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Data volumes are continuing to grow and so are the possibilities of what can be done with so much raw data available. However, organizations need to be able to know just what they can do with that data and how much they can leverage to build insights for their consumers, products, and services. Of the 85% of companies using Big Data, only 37% have been successful in data-driven insights. A 10% increase in the accessibility of the data can lead to an increase of $65Mn in the net income of a company.
While Big Data offers a ton of benefits, it comes with its own set of issues. This is a new set of complex technologies, while still in the nascent stages of development and evolution.
Some of the commonly faced issues include inadequate knowledge about the technologies involved, data privacy, and inadequate analytical capabilities of organizations. A lot of enterprises also face the issue of a lack of skills for dealing with Big Data technologies. Not many people are actually trained to work with Big Data, which then becomes an even bigger problem.
This is not the only challenge or problem though. There are other challenges too, some that are identified after organizations begin to move into the Big Data space, and some while they are paving the roadmap for the same.
Here, we will discuss the top four critical challenges that enterprises are likely to face, if they are planning on implementing Big Data.
- Handling a Large Amount of Data
There is a huge explosion in the data available. Look back a few years, and compare it with today, and you will see that there has been an exponential increase in the data that enterprises can access. They have data for everything, right from what a consumer likes, to how they react, to a particular scent, to the amazing restaurant that opened up in Italy last weekend.
This data exceeds the amount of data that can be stored and computed, as well as retrieved. The challenge is not so much the availability, but the management of this data. With statistics claiming that data would increase 6.6 times the distance between earth and moon by 2020, this is definitely a challenge.
Along with rise in unstructured data, there has also been a rise in the number of data formats. Video, audio, social media, smart device data etc. are just a few to name.
Some of the newest ways developed to manage this data are a hybrid of relational databases combined with NoSQL databases. An example of this is MongoDB, which is an inherent part of the MEAN stack. There are also distributed computing systems like Hadoop to help manage Big Data volumes.
Netflix is a content streaming platform based on Node.js. With the increased load of content and the complex formats available on the platform, they needed a stack that could handle the storage and retrieval of the data. They used the MEAN stack, and with a relational database model, they could in fact manage the data.
- Real-time can be Complex
When I say data, I’m not limiting this to the “stagnant” data available at common disposal. A lot of data keeps updating every second, and organizations need to be aware of that too. For instance, if a retail company wants to analyze customer behavior, real-time data from their current purchases can help. There are Data Analysis tools available for the same – Veracity and Velocity. They come with ETL engines, visualization, computation engines, frameworks and other necessary inputs.
It is important for businesses to keep themselves updated with this data, along with the “stagnant” and always available data. This will help build better insights and enhance decision-making capabilities.
However, not all organizations are able to keep up with real-time data, as they are not updated with the evolving nature of the tools and technologies needed. Currently, there are a few reliable tools, though many still lack the necessary sophistication.
- Data Security
A lot of organizations claim that they face trouble with Data Security. This happens to be a bigger challenge for them than many other data-related problems. The data that comes into enterprises is made available from a wide range of sources, some of which cannot be trusted to be secure and compliant within organizational standards.
They need to use a variety of data collection strategies to keep up with data needs. This in turn leads to inconsistencies in the data, and then the outcomes of the analysis. A simple example such as annual turnover for the retail industry can be different if analyzed from different sources of input. A business will need to adjust the differences, and narrow it down to an answer that is valid and interesting.
This data is made available from numerous sources, and therefore has potential security problems. You may never know which channel of data is compromised, thus compromising the security of the data available in the organization, and giving hackers a chance to move in.
It’s necessary to introduce Data Security best practices for secure data collection, storage and retrieval.
- Shortage of Skilled People
There is a definite shortage of skilled Big Data professionals available at this time. This has been mentioned by many enterprises seeking to better utilize Big Data and build more effective Data Analysis systems. There is a lack experienced people and certified Data Scientists or Data Analysts available at present, which makes the “number crunching” difficult, and insight building slow.
Again, training people at entry level can be expensive for a company dealing with new technologies. Many are instead working on automation solutions involving Machine Learning and Artificial Intelligence to build insights, but this also takes well-trained staff or the outsourcing of skilled developers.
Big Data technologies are evolving with the exponential rise in data availability. It is time for enterprises to embrace this trend for the better understanding of the customers, better conversions, better decision making, and so much more.
It is important for enterprises to work around these challenges and gain advantages over their competition with more reliable insights. Is it the right time to invest in Big Data for your enterprise?