Using big data in scores of industries to produce business is indeed a reality. The data is getting bigger than ever and as a result, mostly facing data challenges: not just growing volumes but change of data coming in abundance from new sources. It is an asset and changing the way the data analytics are seen from mining to advanced analytics. The survey carried out in the last few months shows that about 25% firms have implemented big data initiatives and 70% companies are planning to put into practice big data initiatives.
The promise of big data is undoubtedly real and the growing enthusiasm is getting recognized globally. However, the data today is not in structured format and this cause a major challenge. Other challenges are modeling, data analysis and retrieval. Not only this, there are many challenges associated with Big data. Appropriate investment in big data will take to a unique advantage that would be exemplified in the future of big data management, systems, and analysis platforms.
Real time information, mobile devices and online marketing are alluring big data- one needs to get ready to handle this by creating proficiency in data structural design and analysis tools. Professionals know that the demand for big data knowledge and skills are booming in the market and they can knock your door, said by Dave Lounsbury, Chief technology officer at standards body The Open Group.
Have a look at some of the challenges in Big Data:
Hadoop is hard
Heralded as the best solution to handle great amount of volumes of structured and unstructured data, Hadoop is tough to use. It is new to industries and oodles of experienced people aren’t known to the technology. The reality is that companies that tend to manage Hadoop themselves rapidly scout that carrying out is flat out tough. As per Todd Papaopannou, The chief cloud architect at Yahoo says Hadoop is hard and he and his team learnt the lesson after working on 45000 hadoop servers in organization’s 400,000 node private cloud version. Thus, Hadoop entails huge internal resource to maintain and solve big data problems.
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This application is used to scale up and scale down the application when in demand. Scores of companies fail to consider how rapidly a big data project can nurture and develop. Getting started data scalability tenders the fundamentals issues from a single node to large clusters. To scale up the strategy, you have to understand the causes of database performance deprivation and define database clusters for more and more performance and scalability.
Lack of data knowledge
One requires a lot of understanding to put the data in the right place so as to visualize the data analysis. There is one solution to this challenge is to implement a big data project; a team of experienced developer who should have good knowledge to recognize valuable insights. A depth expertise of where the data comes from, what customers will be consuming the data and how they will construe the information.
Exhibiting significant results
Discover a clear business goal to gather and analyze the data sources to come within a reach of that objective. When you deal with huge number of information or different varieties of information at a time, the analysis become hard and you face difficulty in seeing oodles of data information. The solution to this challenge is to collect the data into a higher-lever, doing that will help you easily visualize the data.
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Higher quality data which guarantees the value of data and the quality standards are not a new concern. No doubt, the emergence of big data has allured the attention of many sectors like education, retail, finance, healthcare and logistics, but to store every piece of data in its original form creating problem. As a result, dirty data arising when companies try to link across various sets. To prevent it from becoming a problem is by maintaining a good relationship with the audience. Organizations can also update their systems to make certain they can handle huge amount of data collection.
Since so many businesses are coming into view, data visualization is turning out to be a main role of analytics in the era of big data. To render a superb way to analyze data, use of high performance visual analytics is required to generate the most value.