Before going big data, every leader or decision-maker needs to be aware of what they are dealing with. And in the process of making these decisions, you will face certain challenges.

Now, if the company hasn’t done full fledged analysis and strategized accordingly, these challenges can be harder to overcome. Here, I am going to cover 7 major big data challenges that people face and provide you with solutions for each one of them. 

Challenge #1: Insufficient understanding and acceptance of big data 

A lot of companies tend to waste not only their precious time, but also their resources on things they don’t even know how to use.

And I strongly believe that without a decent understanding of big data’s value and resistance to change in existing processes, the company’s progress will be hindered.

Solution:

Big data is a major transformation for a company. And it is extremely important for it to be accepted by top-level executives first and then towards the lowest end of the scale. Thus, IT departments need to organize numerous training sessions and workshops in order to establish a better understanding and acceptance at all levels.

In addition to that, the application and use of big data solutions needs to be supervised and composed. 

Challenge #2: Confusing variety of big data technologies

Now, there is an abundance of big data technologies available to you which can create a lot of confusion. It is very easy to find yourself lost in this plentiful amount of technologies available on the market. And it's worse when you’re unsure of what you need when searching for the next technological opportunity.

Solution:

If you are someone who has no clue on where to begin when it comes to big data, then professional guidance will prove to be very helpful. There are many resources out there; you could consult an expert or turn to a vendor for big data consulting. In both scenarios, you will be successful in finding the right strategy and technology stack that will align with it. 

Challenge #3: Paying loads of money

Money, Money and more Money. Big data projects necessitate lots of expenses. Whether it be an on-premises solution or cloud-based big data solution, the first expense is the need to hire new staff (administrators and developers) who will actually make your strategy work.

On top of that, on-premises solutions, even though they have open-source frameworks, require development, setup, configuration and maintenance expenses. When it comes to cloud services, there are other expenses such as, big data solution development, setup and maintenance on needed frameworks. 

However, in both the scenarios, if you are looking to save money, you need to be flexible towards future expansions. 

Solution:

The preservation of your company’s penny will depend on its specific technological needs, strategy in use and business goals. For instance, there are companies that use the cloud for flexibility benefits. Whereas other companies might want on-premises because of extremely strict security requirements.

In addition to that, you can also find hybrid solutions where some parts of data are stored and processed in the cloud and other on-premises. And this strategy in particular can be very cost-effective. Moreover, using data lakes or algorithm optimizations (only and only if done properly) can also save money:

1. Data lakes can help you save money by storing data that is not needed to be analyzed at the moment.

2. Optimized algorithms can reduce computing power consumption by 5 to 100 times.

To sum it all, in order to save money, you need to analyze your needs and choose a corresponding course of action.

Challenge #4: Complexity of managing data quality

Data from diverse sources

Data integration is a major challenge that companies face sooner or later. This is mainly because data used for analysis is derived from various sources. And this data can be in a variety of different formats.

For instance, eCommerce companies need to analyze data from website logs, call-centers, competitors’ websites ‘scans’ and social media.

Unreliable data

Like any other technology, even big data isn’t 100% accurate and no one is hiding it. As a matter of fact, it’s not that critical. But don’t get me wrong, you should definitely control how reliable your data is. Because it can always contain wrong and contradictory information. In addition to that, data can always duplicate itself. Thus, you need to always keep an eye out. 

Solution:

There are a bulk of techniques in the market solely for cleansing data. But first of all, your big data needs to have a proper model and the right strategy. Only then, you can go ahead and do other things, like:

• Correlate data with a single point of truth.

• If data relates to a certain entity, just match and incorporate it.

Challenge #5: Dangerous big data security holes

The most naive move that big data adoption projects make is putting security off till later stages. Time and time again, big data security gets overlooked. The tech evolves, but security is not a factor taken into consideration until the application level. 

Solution:

As the saying goes, precaution is better than cure. It is important to put security first. And this is the precaution against your possible big data security challenges. It is particularly important at the stage of designing your solution’s architecture. 

Challenge #6: Tricky process of converting big data into valuable insights

Has this ever happened to you that you saw an advertisement and you were like, “Damn! It looks so good, I want to buy it.”. Then you go to the store but it’s not available.

Now, you’re disappointed and you decide I am never going to buy anything from here. And as a result of your disappointment, the company lost revenue and a loyal customer.

Solution:

Now, you might be wondering where the problem is. The analysis done by a company's big data tool does not take into consideration the data from social media platforms or competitors’ websites. Whereas, the competitor might be keeping an out in near – real – time.

To solve this problem, the firm needs an ideal system, which, on analysis, brings useful insights and makes sure no meaningful information/data is slipped out. And this system must include external sources. 

Challenge #7: Troubles of upscaling

One of the most serious challenges in the field of big data is associated with its dramatic potential to grow. 

The major problem with upscaling is not the process. Even though your design might be adjusted in a manner that requires no extra effort, it won’t guarantee the same/better performance. There is a chance that it may even decline. 

Solution:

To the greatest extent, precaution for challenges like this is a decent architecture of your big data solution. One of the most important things you need to remember while designing your big data algorithms is future upscaling. 

But apart from that, there is a dire need to figure out maintenance and support of the system in advance, so that any changes can be taken care of in a timely fashion. And on top of that, holding systematic performance audits can help you identify weak spots and address them in a timely manner.

Win or Lose?

It is pretty evident, most of the reviewed challenges can be foreseen and dealt with if your big data solution has a decent, well-organized and thought-through architecture. And this requires companies to commence a methodical approach to it. 

But besides that, companies should:

• Hold workshops for employees to ensure big data adoption.

• Carefully select a technology stack.

• Mind costs and plan for future upscaling.

• Remember that data isn’t 100% accurate, but still manages its quality.

• Dig deep and wide for actionable insights.

• Never neglect big data security.

If your company follows these tips religiously, it has a reasonable chance of defeating the Scary Seven. And for expert advice on various challenges, feel free to contact our experts. Good luck on your journey exploring Big Data!