The rapid and exponential growth of big data comes with a lot of challenges. For the most part, this can be directly attributed to the sheer volume of data that’s being continuously generated.
With the rapid adoption of the Internet of Things across industries, the data that’s being continuously generated is only going to keep increasing by the second.
While a lot of real-world value can be derived from these oceans of data (which can easily get into the Petabytes range), we still need to come up with better ways to collect, store, analyze, and secure it.
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Enterprises will also have to come up with innovative ways to handle the volume, velocity, and variety of data that’s generated to ensure that the data is authentic and synchronized across all data centers in real-time.
Responding to Data Growth, Integration, Validation, and the Demand for Real-Time Analytics
A lot of the data is unstructured, so it will be difficult to access it and analyze it when it’s not resting in a database.
This has forced organizations to explore and adopt various technologies like software-defined storage and hyper-converged infrastructure to scale their hardware.
To reduce the amount of storage space and the costs associated with it, companies have to leverage compression, deduplication, and tiering technologies. As data usually comes from disparate sources, it can also be a real headache when it comes to data integration.
While there are many data integration tools like Adeptia, Oracle Data Integrator, and Talend, this has still been a challenge for several businesses. However, for integration to be successful, the data has to be validated.
This will require solving data governance challenges. This will be highly complex as enterprises will have to make some technology and policy changes. At the same time, it’s also important to ensure data integrity and immutability as data that gets modified will be of little to no value.
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When it comes to managing and rapidly analyzing large volumes of data, it get’s a little easier as there are more than enough tools like Hadoop, Spark, business intelligence applications, artificial intelligence (AI), and machine learning (ML) to choose from. But the overall success of your big data projects will heavily depend on the interrelated variables mentioned above.
So how do enterprises respond to this in a cost-efficient manner? The solution that can effectively respond to these big data challenges is the blockchain.
The highly decentralized nature of the blockchain is key to effectively responding to big data challenges. When your data is on the blockchain, no single entity will be able to control or manipulate the data’s entry or integrity and this data will be continuously verified by every computer on the network.
This means that if any piece of data becomes corrupt, it won’t become part of the chain. This will make it immutable and maintain the integrity of the data as long as the network exists.
When you place your data on the blockchain, it will also create a single unchangeable resource for the company. While being enabled to handle large volumes of data, it also comes with the added benefit of enhanced security.
This is because anyone who has access to the blockchain will require multiple authorizations from various parts of the network before gaining access to the data. At the same time, it will also make it much easier to securely share records with all stakeholders.
While its a great solution to the problems faced by big data, it also presents another problem – big data hiring challenges.
Recruitment Will Be an Enormous Challenge
The demand for data scientists has been growing steadily year after year, research suggests that the demand for these data professionals will soar by 28% by 2020. This means that the demand for data engineers, data developers, and data scientists will reach a whopping 700,000 in just a couple of years.
But it doesn’t stop there as you will also need to employ AI engineers and ML engineers to work with data scientists and synchronize their work.
Some other big data-related positions that will be in demand are as follows:
- AI hardware specialists
- Blockchain engineers
- Data labeling professionals
- Data protection specialists
While big data is certainly going to play a key role going forward, recruitment will be the biggest challenge for enterprises in 2018.
One of the best ways to respond to this problem would be to nearshore or offshore all or some of your big data functions. This is because countries like Ukraine in Eastern Europe boast large pools of data scientists that can help your company achieve its goals and remain relevant for years to come.