Top Big Data Challenges for 2018 that You can Mitigate through Data Science Training
2018 is proving to be the year of emerging technological trends, what with augmented and virtual reality taking a quantum leap in development, and self-driving cars now competing against human racers. Technology is the common denominator for progress, and organizations are on the front line for this wave of advancement.
The valuable questions, however, are how can this technology have a positive impact on your business goals? How can you create a practical image of the benefits that are to come from the implementation of said technology? And, most importantly, what is the driving force behind all this innovation and evolution?
The answer is, surprisingly, Big Data. Almost all technologies, especially AI, use big data as a foundation stone to know what manner of functionality to introduce in each new iteration. Additionally, big data is used by organizations to determine the ideal key performance indicators, for better organizational strategy and decisions.
Big Data is not without its challenges though, as well will discuss in this article. Read on to find out more regarding what obstructions and hindrances organizations face, while handling huge volumes of data.
Immense Data Volumes outpacing Data Storage Technology
Big data is increasing in size and scope, and storage technologies are, unfortunately, not growing at a proportional pace. In fact, according to a report by IDC, conducted back in 2014, the amount of data that will be accumulated by the year 2020, will be exactly 6.6 times more than the distance between the moon and earth. Since big data is unstructured and not arranged according to criteria, it is often difficult to store the entirety of it, which causes some important data to be lost, or corrupted in the storage process.
The various data formats are also to blame for their shortcoming. Online streaming and social media platforms such as YouTube and Facebook are often responsible for hosting data which can amount to several gigabytes for each data piece. Multiply that by the millions of pieces that are required to amount to Big Data, and you have a storage management nightmare on your hands.
Lack of Data Security and Governance
Data inconsistency is common when dealing with big data, and it is often the case when data from multiple sources is accumulated under one roof. Similar variables may have different variables, and it is often difficult to compensate for said variables. This process, also known as data governance, is a challenge in itself, considering the size and scope of the data involved.
The integrity and security of data is another issue, seeing as the number of interconnecting channels and nodes is quite high, which is an opportunity that hackers will exploit. Now, the more critical the data, the more it will need to be protected from malicious attacks by outside parties.
Insights in Real Time
Accumulating stagnant data, and receiving it in real time is quite different; with the latter being significantly more complex. Data is often rapidly organized in real-time situations, which causes some relevant information to be lost, mainly due to the first challenge mentioned in this list.
Industries such as banking, healthcare and insurance require faster decision-making, which can only be made possible with the influx of data I real time, resulting in real-time insights. Fully automated data extraction and organization tools have made data scientists’ lives easier in this regard, however, true real-time insights are still not available on a global scale, leading to some organizations struggling to receive actionable insights as quickly as possible.
Insufficient Data Scientists
It is an unfortunate fact that the amount of data is not just outgrowing storage facilities, but also the number of human resources, in the form of data scientists. Business intelligence analysts, big data engineers and data scientists are all in high demand, with top talent commanding tremendous salaries due to the skills involved and the shortage of competent individuals.
The onset of innovation has promised automation and artificial intelligence in the place of human data scientists. This technology already exists and is being mobilized across the globe. However, it is not optimized for the unique data needs of every organization and is definitely not a viable replacement for talented data scientists; at least not yet.
Implementing Data Science Training to Overcome Big Data Challenges
Fortunately for organizations everywhere, the challenges to big data integration and implementation can be easily mitigated by training the relevant teams on big data fundamentals. Data science training can assist with not just handling big data across the enterprise systems, but also accurately managing the data, and extracting key business insights which then lead to better ROIs for the enterprise.