The year 2019 saw some enthralling changes in volume and variety of data across businesses, worldwide. The surge in data generation is only going to continue. Foresighted enterprises are the ones who will be able to leverage this data for maximum profitability through data processing and handling techniques. With the rise in opportunities related to Big Data, challenges are also bound to increase.
Below are the 5 major Big Data challenges that enterprises face in 2020:
Research shows that since 2018, 2.5 quintillion bytes (or 2.5 exabytes) of information is being generated every day. The previous two years have seen significantly more noteworthy increments in the quantity of streams, posts, searches and writings, which have cumulatively produced an enormous amount of data. Additionally, this number is only growing by the day. A study has predicted that by 2025, each person will be making a bewildering 463 exabytes of information every day.
A report by Indeed, showed a 29 percent surge in the demand for data scientists yearly and a 344 percent increase since 2013 till date. However, the searches by job seekers skilled in data science continue to grow at a snail’s pace at 14 percent. In August 2018, LinkedIn reported claimed that US alone needs 151,717 professionals with data science skills. This along with a 15 percent discrepancy between job postings and job searches on Indeed, makes it quite evident that the demand for data scientists outstrips supply. The greatest data processing challenge of 2020 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data.
Out of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it. A major portion of raw data is usually irrelevant. And about 43 percent companies still struggle or aren’t fully satisfied with the filtered data.
Once you import data into Big Data platforms you may also realize that data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization with the originating system. This implies two things, one, the data coming from one source is out of date when compared to another source. Two, it creates a commonality of data definitions, concepts, metadata and the like. The traditional data management and data warehouses, and the sequence of data transformation, extraction and migration- all arise a situation in which there are risks for data to become unsynchronized.
Data collected from multiple sources should have some correlation to each other so that it can be considered usable by enterprises. In a recent Big Data Maturity Survey, the lack of stringent data governance was recognized the fastest-growing area of concern. Organizations often have to setup the right personnel, policies and technology to ensure that data governance is achieved. This itself could be a challenge for a lot of enterprises.
While Big Data opens plenty of opportunities for organizations to grow their businesses, there’s an inherent risk of data security. Some of the biggest cyber threats to big players like Panera Bread, Facebook, Equifax and Marriot have brought to light the fact that literally no one is immune to cyberattacks. As far as Big Data is concerned, data security should be high on their priorities as most modern businesses are vulnerable to fake data generation, especially if cybercriminals have access to the database of a business. However, regulating access is one of the primary challenges for companies who frequently work with large sets of data. Even the way Big Data is designed makes it harder for enterprises to ensure data security. Working with data distributed across multiple systems makes it both cumbersome and risky.
Whether it’s ensuring data governance and security or hiring skilled professionals, enterprises should leave no stone unturned when it comes to overcoming the above Big Data challenges. Several courses and online certifications are available to specialize in tackling each of these challenges in Big Data. Training existing personnel with the analytical tools of Big Data will help businesses unearth insightful data about customer. Frameworks related to Big Data can help in qualitative analysis of the raw information.
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