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  • Shivani Pai

Top 11 Big Data Challenges and How to Overcome Them

Top 11 Big Data Challenges and How to Overcome Them

As the name suggests, the challenges in big data usually occur in handling the vast data, storing, and analyzing the set of information spread across various data stores. And these challenges need to be dealt with effectively so that it does not turn out to be a costly mistake for the organization. As per a study by Gartner, the average financial impact of bad data quality on an organization is $9.7 million per year. Plus, businesses in the United States suffer a loss of $3.1 trillion yearly due to poor data quality, according to a report by IBM.

But if done well, it can streamline operations and even reduce operational costs. However, if such technologies are not incorporated or implemented within the present workflows, businesses could be in a tight spot.

"Data is a precious thing & will last longer than the systems themselves," said Tim Berners-Lee, Inventor of the World Wide Web.

Top 11 big data challenges and how to overcome them

1. Managing large volumes of data

Data creation is happening at lightning speed. However, the same does not hold for the storage and computing systems. These are still taking time to develop. One of the reports by IDC further stated that the amount of data available by the end of 2020 would be enough to completely occupy a stack of tablets that measures 6.6 times the distance between planet earth and the moon. Over a period, organizing unstructured data is getting more problematic, from 31% in 2015, 45% in 2016, and 90% in 2019. In addition, analysts claim that the unstructured data generated grows by 55% to 65% every year.


Companies need to organize big data workshops and seminars for everybody. Or conduct training programs for all the workers involved in handling the data. Plus, all levels of the organization must at least have a basic understanding of knowledge concepts. Further, it's wise to first make minor adjustments rather than attempting to implement big changes.

2. Finding and solving data quality issues

The analytics algorithms and AI applications can give out bad results if data quality issues exist in the big data systems. These issues can get difficult to solve as the data management and analytics teams try to source different data types.

To explain it with an example of the company Bunddler, an online marketplace for finding web shopping assistants who assist people in purchasing products and arranging shipments, faced such data-related problems when they wanted to grow their customer base. A significant growth driver for the company was using big data to create a highly personalized experience, reveal upselling opportunities and track the latest trends. Where does it lead? Duplicate entries and typos are the most common issues when getting data from multiple sources.


To ensure the data quality is favorable, Bunddler created an intelligent data identifier. It's developed to match duplicates with minor data variances and report any possible typos. In addition, it improved the accuracy of business insights generated by studying and analyzing the data.

3. Handling data integration and preparation complexities

Big data platforms help out a company that deals with collecting and storing large volumes of data. They also help in retrieving data needed for analytics. However, this will only have relevance if the data is updated constantly and is not as easy as it seems.

Constant data updating would require maintaining access to various data sources and having dedicated big data integration strategies. Some companies use a data lake as a catch-all repository for big data sets gathered from various sources. But, they ignore how to integrate this data. For optimal ROI on big data projects, it's best to have a strategic approach to data integration.


Problems of data integration are resolvable if companies purchase proper tools. Some of the simple data integration tools are listed below:

  • Talend Data Integration

  • Centerprise Data Integrator

  • ArcESB

  • IBM InfoSphere

  • Xplenty

  • Informatica PowerCenter

  • CloverDX

  • Microsoft SQL QlikView

4. Scaling big data systems effectively and efficiently

Companies spend a lot and waste a lot of money to store big data. But unfortunately, they don't have a proper strategy to employ the resources efficiently. Even organizing a company's data repositories requires consistent retention policies to remove old data, like how the data that occurred before the pandemic is not accurate in today's market.


Before going for big data solutions, data management teams should roll out plans to handle different data types. A data lake with the appropriate structure makes it easier to reuse data efficiently and cost-effectively. Even the recent technological breakthroughs have brought down the cost of data storage and computation. These developments have made it more accessible and affordable to store data in big volumes.

5. Evaluating and selecting big data tools

Data management professionals or teams can take a pick from a variety of big data technologies. But they are at their wit's end even while selecting from the most straightforward tools for analysis and storage.

Lenley Hensarling, chief of strategy at Aerospike, advised teams to consider present and future needs for data from streaming and batch sources, such as mainframes, cloud applications, and third-party data services. He then maintained that the teams must evaluate the data preparation capabilities required for AI, machine learning, and other complex analytics systems. Plus, they should also plan for where the data gets processed. You'll also have to weigh capabilities against the cost of deploying and managing the equipment and applications run on-premises or in the cloud.


To simplify this task, you can hire experienced professionals with hands-on knowledge about these tools. Or you have consultants who will provide a recommendation for simple tools that align with your company's needs. Then, following their advice, you'll compute a technique and select the less complex tool.

6. Generating business insights

At times, data teams focus on big data technology rather than outcomes. In several instances, quite a little attention is on what to do with the data. When you generate business insights from big data applications, you have to consider instances like creating KPI-based reports, identifying valuable predictions, or making different recommendations.


All these can be possible with inputs from business analytics professionals, statisticians, and data scientists who have expertise in machine learning.

7. Attracting and retaining workers with big data skills

A significant challenge in big data is hiring and retaining skilled workers in this field. The buzz around big data won't be dying anytime soon. So the next best thing would be to employ people skilled enough to deal with big data software.

In a report by S&P Global, cloud architects and data scientists are among the most in-demand positions in 2021. "Many big data initiatives fail because of incorrect expectations and faulty estimations carried forward from the beginning of the project to the end," said Pablo Listingart of ComIT. And when you have the right team, you'll not only estimate risks but also evaluate the severity of the issues and resolve a variety of big data challenges. But it's also essential to foster a work culture that attracts and retains the right talent.


Companies invest a whole lot of money to recruit skilled professionals. However, they even have to conduct training programs for the existing staff. A vital step taken by companies is purchasing knowledge analytics solutions powered by artificial intelligence or machine learning. And professionals who aren't data science experts but have the basic knowledge to explore these big data tools. By implementing this step, companies save lots of cash for recruitment.

8. Keeping costs under control

The other challenge for companies using big data solutions is the cost they bear. They use existing data consumption metrics to estimate the costs of their new big data infrastructure, but that's a mistake. The problem is that the companies underestimate the demand for computing resources. The cloud, in particular, offers access to richer, more granular data, something that can raise costs because cloud systems elastically scale to meet user demand. Even using an on-demand pricing model increases costs.


To solve the issue of rising data costs, companies can go for fixed resource pricing. Unfortunately, it won't completely resolve the problem. Even if the meter stops at a fixed amount, poorly written applications may still impact the resources that bring down productivity for other users and increase their workload. Even the pay-per-use model is common to all public cloud services.

A similar model is adapted and adopted for budgeting resources in private or hybrid on-premise environments to bring more significant cost and resource efficiencies. The users will optimize hardware and virtual machines (VMs) for system utilization.

9. Governing big data environments

Data governance becomes a menace when big data applications expand over other systems. This problem occurs as new cloud architectures allow companies to collect and store all the data they collect in unaggregated form. At times, even the protected information can accidentally creep into various applications. And when data governance is absent, the advantage obtained from broader, deeper data access gets lost.


By treating data as a product, you can have built-in governance rules. Companies need to focus on three critical aspects of building an effective data governance strategy — people, processes, and technology. You ensure that your organization remains compliant and adds value to your overall business strategy with such a strategy.

10. Ensuring data context and use cases are understood

Companies often stress the technology without understanding the context of the data and its uses for the business. "There is often a ton of effort put into thinking about big data storage architectures, security frameworks, and ingestion. But very little thought is put into onboarding users and use cases," said Adam Wilson, CEO of data wrangling tools provider Trifacta.


Teams need to think about who will refine the data and how. Those who deal directly with business problems need to connect with those who work in technology. This way, they can manage risk and ensure proper alignment. It involves thinking about how to democratize data engineering. It's also helpful to build out a few simple end-to-end use cases, understand the limitations, and engage users.

11. Securing data

A daunting task is securing these enormous volumes of data. While companies get sucked into understanding, storing, and analyzing their data sets, they ignore the security part for later stages. It is not sensible as unprotected data repositories will always be on hackers' radar. Companies can lose up to $3.7 million for a stolen record or a knowledge breach.


Currently, companies are employing cybersecurity professionals to guard their data. Other steps taken include -- data encryption, data segregation identity and access control implementation of endpoint security, real-time security monitoring, big data security tools, etc.

Wrapping up!

Big data is complex and requires special expertise. If your company does not have the resources to handle your data-related needs, then Datahut is here for you! We extract data in huge volumes to help our customers with their big data initiatives. With our round-the-clock large-scale data processing, you will have updated and quality data. It will enable you to generate better insights and make smarter decisions. Contact us now and let us handle your data woes.

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