Through collecting and analyzing large amounts of data, companies can unlock unprecedented potential, from predicting new market trends to optimizing security to reaching new demographics. However, the age of big data analytics on the best big data platform and real-time computing does not come without its own unique challenges. Without taking the right approach, companies can be left without targeted, well-protected data.
Here are some of the most common big data challenges to your big data projects - and how they can be solved before having a negative impact on your company:
1. Too Many Options
As with any rising form of technology, there is no shortage of choice when it comes to big data analytics. It can be difficult to choose exactly which big data technologies are appropriate for your goals, even if you are relatively well-versed on data issues.
In fact, the overload of options goes beyond which programs to use. Data scientists employ a number of strategies and techniques to collect, protect, and analyze your data, with no one-size-fits-all approach.
The solution is obviously not to sift aimlessly through real-time data with whatever program came up first in a Google search. To get the best data, you need the best strategy. Professional data firms know how to tackle big data challenges and can help you find the best strategy for your unique needs.
2. Security Scares
When it comes to data, the potential for security breaches is vast. Big data analytics is a highly lucrative asset for any business and, as a result, any company storing data could face countless threats against the data to be stolen and used for nefarious purposes. Data security is a loss prevention issue as well as a privacy one.
Unfortunately, security breaches of such technologies are not as rare as we may like to think, either. The sad reality is data theft has spiked dramatically - 400% since 2012. Worse still, data breaches are far from an easy blow to bounce back from, with over half of all businesses experiencing such an ordeal shutting down within a year.
The focus should never be solely on how to address a data breach; instead making sure one never occurs in the first place. It is important all businesses pay for adequate data protection, instead of shirking away from the initial cost. It is also important to remember, with more data comes more risk, so it is wise to invest in security measures accordingly.
3. Converting Data to Insights
Make no mistake, collecting data within itself is a critical and complex task. However, all the real-time big data analytics in the world will not be relevant unless it can be converted into tangible, high-quality insights. Therefore, unstructured data is a big no-no.
Why is this such a challenge?
The issue is often in the initial strategy, rather than the data analytics itself. In order to use analytics to your advantage, you need a system in place that can look at all factors, variables, and data sources, without letting anything critical fall through the cracks - and ensure data integration.
A strong strategy is essential for good insights. If your company is collecting data analytics but unsatisfied with the results, it might be time to work with professionals to see what connections are being missed.
4. Finding Skilled Workers
There is an unfortunate talent gap when it comes to data science. As frustrating as it can be, it should not be particularly surprising, considering data science requires an enviable and versatile skill set.
Look for talent where it already exists. Rather than compromising and selecting under-skilled employees, consider firms that have already built positive reputations and snatched up talented workers that can help.
5. It’s all about Quality
Like any other asset, data can range in quality. Some analytics might be out of date, incomplete or inconsistent. This can make it challenging to gain insights that are actually relevant and applicable to your company. In fact, poor data can lead to businesses making poor or uneducated decisions that can have serious ramifications in the long run.
This problem can be solved when a company is ready to take data seriously and treat it as something truly valuable. By reaching out to consultants who can provide tailored strategies, quality software, and comprehensive collection, you can ensure that your data reflects the same level of quality as the rest of your company.
6. The Cost
Whatever industry your company falls under, chances are that your goal is to earn money. With the help of big data, you can make lots of it. Data services are ultimately an investment - meaning you will have to put a little money in if you want to get a lot of money out of it.
Handling data in-house can be particularly pricey. First, there are the new hires. Then, all of the new hardware and technology. Followed by software, cloud services, potential expansions. For too many businesses, the opportunity for valuable insights is passed upon because of the steep initial cost.
First and foremost, your company should prepare for the cost of data management and treat it as a serious but worthwhile investment. Here some tips and tricks to help your business handle the cost:
Preventing Problems Before They Start
If you are good at identifying patterns (but not so good you would trust yourself to oversee your own data analytics), you might have noticed a common solution among the many challenges of big data: your data is safest and most effective when it is handled by trained professionals.