Collecting data and deciphering critical information from it is a trait that has evolved with human civilization. From prehistoric data storage that used tally sticks to the current-day sophisticated technologies of Hadoop and MapReduce, we have come a long way in storing and analyzing data with different types of big data analysis. But how much do we need to innovate and evolve to store this massive, exploding data? And with so many business decisions riding on it, will we be able to overcome all the Big Data challenges and come out successful?
Rise of Big Data and Data Analytics
Today is an age where we use Ethernet hard drives and Helium-filled disks for data storage. But the idea that information can be stored was formulated and put to use centuries before the first computer was even built. Libraries were among the first mass storage areas and were built on massive scales to store the ever-growing data. With time, more targeted devices were invented such as punch cards, magnetic drum memory, and cassettes. The latter half of the 20th century saw huge milestones in the field of data storage. From the first hard disk drive invented by IBM to laser discs, floppy discs, and CD-ROMs, people realized that digital storage was more effective and reliable than paper storage. During all this time experts were lamenting the fact that abundant amounts of information were simply being ignored when they could provide good commercial insights. But it was not until the invention of the internet and the rise of Google that this fact came to be truly appreciated. While data was always present, its velocity and diversity have changed and it was imperative to make use of it.
This abundant data now had a name—Big data and organizations were realizing the value of analyzing it and using it to derive deep business insights which could be used to take immediate decisions.
So what exactly is Big Data? The classic definition of big data is that it is large sets of data that keep increasing in terms of size, complexity, and variability. Analyzing enormous amounts of data could help make business decisions that lead to more efficient operations, higher productivity, improved services, and happier customers.
Big Data Analytics: Challenges And Opportunities
1. Lower costs
Across sectors such as healthcare, retail, production, and manufacturing Big Data solutions are help reducing costs. For example, a survey by McKinsey & Company found that the use of Big Data analytics in the healthcare industry could save up to $450 billion in America. Big data analytics can be used to identify and suggest treatments based on patient demographics, history, symptoms, and other lifestyle choices.
2. New innovations and business opportunities
Analytics gives a lot of insight into trends and customer preferences. Businesses can use these trends to offer new products and services and explore revenue opportunities.
3. Business Proliferation:
Big Data is currently used by organizations for customer retention, product development, and improvement of sales all of which lead to business proliferation and give organizations a competitive advantage. By analyzing social media platforms they can gauge customer response and roll out in-demand products.
But all said and done, how many organizations are able to actually implement Big Data Analytics and gain profits from it? The challenge for organizations that have not yet implemented Big Data into their operations is; how to start. And for those who have already implemented it; how to go about it? Analysts have to come up with infrastructure, logistics, and architectural changes to fit in Big Data and present results in such a way that stakeholders are able to make real-time business decisions.
4. Identifying the Big data to use
Identifying which data to use is key to deciding if your Big Data program will be a success or failure. Data is exploding from all directions. Internally from customer transactions, sales, supply chain, performance data, etc., and external data such as competitive data, data from social media sites, and customer feedback. The trick is to identify which data to get, how to get it, and how to integrate it to make sense and affect business outcomes.
5. Making Big Data Analytics fast
Relevant data needs to be identified quickly to be of value. This requires high processing speeds that can be achieved by installing hardware that can process large amounts of data extremely quickly.
6. Understanding Big data
Your machines are super fast and you have all the required data. But does it make sense to you? Can your management take decisions based on that data? Understanding and interpreting the data is an important parameter of using Big Data and this requires relevant expertise and skilled personnel who understand where the data comes from and how to interpret it.
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Conclusion
To handle the constantly changing variables of Big Data, organizations need to invest in accurate data management techniques that will allow them to choose and use only the information that will yield business benefits. This is where Big Data technologies come into the picture. These advanced technologies such as Hadoop, PIG, HIVE, and MapReduce help extract high-velocity, economically viable data that ultimately deliver value to the organization.
