10 Big Data Challenges: Expert Solutions to Solve Them

January 25, 2023
Get advanced tips with our free guide
Download Free Expense Analytics Data Sheet
Get advanced tips:
Get advanced tips

70% of the globe’s GDP will have undergone digitisation by 2022. We’re living in a time where data is all around us. With data available and generated everywhere, there’s no denying that big data challenges exist, especially for organisations.

We’re going to share common big data security challenges and analytics challenges. We’ll also quickly review how you can use automation software to overcome the common challenges of big data.

Coming Up

1. What is Big Data?

2. What are the Challenges of Big Data and Solutions of Big Data?

3. What are Big Data Analytics Challenges Across Different Industries?

4. How to Create an Effective Big Data Strategy?

5. Wrap Up

What is Big Data?

Rather than quantifying big data in terms of gigabytes or terabytes, it’s easier to define it using the “Three V’s.” This is because what’s considered big data for one organisation may not be the same for another, so there’s objectivity in this definition.

  • Volume: This refers to the amount of data that is generated, processed, monitored, and stored.
  • Velocity: Velocity defines the speed at which data is generated and utilised.
  • Variety: This refers to the various types of formats in which data appears (i.e. email messages, images, videos, word processing documents, etc.)

What are the Challenges of Big Data and Solutions of Big Data?

As ubiquitous as big data is, so are big data challenges. Luckily, for each challenge, there are proven solutions to help keep data protected, properly utilised, and shareable.

Let’s take a look at the common challenges:

1. Lack of Knowledge

In this case, the data isn’t the issue. To run modern technologies, companies typically require experienced professionals.

They may hire data analysts, data engineers, and data scientists who are able to collect, format, protect, and use the data they capture. However, with the growing amount of tools on the market and the expanded use cases of data, there’s a lack of professionals who can fit the bill for the job.


One of the primary solutions to overcome the high-skilled professional shortage is to make use of data tools that don’t require such staff. With low code and no-code automation solutions, anyone is able to design workflows, analyse data, and glean insights from the analytics.

2. Rapid Data Growth

Data grows at an explosive rate. This avalanche effect can become overwhelming for companies to keep up with. The large amount of data that can be pulled from various sources is often unstructured and isn’t stored in databases. This all lends to the big data infrastructure hurdles that companies end up facing.


To be able to keep up with the proliferation of data, companies seek software-defined storage and hyper-converged infrastructure. Another way to remove the unnecessary storage of extra data is to be able to delete duplicates and compress data, all in an effort to reduce storage costs.

3. Real-time Insights

One of the most common big data challenges is data going to waste due to the inability to gain insights. Many companies collect and store data, but don’t know how to make use of it. Without real-time insights, data becomes old very fast. That being said, it takes time between data extraction and analysis.


One of the best ways to gain real-time insights is to use an automated data software solution that can process in real-time. This way, your company is ingesting data, and at the same time, reports are generated that provide valuable information.

4. Data Validation

Another hurdle of big data is data validation. Data validation falls under a broader umbrella of data governance. It means that data from various sources agree with one another and are therefore usable and considered accurate.


Many times, companies will hire data governance teams to handle data accuracy. Additionally, you can leverage solutions that are able to cross-check data sources and provide governance through inherent security measures. This way, you can overcome big data security challenges.

5. Organisational Resistance

Organisational resistance appears in more aspects than just big data. You’ve likely dealt with it before, but it refers to the resistance felt by employees and stakeholders to be open to change.

Since big data is only growing with time, some organisations feel afraid to adopt new technologies or handle situations regarding big data challenges.


Along with change management, a great way to overcome the resistance is to clearly communicate a goal and showcase how to get there.

It has a lot to do with communicating the benefits of big data to your team and being able to show them how big data is going to help make their lives easier. The same can be said about automation solutions that can be used to collect, manage, store, and use big data.

What are Big Data Analytics Challenges Across Different Industries?

With big data comes the need for analytics. This makes it possible to get use from your data so you can make informed business decisions, better serve customers, and improve business operations.

Across industries, companies run into big data analytics challenges, too. Like big data challenges, they can be overcome using the right approach and useful automation software solutions.

Let’s take a look:

1. Locating Data

The more data you collect, the more you have to organise and cleanse in order to use. The truth is that you’ll end up collecting more data than you actually need for your business purposes.

So, one of the main problems that arise is being able to locate relevant data when you need it. Since data enters the business through multiple channels and can be unstructured, there’s the need to format and validate the data. Automation solutions can help to collect, format, cleanse and organise your data. This way, you have what you need, when you need it.

2. Outdated Data

Besides not having access to the data you need, you may be collecting data that quickly becomes outdated.

Having outdated data is utterly useless because it’s no longer relevant. Data quickly changes, so if it hasn’t been standardised across its collection points, it will be hard to know what data is up-to-date versus records that are old.

3. Siloed Data

Just like you want your teams to work together, data shouldn’t become siloed either. When different teams have access to different data, mistakes can occur.

Rather, you want connected data and to enable collaboration across business units. When teams can access the full story, they’ll be able to gain the most value from the data and the insights.

Automation solutions connect all data streams, along with legacy systems, so that its users always have the big picture view (as well as the granular close-up).

4. Securing Data

When it comes to big data challenges and analytics challenges, a primary concern ought to be data security and protection. When this goes overlooked, massive and detrimental consequences occur - from extra costs like fines to reputational damage and business shutdowns.

With automation software and big data solutions, security is inherently provided and updated to adhere to compliance and regulations.

5. Personnel

Finding people to organise data can get complicated, too. However, big data tools are making it easier for anyone to be able to gain insights from data in real-time.

How to Create an Effective Big Data Strategy?

While big data challenges can feel overwhelming, with the right strategy and plan in place, it can become easy to manage. Consider following these four steps to create an effective big data strategy:

  1. Define Your Objectives: Make note of what business objectives you wish to achieve using big data. Include stakeholders and employees as part of the process.
  1. Identify Data Sources: Review your current sources of data and how they are working in your favour. If you have many disconnected sources of data, consider using software to centralise your data in a single repository. This makes it easier to organise, use, and track.
  1. Prioritise Use Cases: Start small on your big data journey. Review your business objectives and organise them in order of priority.
  1. Execute Your Plan: Be honest about any gaps you may have in your current big data setup and where you want to be. Build out a roadmap including data architecture to fill in gaps and resolve issues. Then, execute your strategy and monitor it over time. If you find process improvement opportunities, jump on them.

Wrap Up

While big data brings with it big data challenges, it’s nothing that isn’t solvable. This is especially the case if you use automation solutions.

Automation solutions connect data from multiple sources, cleanse and validate it on the spot, protect your data, and can be used to generate reports. All-in-one no code/low code solutions also mean that you gain access to real-time insights so that you can make use of your data.


Related Posts

Our Top Guides

Our Top Guide

Popular Posts

Free Up Time and Reduce Errors

Intelligent Reconciliation Solution

Intelligent Rebate Management Solution