approach to implementing a big data system. 

In fact, the way out of these big data problems is simple: You need to find experienced experts who will analyze your needs and develop solutions specifically for your business. This way, you can understand which technology stack will be the most effective in your case.

Incorrect Integration

We’ve mentioned above how difficult it is for companies to provide centralized management. At the same time, integrating data incorrectly will cause negative consequences. For example, when different departments of an enterprise use different software and hardware solutions, data leakage or desynchronization may occur. In addition, not all solutions are suitable for end-to-end integration, so the structure of a big data system turns out to be unnecessarily complex and expensive to maintain.

The solution to these issues with big data lies in deep automation, the integration of individual subsystems through an API, and the rejection of manual control of the system. This modernization will entail significant costs, but in the long term, the likelihood of the above big data challenges will be minimized.

Heavy Expenses 

When companies implement complex big data systems, they need to be prepared for serious financial costs. These costs start from the development planning stage and end with maintenance and further modernization of systems, even if you implement free software. In addition, you will need to expand your existing staff, which will also result in extra costs. With such significant innovations, you will have to calculate your budget in the long term to prevent an uncontrolled increase in costs to support the viability of your big data system.

How can such control be ensured? It’s important to make the right decision on whose side the data storage and processing will take place. For example, if you need flexibility, cloud-based architecture is ideal. If it’s much more important to ensure the reliability and privacy of data, it’s better to purchase local server equipment and expand your staff with new specialists who will take responsibility for its configuration and support. A hybrid option is also possible. Thus, planning your business goals long term will help you stick to your budget as closely as possible.

Real-time Big Data Problems

As digital technology advances, companies’ business goals and the needs of their customers also change. From the point of view of big data issues and challenges in analytics, this suggests that they must be up to date, which means that some that were relevant yesterday may already be outdated. In addition, the COVID-19 pandemic, which significantly changed the habitual patterns of users, aggravates the problem of relevance. This means that you can no longer rely on historical data analytics for marketing and consumer analysis.

From a technical point of view, these challenges of data lie in the need for a tool that would provide up-to-date filtering of irrelevant data and shorten the processing cycle for new data so that innovations are introduced as quickly as possible.

In particular, you’ll have to think about a way to prioritize and segment big data so that it takes minimum time to process it, and that each iteration yields a significant result for the company. This is where the agile methodology comes in handy—which, by the way, applies not only to software development.

Also, you must provide automation wherever possible. Artificial intelligence may come into play, which will devastate big data analytics challenges and analyze new unstructured flows of information. Also, don’t forget to do an in-depth analysis of the data you already have to eliminate irrelevant ones.

Data Verification

Many companies mistakenly believe that their big data can be used effectively as it is. However, in practice, before using colossal amounts of unstructured data coming in different formats and from different sources, organizations need to implement data validation and cleansing processes that can identify and correct errors in data. Clearing data takes a long time, and only after that can it be used within software algorithms. For example, data processing by a specific algorithm can take only minutes, while its preliminary cleaning can take weeks.

Even though there are many advanced methods for organizing and cleaning data, your company needs to decide on the one that would bring maximum efficiency in your case. For example, your cleanup model might come from a single source of truth, or it might compare all duplicate data and combine them into one.

In addition, we’ll never tire of repeating how important automation is when working with big data. It can be implemented with the help of solutions based on machine learning and artificial intelligence. Remember, even though none of the existing approaches can completely tidy up all the data, you can choose the one that will bring you the most accurate results.

Lack of Expertise 

Although the concept of big data is not new at all, the demand for big data specialists exceeds the number of existing specialists. This can be explained, first of all, by the trends of everything related to big data. Thus, many companies try to migrate to such technologically advanced systems as quickly as possible to get ahead of their competitors and take a top position in their industry.

In practice, this niche still remains quite difficult and expensive to master, since it involves working with complex big data tools and technologies and requires a lot of computing power accordingly. This is why the job market in the big data niche won’t be overcrowded anytime soon. What should companies do in such situations, and how can they find highly qualified specialists?

In addition to looking for talent “on the side,” you will likely be puzzled by the issue of training your employees. After all, it will be much more cost-effective to transfer some specialists from your IT department to new positions and then fill the vacancies with new specialists than to hire people who are completely unfamiliar with the work processes taking place in your enterprise. 

Also, companies should consider the prospects for cooperation with universities—there they can find new employees with relevant knowledge who have not yet had time to get a job elsewhere. Another effective option is to renew your partnership with your dedicated team that previously provided digital services for your company. This saves you the time and resources of bringing new contractors’ services up to date. And of course, make sure that manuals on how to use big data solutions are always available to each of your employees.

Organizational Resistance

Resistance to organizational change or organizational inertia is the ability of enterprise personnel to resist innovations, which is expressed in actions aimed at maintaining the existing state of the enterprise or its separate system.

Organizational inertia can be individual and collective, which, in turn, can be divided into system resistance and resistance from specific groups. Therefore, it’s most convenient to consider the causes of resistance on the example of these three types of resistance, since each of them has its own specifics and characteristics.

So, the reasons for resistance to organizational change are:

• Fear factor—loss of job, loss of image and authority, decrease in salary, increase in the amount of responsibilities, fear of something new, etc.
• Information deficiency factor
• Employee’s limitations—intellectual limitations, conservatism, or professional limitations.

It’s necessary to solve this problem comprehensively and competently, by introducing new approaches to local management. To overcome big data management challenges, you’ll need to place big data staff in management roles in every department that uses that data.

Security and Privacy of Big Data

Security for big data projects is not just about making information accessible. The data that serves as a source for analysis, as a rule, contains information that is critical for business: trade secrets, personal data, etc. Violation of the confidentiality of working with such sensitive data can turn into serious problems, including fines from regulators, loss of customers, loss of market value, etc.

Unfortunately, today there are no clearly formulated methods describing the systematic steps and actions to protect big data. This requires approaches focused on protecting critical data at all stages of its processing, from data collection to analysis and storage. How can you know which principles to be guided by?

FFortunately, some organizations have standardized how big data is protected. These include the International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC), the International Telecommunication Union (ITU), the British Standards Institute (BSI), and the US National Institute of Standards and Technology (NIST). In particular, most companies are guided by the NIST Interoperability Framework specifications when implementing big data solutions—where you can find a list of recommendations in the “Security and Privacy” section.

Finding Experts Who Can Analyze Big Data

In addition to high-level specialists who will plan the implementation of your system based on big data, analyze its development prospects, and protect your business from all potential big data challenges, you’ll also need those who will directly interact both with this system and analytical data from day to day.

This is especially difficult for business niches that require specific knowledge—for example, in the field of medicine and healthcare. How can you solve these problems with big data in healthcare andother highly specialized niches? And where can you find specialists suitable for this position?

In case you still haven’t found employees with specialization in the niche you need, we recommend that you consider software solutions. In particular, there are dozens of machine learning-based products today that are ready to take charge of data analysis. In addition to ready-made solutions, you can always find developers who will create a turnkey custom product.

Big Data Challenges and Opportunities: Conclusion

Even though the concept of big data has been on the market for a long time, many companies do not pay enough attention to the typical challenges of big data that can be prevented in the early stages of implementing big data solutions.

As a result, sooner or later,these big data challenges lead to significant growth in the cost of implementing software and hardware solutions that support their viability, as well as the need for a constant increase in human resources involved in the working processes of the system, and other big data issues.

To avoid all these big data problems, we strongly recommend that you analyze your solution and identify the above big data challenges if any. Or you can shift the responsibility for planning, implementation, and further support of big data systems to us—skilled data professionals who have successfully implemented numerous big data solutions and now, will help you overcome all the possible big data challenges.

FAQ

01/

What Are the Challenges of Big Data?

The main big data challenges are the lack of understanding of how to work with it, poor quality and data silos, possible data scaling, a variety of technologies that need to be implemented, incorrect integration, heavy expenses, real-time processing, data verification, lack of expertise of specialists who work with this data, organizational resistance, security and privacy, and the hiring of big data analysts.

02/

What Are the Three Massive Big Data Problems Everyone Should Know About?

Three key challenges of big data are data privacy, data security, and data discrimination.

03/

What Are the Contemporary Issues of Big Data?

As today’s big data solutions continually scale, key challenges of big data include meeting data security and privacy standards, maintaining a resilient data infrastructure as it grows, and optimizing the processes that use that data (reducing their cost and associated risks, as well as their acceleration).