Most common challenges with Big Data

Insight from big data can identify and solve problems within an organization, provide insight into the customer lifecycle, and inform ways to increase productivity, among other benefits. However, it also comes with its share of challenges. The amount of data generated every day keeps on growing. In fact, data production was 44 times greater in 2021 than it was in 2011, suggested a study that examined the scenarios of the both the years. As a result, businesses have more data than ever at their disposal to inform their business decisions. But, this vast amount of data brings almost as many challenges as it does solutions. Data is no longer what it used to be. Different companies are finding newer uses for their data, thanks in part to digital transformation. This data is becoming increasingly important for competitive advantage and varies considerably from the data that we have grown familiar with. While old data was largely transactional, captured predominantly from internal sources, the new data is a combination of unstructured and transactional, privately collected and publicly available. The value of this new data lies in how it can be aggregated and analyzed. This new data may be divided into two distinct groups — Big Data and fast data.

In order to be used efficiently, data needs to be monitored, tracked, managed, cleaned, secured, and enriched throughout its journey inside your organization to yield the most effective results. In this article, we shall cover some of the main big data challenges, and solutions for how your business can overcome them.

Most Common Big Data Challenges

Data Integration

Normally, an organization will connect data from numerous sources, which makes it hard to monitor the effectiveness of the integration process. A lot of the problems pertaining to inaccurate insights may be tracked back all the way to how the data gets collected, verified, stored, and utilized. The problem is, when working in data-sensitive and intensive industries, even the smallest error may prove fatal to the success of the overall process. Furthermore, there may be some data quality issues that need to be addressed before the data can be integrated. Businesses need to have a data integration strategy in place if they strive to handle these kinds of big data challenges properly. They should also use the right tools and technologies, such as data virtualization and ETL, to facilitate the data integration process.

Lack of Skilled Professionals

Although the concept of big data is getting hyper and more prevalent, it is still a niche that remains uneasy or even challenging for businesses to step in and master since it involves a lot of complex tools as well as technologies and requires qualified specialists who have solid knowledge and experience in it. Unfortunately, the current talent pool of data professionals is insufficient, leaving a big gap between the rising demand and the available workforce. Or in other words, the shortage of data professionals is the most intense obstacle businesses, especially young ones, face when they first venture into the big data world. In addition, it is not only the data scientists or data analysts that businesses need to have on their teams but also other roles like data engineers, big data architects, business analysts, and so on. Consequently, acquiring the proper workforce to steer the big data initiative can be more challenging yet more costly than expected. Additionally, the lack of experts may lead to some pitfalls when implementing big data, such as difficulties in managing data assets, data quality issues, wrong data interpretation, and lack of data governance, which all can jeopardize the success of big data projects. Confronting such a challenge, you have one optimal solution that can resolve issues related to talent shortage and also cost at the same time. It is hiring a dedicated team of big data experts from offshore or nearshore outsourcing companies. In addition, similar to how to get over the shortage of software engineers, businesses should invest in data science education to prepare for the next generation of data specialists. Always remember, no matter how brilliant your data governance strategy is, it means nothing and results in nothing if you do not have good personnel by your side. Therefore, if you overcome this challenge of expertise and workforce shortage at the beginning of the big data adoption process, you will lay a good foundation for the success of your big data initiative. The technology and tools around big data are advancing rapidly, but there aren't necessarily enough people who can operate this technology at an expert level. It's much harder to collect, manage, and build actionable reports from big data if your team simply doesn't have the know-how.

Finding the Right Data

One of the major challenges of big data analytics that a lot of businesses encounter is that big data is to extensive and vast. There seems to be data for everything — customers' interests, website visitors, conversion rates, churn rates, financial data, and so much more. While a lot of that data is extremely useful, there are huge chunks that aren't exactly relevant for your business. And, with the sheer amount of information available, it can be hard to decide what is valuable for your business and what isn't. This problem typically presents itself if data is coming into your business unfiltered and unstructured through various different channels.

Security and Privacy

Along with the great advantages of big data solutions, there come the threats and risks for big data security and privacy. According to the 2022 KPMG survey, 62% of companies in the U.S. have experienced data breaches or cyber incidents within 2021, resulting in economic losses. Obviously, businesses have to handle a larger amount of sensitive data than ever before, and the data floods from various sources, making it daunting to manage and organize. Hence, the demand for protecting it from being mishandled or stolen also increases accordingly. In addition, the data grows at a high pace as business scales up, forcing the decision-makers to implement more tools and technologies in their big data systems for better data management and exploitation. Moreover, as more businesses are on the way to moving to cloud services, leaving the data vulnerable to cybercriminals and creating entry points for potential threats and data breaches. Speaking of data privacy, it is also one of the currently typical challenges of big data and analytics. The concern is that the data may be mishandled and used for unethical or illegal purposes, which can violate the privacy of individuals. The recent GDPR and CCPA regulations in Europe and California are good examples that show how seriously data privacy is taken. Businesses that process the data of individuals from these regions must take extra steps to protect the data and ensure compliance with these regulations. That explains why businesses must have the proper big data security tools and strategies in place to prevent the risks of data breaches and privacy violations to the fullest. By taking some proactive steps, such as encrypting the data, building a data classification system, and deploying security analytics tools, businesses can reduce the risk of big data security threats and protect their valuable data assets. Merely collecting and storing all the information from different sources isn’t enough. The available data needs to be secured at all costs. The creation of technologies such as the cloud helps store data on a single platform so that it can be accessed anywhere, anytime, and make life easier for organizations and consumers alike. But, at the same time, storing all the vital data in one place has also given rise to several security issues. Thankfully, most of these broad-level problems may be handled if all the small steps are taken correctly and the data integration process flows smoothly without any hiccups. To achieve this sort of free flow of information and produce meaningful business insights, the accuracy of the data needs to be considered. Even a small step in the right direction can pay off big time. Organizations must begin with the goal of working on methods to bring the disparate data sources together and find a way to harmonize the data so that it can impress clients.

Inaccurate or Outdated Data

If you have too much data in your databases, it's likely that somewhere along the line you've inadvertently collected inaccurate data, or that some of your data is no longer valid. This problem starts at the collection process of your data lifecycle and is especially prevalent if your business is collecting data from a multitude of different sources and formats. If data collection isn't standardized across all channels, you can run into real problems when you need to analyze the data and extract insights from it. This data is also collected from different apps that don't always “talk” to each other, looked at by several teams that don't have access to the full picture, and analyzed without any safeguards to ensure data quality, validity, and security. In essence, poor data collection leads to low standards of quality and accuracy. And if you can't trust your data, you can't trust the analysis you get from it.

Data Quality

Quality of data poses one of the most significant challenges in the domain of data, so businesses should never overlook data quality. By quality, we mean all the aspects that ensure the collected and stored data is accurate, complete, and consistent. Poor-quality, fake, or invalid data probably leads to wrong data interpretation and uninformed decision-making, which can consequently jeopardize the success of big data projects. There are many factors that can affect data quality, such as human error, incorrect or missing data, duplicate data, and so on. To address such big data risks, you need to put in place good data quality control and conduct systematic performance audits at regular intervals. Also, you can use data cleansing and data enrichment techniques to improve the quality of your big data.

Data Silos

Data silos are another big problem that can occur when dealing with big data. If all of your information is stored in separate databases that don't communicate with each other, you've got data silos on your hands. What this means is that your teams aren't all looking at the same data, but instead only have access to a limited snippet that doesn't tell the whole story. If your teams can only see a portion of the data, it can lead to poor execution — it could be the reason why your marketing and sales teams are misaligned, or why your customer service department misinterprets a customer’s needs. Without a 360-degree view of your data, it's difficult to figure out how to build accurate, trustworthy reports and extract the best value. When data is stored in silos, it becomes difficult to access, manage, and analyze, thereby wasting time and resources. Also, it hampers collaboration and decision-making as different teams have to depend on each other to get the data they need. Among the causes, the primary one of data silos is the lack of communication and coordination between different departments within an organization. For example, the marketing team may be using one CRM system, while the sales team may be using a different one. This can result in data duplication and inconsistency, making it difficult to get a 360-degree view of the customers. Businesses need to have a well-designed data architecture in place that supports data integration and facilitates communication between different departments in order to avoid such big data challenges. Also, the key to breaking down data silos is to have a centralized data storage where all the data is stored and accessed by authorized users.

Internal Resistance

This seems to be uncommon but not nonexistent. This is an obstacle that often occurs within organizations that are in the early stage when their businesses start moving to the big data environment. Some employees may be hesitant to embrace big data and its potential benefits as they fear that it may lead to job cuts. Also, they may not be comfortable with the idea of sharing their knowledge and expertise with machines. As a result, they may resist change and refuse to use new technologies or follow new processes. Organizations need to first raise awareness about big data and its benefits among the employees. Afterward, they need to provide training programs and support to help them learn the basic knowledge of big data technologies and how to utilize the big data tools to grasp valuable insights and achieve their work efficiency.

Conclusion

Businesses need to devise ways to find, extract, arrange and store data. Thus, they have to develop a new database for storing all the information that does not fit the mold. The truth is, Big Data problems aren’t getting eradicated anytime soon. Organizations must come to terms with the fact that Big Data offers a chance for highly improved decision-making and planning.

While big data can be a game-changer for businesses, they need to be aware of the potential risks and challenges associated with it. By having a well-designed strategy in place and using the right tools and technologies, businesses can probably overcome the big data challenges and make the most out of them.