You are currently viewing Top 9 Problems with Big Data and How to Solve Them – Eun Rockwell
Eun Rockwell

Top 9 Problems with Big Data and How to Solve Them – Eun Rockwell

There are a lot of sensitive data issues, such as the key big data problems, that can be discussed. However, it is important to understand the meaning of big data. Different organizations describe big data in different ways. A specific organization may define big data in one way at one time and another way at a different time. What matters is the need and technology in use at the moment.

The way microenterprises describe big data could be entirely different from the way macro enterprises describe it. Based on these differences, it cannot be right to limit big data description in terms of gigabytes, terabytes, or petabytes. The better way is to describe it in terms of volume, value, velocity, variety, and veracity. Both micro and macro organizations face varying big data challenges. Each problem has unique solution strategies.

Big Data Defined

Experts around the world define big data in different ways. The commonest definition focuses on three major characteristics that start with the letter ‘V.’ Any organization whose databases meet these characteristics can claim to have big data.

  • Data Variety: Variety means the data is available in multiple formats. Some of it can be available as images, word processors, videos, PowerPoint, PDF, emails, and so on. All these different data formats are stored in structured Relational Database Management systems.
  • Data Volume: Volume is measured in terms of quantity. The amount available must be big enough to give an organization handling or storage challenges. Due to technological growth, organizations generate data from multiple online sources through multiple devices.
  • Data Velocity: Velocity means the speed at which data is generated in an organization. This data needs to be responded to in real time. Organizations in the categories of social media, e-commerce, and IoT often generate data at high speeds.

There are many other varying definitions given by different experts. Our focus currently shall be on these three Vs. Nobody can deny the role played by big data in transforming business growth. It makes the experience better because the influence touches every business sector worldwide. Organizations can generate big volumes of data today and extract big potential from it.

They can use it to predict changes in the market and enhance security. It can be used to target new market segments and achieve other unimaginable breakthroughs. Regardless of these benefits, organizations admit that big data presents its share of problems too.

Computer science experts are working round the clock to create solutions for each challenge. They study a variety of challenges and write reports of their findings and recommendations. Due to the challenges of writing or lack of enough time, many of these experts get writing help from the best essay review professionals.

When seeking or implementing solutions, there are other key challenges experts must pay attention to. They must have solutions for data security, ethical issues, privacy, and compliance issues. Oversight is required, too, but more often, the challenges posed by big data often overshadow these other challenges. This is because data always exists in an unstructured form and is highly unpredictable. Here are the top 9 big data challenges faced by organizations and their solutions.

Handling High-Volume Data

An organization reaches a point where the amount of data generated is higher than the storage capacity. A report by International Data Corporation reveals that data generated by the close of 2020 will be huge. It is stored in tablets, and the quantity required will be enough to be stacked from the earth to the moon 6.6 times. The challenges to managing unstructured data in 2015 were 31%. It increased to 45% in 2016 and shot to 90% in 2019. Data analysts argue organizations generate unstructured data at the rate of 55% – 65% annually.

So, what is unstructured data? Any data that cannot be stored in conventional databases or spreadsheets in its raw form is considered unstructured. Such data is hard to analyze or search for. Due to this, a lot of organizations disregarded unstructured data terming it valueless until recently. The main challenge with unstructured data is its existence in a variety of formats. Some of it is stored as audio, documents, video, smart device formats, and social media.

E-commerce transactions are growing at an unprecedented rate. IDC predicts this growth will reach 450 billion transactions daily. The total number of connected devices, according to Cisco Research, will reach 50 billion in the next five years. This sends a warning to organizations that the amount of generated data will massively grow at the same rate. They have to get ready to work on effective solutions.

Remedy: Use Unstructured Data Analytics Tools

Unstructured data analytics tools use AI technology to generate insights. These tools analyze big data daily and provide key information to organizations. The tools are used to perform different tasks as follows:

  • Generating reports
  • Data mining
  • Integration
  • Processing
  • Storage
  • Indexing
  • Tracking

It is almost impossible to manage unstructured data without these tools. They help organizations scan through voluminous data at super speeds and in real time. This helps organizations capture customer behavior in real-time and decide on the best products to suggest to them. The tools test regulatory needs to ensure the organization meets them. To get optimized benefits, organizations should move away from data silos to a more scalable data hub. For example, Adeptia makes B2B integration seamless and easier.

Lack of Data Science Experts

The thoughts and imaginations of managers often differ from that of data scientists. Inexperienced analysts often stray from the actual data value. Due to this, they give business insights that cannot effectively solve the current issues. Getting experienced data scientists who can deliver value is a big challenge to organizations. The main skills of an experienced data scientist are as follows:

  • Statistics skills
  • Communication skills
  • Software engineering skills
  • Modeling skills
  • Domain expertise
  • Data munging expertise

Different studies show computer science experts are highly compensated. Despite this, retaining them is a bigger challenge. Organizations can hire inexperienced experts but training them is quite costly.

Remedy: Leverage AI-Based Machines Where Human Talent Is Lacking

A lot of organizations are considering the use of automated AI-based solutions. They leverage deep learning, machine learning, and natural language processing to extract data and generate ideas. The automated process does not require manual coding to execute. Some organizations opt not to use machines but instead outsource talents.

Managers should be careful and avoid using poorly skilled experts. It would be much better to do headhunting of talent from reputable firms. If this approach fails to work, the use of automation and AI remains the best option. It is a cost-friendly and effective solution to the problem of the lack of experienced data scientists.

Dealing With Data Safety and Governance

Big data is generated from multiple sources, which often use distinctive data generation techniques and unique formats. Due to this, organizations record various data inconsistencies such as same value variables. This makes adjusting them a big challenge.

A single organization can generate information for one transaction across multiple departments. It can be generated from the local point of sale, accounts department, e-sales tracker, and the organization’s ERP. It requires effective data governance strategies to deal with such a scenario. One reality organizations must acknowledge is that big data doesn’t lack gaps.

It can never hit a 100% accuracy mark. Nevertheless, this cannot be a reason for organizations not to heed keeping controls to enhance data reliability. It is possible to find wrong information in big data. It may also contain duplicates and contradictions. Sometimes its quality can be inferior, making it redundant and unable to offer insights into opportunities. Organizations have no option but to increase data quality in all aspects.

Remedy: Use Different Data Cleansing Methods

An organization cannot effectively use data unless it converts it into a proper model. After this, the organization can achieve other goals as follows:

  • Comparing different sets of data based on their point of truth, like contact variants and spellings.
  • Creating record mergers and matching data with similar entities

The five main data characteristics are:

  • Consistency
  • Accuracy
  • Completeness
  • Auditability
  • Orderliness

The organization must also define its guides for preparing and cleaning data. These processes can be made easier by the use of automation tools. Another key thing is to decide which data is not necessary at the moment. This helps the organization to automate data purging to remove unwanted data before the collection process begins. These steps are important, but the personnel should bear in mind that it is impossible to achieve 100% big data accuracy. This is a fact the organization cannot ignore, and it must learn the best way to deal with it.

Managing Data Integrity and Safety

Data safety and integrity are major challenges in big data environments. The challenge is made worse by the presence of multiple channels and interconnecting nodes from which data flows. It increases the system vulnerability loopholes and the chances of hackers attacking.

Huge data is very critical, and a small error can lead to devastating losses. Organizations need to adopt high-level data security practices within their data control systems.

The Remedy: Prioritize Data Security above Everything Else

Organizations cannot afford to compromise data security over other processes. During the data structure design phase, organizations should pay close focus to data safety and integrity. The IT team should not delay until the advanced phases of structure development to consider online security details. If the team overlooks this importance from the start, it might be overcome by data security issues when least expected.

New big data technologies are evolving all the time, and an organization should not ignore its security aspects. The organization should update its security systems with each new technology. Lack of action can put the organization in a serious data breach challenge that compromises its integrity and reputation.

A lot of organizations are adopting advanced security solutions that can leverage machine learning technology to mitigate cybercrimes. Demand for big data security solutions is on the rise, and innovators are working day and night to offer solutions. It is expected that there will be more solution options available sooner.

Dealing With a Wide Range of Options

Technology and innovation are growing daily as professionals seek better big-data solutions. Due to this, customers in the big data analytics field have too many options. It makes it harder to choose the best option, and users often feel confused. The main problem is identifying a solution that will meet the business goals effectively.

This challenge is affecting even organizations that are well knowledgeable on big data issues. Having too many options surpasses the challenge of choosing the right applications to use. Data specialists use several approaches and techniques for data collection, analysis, and safeguarding. There can never be a single approach that solves everything.

The Remedy: Be Cautious When Choosing the Best Strategy

What is required by organizations is great caution when choosing the best big data solution strategies. During a search, many programs will pop up, but the organization should not rush to choose one. Sometimes data experts in the company might advise hiring data consultants as the best option. The company should effectively pinpoint the best appropriate approach for its specific objectives.

Dealing with Organizational Resistance

Companies around the world have experienced organizational resistance for hundreds of years. It is a common problem that every organization expects at one time or another. How the company handles the problem determines how well it overcomes it. It determines whether the organization will record big data success or not.

The Remedy: Build a Strong Company Structure and Culture 

Creating a strong database architecture supersedes involving a team of data scientists in the process. This is not a difficult part because an organization may opt for outsourcing for analysis. The main problem revolves around building a structure, architecture, and culture that promotes data-based decision-making processes. Many organizations today are faced with three main challenges when handling organizational resistance issues.

  • Inadequate organizational adjustments
  • Neglecting adaptation and appreciating the importance of middle management
  • Dealing with resistance within the business

Organizations undergo diverse challenges when they need to make serious adjustments. This problem is bigger in larger companies that have deep-rooted and well-scaled functions built on conventional mechanisms. Data professionals advise companies to consider hiring people with strong leadership skills. They should be knowledgeable on data management issues and be proactive in challenging the existing status quo. They need to suggest better solutions. An organization will achieve this goal fast by nominating an overall data officer.

NewVantage Partners released a survey recently and reported that 55.9% of Fortune 100 organizations hired a chief data officer to meet this need. This is something that can be replicated by other companies, but it is yet to be prioritized. If an organization lacks a chief data officer, it might require a greater commitment from its top leadership to solve its big data problems.

The Cost of Handling Big Data

Handling big data from the inception phase upwards is a cost-intensive undertaking. An organization may opt for an on-premises solution. This approach might require the organization to spend on the recruitment of experts, purchase of new hardware, software upgrading, and electricity. There will be additional costs for structure development, setup, configuration, and software maintenance. The big data framework required should be open source.

Another organization may opt for cloud-based big data solutions. They must spend money on hiring developers and administrators. There has to be a budget for cloud services, application development, and other related services. Both on-premises and cloud-based strategies require room for future expansions. This will eliminate the challenge of having too much data that has nowhere to store. It would be more expensive if the organization opts to start from scratch again.

The Remedy: Adopt Solutions That Meet the Organization’s Needs

The goals of one organization are different from those of another. Each company should spend money on solutions that meet its goals and unique technological needs. Whatever solution it adopts, the organization should be able to save a considerable amount of money. A certain company may desire to achieve a highly flexible solution, and a cloud-based big data approach can be the best.

Another company might have complex security requirements and opt to adopt an on-premises solution. Another organization may choose to adopt both and use a hybrid approach. Such an organization stores and processes part of its data in the cloud and others on-premises. As long as it is cost-effective and meets the company’s goals, it is a viable option.

Another approach organizations may consider the use of data lakes and AI algorithm optimization, which can effectively save costs. However, organizations should opt for data lakes only for data that doesn’t require immediate processing and analysis. For companies seeking maximized computing power that exceeds 100x, the best option, in this case, would be algorithm optimization. To ensure the most minimal data management cost, the best approach is to conduct a company needs analysis and choose the best course of action.

Handling Data Integration Challenges

Data integration follows a six-step effective cycle:

  • The company experts discover data first
  • They progress to the data preparation phase
  • They move on to model planning
  • Model building
  • Results communication
  • Operationalization

The data is brought from several company departments into a central repository that every authorized user can access. It is necessary to note that this data comes from multiple sources and in a variety of formats. This is already a bigger challenge to the IT department when bringing all the data into one place. The need to make the IT architecture simple triggers the need to make big data flow and management easy. Considering it is coming from a variety of data processing platforms, this creates a dilemma for the IT team.

The Remedy: Avoid Adopting Manual Processes

Manual processes might seem cheaper, but it eventually escalates the cost. It is cheaper to go the automation way by choosing a top software solution. Some automation applications are loaded with hundreds of features and APIs. They can be applied to a wide range of databases and file formats. The IT team might be required to develop some of the APIs. Regardless, these are reliable tools that can effectively handle a big chunk of the work.

Handling Upscaling Challenges

One of the main advantages of big data is its super-fast growth. An organization can generate thousands of gigabytes of data daily. This volume can develop into several terabytes within a short time. This is an advantage to the company but also one of the greatest challenges. An organization might have a well thought and flexible solution that makes upscaling easy.

This solution may work well but poses challenges when launching fresh data storage and processing abilities. The upscaling must be done in a way that ensures the system performance is not affected. If it is affected, the organization may have to add another big budget to solve the challenge.

The Remedy: Develop a Strong Data Solution Infrastructure with Future Upscaling In Mind

These steps are crucial when developing a strong data solution infrastructure with future upscaling in mind.

  • The best place to start is in the architecture development phase. Make sure the infrastructure is strong to eliminate future challenges.
  • The organization’s big data algorithms should be designed in a way that allows upscaling if the need arises in the future.
  • Create maintenance plans and system support infrastructure that can address data growth evolution effectively.
  • Perform system functionality audits to pinpoint any possible weaknesses and create solutions for them.

Making a company’s data infrastructure flexible helps it support both current and future processing and analytics needs. Due to the challenges that may arise, work on priority projects first. It can be more challenging to handle all big data solution projects at the same time. Build a structure that shows data movement from the source to its destination. This is one of the ways to maintain good quality and available data.


Organizations face diverse challenges in their efforts to manage big data problems. Regardless, each challenge has an effective solution that organizations can adopt. It is necessary to appreciate that each of these challenges evolves with time. The secret to their solution is to adopt effective techniques while keeping in mind the objectives and technological needs of the organization. Big data is not going away any time, and organizations should hasten to find solutions for every data challenge it is facing.