In today’s age, various social apps are being developed that results in increasing the data considerably every day. When we mainly talk about the social media platforms, millions, in fact, billions of users connect daily, information is shared whenever users use a social media platform or any other website, so the question here is how this massive amount of data is managed and which tools are used to process the data. This is the point where big data comes into light. As organizations have entered in their digital transformation journey, big data plays a significant role in becoming successful.
The volume of data is increasing continuously, and so are the chances of what can be done with so much raw data. But, organizations need to know what they can do with the available data and how they can use it to build insights for their customers, products, and services. It is found, out of the 85% of the companies using big data, only 37% have been successful in producing data-driven insights. A 10 percent rise in the accessibility of the data can lead to an increase of $65Mn in the annual income of an enterprise.
The big data is firmly embedded in the business dialogue. The amount of data being generated is astonishing. According to Cisco, the annual global IP traffic might reach 3.3ZB per alum by 2021. Also, the number of devices connected to IP networks will become more than three times the global population by the year 2021. Gartner also predicted $2.5M per minute is spent in the IoT and 1M new IoT devices will get sold every hour by 2021. It is evidence of the speed with which digital connectivity is transforming individuals’ lives all over the globe.
Big data technologies offer excellent advantages to an organization. It is capable of answering questions in seconds instead of days. Moreover, the acceleration allows businesses to allow quick reactions to critical business questions and issues that can build competitive advantage, enhance performance and provide answers to complex problems that have resisted analysis.
Major Big Data Problems
As big data provides numerous benefits, it also comes with its own set of issues. It is a new set of sophisticated technologies, while it is still in the initial stages of development and evolution. Some of the most commonly faced issues include inadequate knowledge regarding the techniques involved, lack of analytical capabilities of the organization, and data privacy. Also, not many people are trained to handle and work with big data, which later emerged as an even bigger big problem.
Well! These are not the only challenges or problems. There are other issues, too, some of which are detected after an enterprise begins to move into the big data space, while some when they are making the roadmap for the same.
Here, mentioned below are the significant challenges associated with big data.
Managing a Massive Amount of Data
Data is available in a massive amount. If we look back and compare it with today, you will see that there has been an exponential increase in the data that the organization can access. They have data for everything, from what a customer likes, to how they react to a particular fragrance, to the fantastic restaurant that opened up last week in a nearby locality.
The data spills the amount of data that can be stored, computed, and retrieved. The problem is not so much of availability, but the management of course. The statistics claim that the data get an increase to 6.6 times the distance between the earth and the moon this year.
With the rise in the unstructured data, there has been a rise in the number of data formats. Audio, video, social media, and smart device data are among some of them.
The latest way to manage this data is a hybrid of relational databases combined with NoSQL databases. For example, MongoDB is an inherent part of the MEAN stack. There are also distributed computing systems such as Hadoop to manage significant data volumes.
Most of the organizations claim that they face trouble with data security too. This seems to be a more significant challenge for them than any other data-related problem. The data that comes into a company is made available from a wide range of sources, some of which can’t be trusted to be secured ones and stands with the standards of the organization.
They are required to use a wide variety of data collection strategies to keep up with data needs. It, in turn, leads to inconsistencies in the data, and then the results of the analysis. An example of this is an annual turnover for the retail industry can be different if analyzed from various input sources. A business needs to adjust the differences and narrow it down to an answer that is both interesting and valid.
As the data is made available to various sources, therefore it has the potential of security problems. You might never know which channel of information is compromised resulting in compromising the security of the data available in the organization and providing hackers a chance to move in.
Therefore, it is necessary to introduce the best data security practices to protect and secure data collection, storage, and retrieval. Using a VPN can also ensure data security and provides numerous benefits too. So what are you waiting for? Click on this link and get an insight into the best VPNs providers.
Lack of Skilled Individuals
There is a shortage of skilled prominent data professionals available at present. This point has been raised and mentioned by several enterprises seeking to use big data better and build data analysis systems more effectively. There is a significant lack of experienced people and certified data scientists and analysts which makes the insight building slow.
Conducting workshops, seminars, and training people at entry-level can be a bit expensive for a company that is dealing with new technologies. Many people are interested in working on automation solutions both artificial intelligence and machine learning, to build insights; however, this also requires well-trained staff or skilled outsourcing developers.
Data Silos is an origin of a fixed data that remains under the control of a single department and remains separated from the rest. It stores the data in separate different units. The data in units are isolated from each other and can’t communicate. You can’t extract any meaning from such data as it is not integrated into the background. The possible solution to prevent data silos is to incorporate the data so that we can extract useful information from it.
Real-Time can be Complex
A lot of the data keeps on updating every other second, and enterprises need to be aware of it too. For example, if a merchandising company wants to analyze customer behaviour, the real-time data from their current purchase can help. There are data analysis tools present for the same-velocity and veracity. They come with ELT engines, computation engines, frameworks, visualization, and other necessary inputs.
The business must keep themselves updated with this data as well as with the stagnant and always available data. It will help them to build better insights and improve their decision-making capabilities.
But, not all organizations can keep up with the real-time data, as they are not updated with the evolving nature of the tools and technologies needed. At present, there are very few reliable tools and still many lack the necessary sophistication too.
Big data technologies are evolving with a significant rise in data availability. Now, it is time for enterprises to embrace this trend for a much better understanding of the customers, better conversions, better decision making, and much more. Organizations need to work around the challenges mentioned above and gain benefits over their competition with more reliable insights. They must set priorities and accept that it is the right time to invest in big data for your enterprise.
Rebecca James Enthusiastic Cybersecurity Journalist, A creative team leader, editor of PrivacyCrypts.