The development of an ever changing connected world means that there has never been a time when more bytes of data were being created. Some estimate that in excess of 2.5 quintillion bytes of information are added to the already astonishing amount of stored data in a normal 24-hour period. With such huge numbers involved, it is hardly surprising that any businesses engaged with managing big data face certain challenges.
This is where expertise and experience in big data capabilities can be so advantageous. And with so many companies now using big data the demand for know-how in this field is only likely to grow.
In today's IT world, big data represents a huge opportunity for improved analytics, measuring key data points in real-time and gaining the insights that businesses need to make some of their most important strategic decisions. However, it should be noted that big data does not merely allow for superior decision-making. There are several questions that big data management poses, too.
What are the top five challenges for the big data industry?
Data Privacy and Cybersecurity
This is probably the number one concern for big data industry professionals nowadays. The challenges around data security are not merely those associated with regulatory compliance, although this is a big factor in its own right.
The challenge of data privacy alters according to the sensitivity – or otherwise - of the data concerned but there are also conceptual and technical considerations to take into account before you even begin to weigh up their legal ramifications.
In addition, big data systems are constantly changing – this is a cutting-edge industry, after all. However, the cybersecurity features that are needed to keep malpractice at bay do not always develop at the same rate.
Sometimes, legacy security systems are simply not up to the challenge of properly safeguarding the data that it is now routinely stored. Indeed, as big data operations continue to evolve, so the potential entry points for data breaches to occur increase. It is consequently a constant battle to maintain the security of systems handling big data.
Of course, when it comes to collecting and storing personal data, the regulatory framework commonly needs to meet multiple privacy laws depending on the citizenship of the person to whom the data relates, where they might be in the world and the local laws affecting the servers holding such information.
In other words, a single security breach could, in theory, break laws in different ways in different settings and lead to a catastrophic loss in brand reputation. To meet challenges like these, big data professionals must prioritise data security in all aspects of their chosen solution.
Coping With Ever-Growing Levels of Data
As mentioned, the amount of data being generated today is unprecedented. Although creating the capacity to store this vast amount of data is well within the capabilities of the big data industry, managing such exponentially growing information presents a true challenge.
Why? Because accessing and retrieving the stored data rationally is not as simple as merely beefing up the systems that store it. In short, big data firms have to look into storing data in less complicated ways so that system performance is affected as little as possible.
In theory, this is technically possible in all cases but, of course, it comes down to budgetary constraints and balancing management performance versus storage capacity in a way that does not cost the earth. Nowadays, one of the most cost-effective technical solutions is to opt for hybrid relational databases where they can be successfully integrated.
Synchronisation With Dispersed Data
Processing big data successfully relies on the proper transformation and extraction of data along with a rationalised approach taken to data loading. Where data is dispersed over multiple servers, perhaps in multiple data centres, so a data integration strategy becomes essential.
Yet, in this regard, synchronising the systems involved presents a major challenge to many big data experts. This is because such data may need to come into a staging area where the necessary synchronisation can occur before the various datasets can be loaded onto a single system together.
Depending on the number and variety of origination points of the required data, the speed of such big data synchronisation processes will come under strain. In other words, dispersed data commonly creates a problem when it comes to successfully integrating multiple data touchpoints. What's more, this is not just about the number of them but how isolated they are from one another.
Although there is some interesting work ongoing regarding this issue – principally focusing on ETL alongside a number of innovative data integration tools – this looks like a challenge that the big data industry has yet to fully address and is consequently one that may be around for some time to come.
The Handling Costs Associated With Big Data
Managing big data sets requires a great deal of expenditure. It has always been this way but because large datasets are becoming even more vast than they were, so these costs, too, are going in an upward trajectory.
Of course, handling big data is not just about the expenditure associated with data management - there are development costs, configuration costs and even new software costs to throw into the mix even before you look at the spending that might be needed on server hardware.
Of course, some open-source software solutions help to deal with the issue of cost but these are not always suited to companies that want a bespoke big data solution that meets their exact requirements. Even when a cloud-based platform is preferred, there are some large sums involved when it comes to hiring the necessary staff to oversee it.
In fairness, effective business planning is the most useful way of bearing down on such increasing costs, so that spending is minimised and, where it is necessary, it goes into supporting commercial priorities. Industry professionals also make use of data lakes where they are viable. These are known to provide a less costly approach to data storage in certain applications.
Recruitment and Retention Challenges
Finally, the big data industry faces a skills shortage. Although data miners, analysts and other IT specialists are becoming more plentiful, they are often snapped up quickly and remain highly sought-after. The industry also has a high turnover of specialists, some of whom will simply want to move into technical roles outside of big data specialisms.
Although increasing automation – not least the deployment of ever-more sophisticated artificial intelligence systems – will plug some of the gaps in this skills shortage, the industry still needs to train more people with the necessary coding ability to support it fully.
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