Like any other complex technological concept, big data can provoke some problems for enterprises that implement solutions based on it. What exactly these big data challenges and opportunities are, and how to solve them, we will find out below.
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):
Big data is a broad concept. That’s why it involves the use of advanced digital solutions. Among them:
Together or separately, these solutions are able to collect and process colossal flows of unstructured data. However, they are not able to independently solve a number of problems with big data we’ve listed below.
Next, we’ll look at twelve of the most common big data problems and solutions.
The lack of understanding of how to work with big data opens our list of data challenges. 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.
To overcome these data challenges it’s very important to connect qualified specialists 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.
Another typical problem of companies dealing with big data is data silos or poor data quality: it may be unstructured, have different formats, contains 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 challenges. The first way is to practice data consolidation. In this case, you form a repository of key data that acts as a “single source of truth.” Next, you will need to create a data directory in which all records will be structured and sorted. In this case, you will be able to eliminate duplicates. Obviously, this transformation of colossal amounts of data must happen incrementally, so it is imperative to determine what data is used most often and is most important to your business.
Most companies are gradually increasing 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 will need to thoroughly analyze the existing software and hardware architecture of the solution and make sure it is 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 this challenge of big data 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.
You can use different data science solutions to implement big data—from machine learning to data simulation 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 data challenges 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.
We have already mentioned above how difficult it’s for companies to provide centralized management. At the same time, incorrect integration also has 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 an 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 data challenges 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 problems will be minimized.
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 is important to make the right decision on whose side the data processing and storage will take place. For example, if you need flexibility, cloud-based architecture is ideal. If it is much more important to ensure the reliability and privacy of data, it is 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.
As digital technology advances, companies’ business goals and the needs of their customers also change. From the point of view of challenges in big data analytics, this suggests that they must be up to date, which means that some of them, which were relevant yesterday, may already be outdated. In addition, the COVID-19 pandemic, which has 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 data challenges 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 will have to think about a way to prioritize and segment big data so that it takes a minimum of 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, is applicable not only to software development.
Also, you must provide automation wherever possible. Artificial intelligence may come into play, which will be able to take responsibility for processing and analyzing 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.
Many companies mistakenly believe that their big data can be used effectively as it is. However, in practice, before using the colossal amounts of unstructured data coming in different formats and from different sources, it needs to be checked, formatted, and, if necessary, cleaned up. 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.
Here’s what the solution to these data challenges looks like: Despite the fact that in our time there are many advanced methods for organizing and cleaning data, it is important for your company 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 records and combine them into one.
In addition, we will 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.
Despite the fact that the concept of big data is not new at all, the demand for employees who specialize in it still exceeds the pool 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 in order to get ahead of their competitors and take a top position in their industry.
In practice, the big data niche still remains quite difficult to master, since it involves working with complex tools and technologies. 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.
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:
It is necessary to solve this problem in a comprehensive manner, competently introducing new approaches to local management. Specifically, you will need to place big data staff in management roles in every department that uses that data.
Security for big data projects is not just about making information accessible. The data that serve 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 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 collection to analysis and storage. How can you know which principles to be guided by?
Fortunately, there are organizations that 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.
In addition to high-level specialists who will plan the implementation of your system based on big data and analyze its development prospects, you will also need those who will directly interact 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 and in other 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.
Despite the fact that 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 that can be prevented in the early stages of implementing big data solutions. As a result, sooner or later this leads 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 problems if any. Or you can shift the responsibility for planning, implementation, and further support of big data systems to us—a company that has successfully implemented numerous big data solutions.
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