In today’s world big data represents both a huge opportunity for businesses to succeed and significant challenges. It holds the power to revolutionize so many parts of our day-to-day lives as well as scientific discoveries and business practices, but at the same time, it comes with many hurdles.
In this article, we will be discussing seven key challenges for big data and explore different ways and strategies to overcome them.
Well, first things first let’s define what actually is big data.
Although it has become such a widely used term, shockingly enough many still have a very vague understanding of what it is.
Big data refers to large volumes of data that are too complex to process using traditional methods.
This data is typically characterized by its volume, velocity, and variety, also known as 3Vs. However, nowadays value and veracity are also often added to this list making them 5Vs and here’s what each of them represents.
Volume – The amount of data collected or generated
Velocity - The speed at which said data is generated and processed
Variety - The different types of data sources and formats
Value - The ability to extract valuable insights from collected data
Veracity -Tthe reliability or trustworthiness of the data
Basically, it's a massive amount of information coming in at high speeds from different sources including social media analytics tools, sensors, or business transactions. The main purpose of big data technologies is to help organizations analyze and derive insights from this data to make better decisions, understand trends, and predict future possible outcomes.
Okay, now that we’ve cleared up what it is let’s take a look at what challenges it comes with.
The first challenge for big data on our list is the storage issues.
It is no secret that data volume is growing exponentially.
Every click, swipe, sensor reading, and social media post contributes to this constantly expanding digital tide. And these are the reasons why storing, managing, and processing such huge datasets can overwhelm traditional storage solutions and processing capabilities.
Sometimes traditional solutions simply cannot contain so much data and sometimes they crash and take hours to load properly. And if you gather data from your influencer marketing campaigns, website, internal research and analytics chances are a traditional system won’t be able to handle it. In a world that functions based on such systems, this is unacceptable and just outright wrong.
Luckily, in recent years cloud storages seem to save businesses from ever facing such issues.
Cloud storage platforms offer a scalable and cost-effective solution for big data. Cloud providers like Google Cloud Platform and Amazon Web Services (AWS) offer on-demand storage and processing power that can adapt to different data volumes.
This means that you won’t have to worry about your systems crashing because of data overload and won’t have to spend so much time and nerves waiting for your systems to load.
No more worrying about space on your hard drive!
Just upload as much or as little data on the cloud storage platform and go on with your day.
Big data comes in all different shapes and sizes.
What this means is that structured data from databases can sit alongside unstructured data like social media posts, sensor readings, and images.
This variety creates integration challenges, as traditional data analysis tools often struggle with so many different data formats.
And if your data is unorganized who needs it?
All your software and solutions provide insights based on this data so if it’s unorganized and is just a clutter of huge amounts of random snippets it might as well just not be there at all. Even when you’re simply producing essays or creating a marketing plan you need all your data to be organized to be able to actually use it efficiently, so don’t underestimate the importance of keeping your data clean and organized.
Invest in advanced data integration tools!
Data integration platforms can digest data from different sources, thoroughly cleanse it and transform it into a single unified format, making it readily available for analysis.
Seems too easy to be true but integrating such solutions is definitely your answer to reducing manual work on already collected data.
Data silos occur when information gets isolated within different departments or systems.
What this means is that data gets controlled by one department and is isolated from the rest of the organization. This fragmented data is a great obstacle to comprehensive data analysis and it prevents organizations from painting a complete picture.
For instance, the HR department might have valuable employee data in their RPO software siloed from the sales team's insights.
Or maybe the IT team has insights into the website performance that the sales team can use to increase checkout conversion or nurture leads, but it’s simply not available to them.
Well, the short answer is collaboration!
Breaking down a chain of data silos requires a complete cultural shift towards data sharing and collaboration. To encourage this it’s important to set up data governance frameworks that will define ownership, access controls, and usage guidelines for data assets.
Furthermore, you can always promote a data-driven culture through training and different incentives to encourage employees to share information openly.
Obviously, only the information that needs to be shared, nothing confidential!
Big data is the perfect target for cyber attacks. It’s a dream destination for cybercriminals, which means you have to work even harder to keep this large amount of data safe and far from their reach.
In case data branches end up happening they will have severe consequences that would be extremely hard to recover from. They will not only result in financial losses and legal complications but also harm your reputation, meaning you will instantly lose your customers’ trust.
I want to start of by saying that when it comes to data security it’s much easier and cost-effective to set up good security measures from the get-go rather than deal with the consequences later.
These measures include data encryption, access controls, regular security audits, and employee training on cybersecurity best practices. So follow these practices and make sure that your data storage, processing, and transmission mechanisms adhere to cookieless privacy regulations like GDPR and CCPA.
Moreover, apart from following all these regulations, it’s crucial to promote a culture of data ethics and compliance within the organization to maintain trust both with customers and stakeholders. Moreover, you can collect customer feedback to find out if there are any areas for improvement when it comes to trust.
In short, ensure data compliance and communicate it to the interested parties.
One of the problems of big data is in its name - it’s too big.
No this is a problem because it’s often quite challenging to extract useful insights from so much data. Not to mention, traditional data analytics tools struggle to identify patterns or hidden correlations within such massive datasets, whether you collect them through web scraping API free or from your own research and databases.
If you miss out on the extraction of valuable data then what’s the point of gathering so much of it anyway?
One thing that’s certain is you can’t avoid using AI when it comes to large or even not-so-large amounts of data.
AI and machine learning algorithms are the best tools at processing large amounts of data and identifying any hidden patterns. Techniques like natural language processing (NLP) can analyze unstructured data like text, while machine learning algorithms can discover trends and relationships in complex sets of data. You can also look up AI usage statistics to figure out how exactly you can use AI to your advantage.
So, if you want to ensure that the data you collect is used and interpreted correctly, providing you with crucial insight make sure to invest in advanced data mining software and tools.
Doing this will not only help you make assumptions and predictions about your customers and your company but also overall trends in the industry, and why not, the economy in general.
Big data operation, like almost any other job, requires a skilled workforce with experience and knowledge in data analysis, data engineering, and, of course, big data technologies.
The issue is that the demand for these skills and expertise is often much greater than the supply, which makes it extremely challenging to find and retain top talent.
Big organizations can bridge the talent gap by investing in employee training programs on big data technologies and data science methodologies.
Moreover, they can create programs designed to teach everything there is to know about big data from scratch. These can be in the form of internships and can be easily done by collaborating with universities or even schools, allowing companies to train potential employees from a young age. You can also use Kanban to manage the projects and tasks and allow the team members to see the whole process from start to end.
And believe it or not, these young adults are more likely to turn into the perfect employees than your hires that already have lots of experience.
Why? Because you taught them everything they know and you molded them in shape that’s perfect for your needs specifically.
Additionally, make sure to encourage a culture of continuous learning and provide opportunities for professional development to your employees, as this can also help you attract and retain skilled professionals.
Make sure to also provide them with benefits and bonuses and implement them in your payroll software to streamline the process for you.
Big data initiatives can and usually are pretty expensive. From data storage and processing costs to investments in technologies and talent, the price tags are often quite hefty.
In this article alone, I have already suggested a couple of different software and tools you should consider using if you want to improve your big data management efforts and confront challenges for big data.
So, how do you not go bankrupt if you have so many expenses?
Well, first of all, even if you think you have the budget for it don’t go around splashing money left and right. Be cost- conscious, because a cost-conscious approach is essential for big data success.
Make sure to carefully evaluate different storage and processing options to find the most cost-effective solution for your needs.
Maybe software X software gives you features that you don’t necessarily need, and software Y gives you just what you want but is not as popular as X and is way cheaper.
What I’m saying is don’t always go with the popular and more expensive option, consider other factors as well, because what might be an excellent choice for a big organization might be a waste of money for a smaller one and vice versa.
Moreover, consider prioritizing high-value data projects with a clear return on investment (ROI) ensures that big data initiatives deliver tangible benefits to the organization rather than more risky ones.
The challenges for big data are not minor, but they are not unbeatable either!
By adopting innovative technologies, promoting a data-driven culture, and prioritizing security and talent development, organizations can fully use the power of big data to gain valuable insights, improve decision-making, and achieve a competitive edge.
The world of big data is not standing still and is always changing, so the journey towards becoming a data-driven organization requires continuous learning and adaptation.
I hope what you read today helped you in one way or another, good luck on your big data journey.
