We all are living in a world that seems to be shifting rather dramatically with the technological advancements in every field. Businesses are more than ever conscious of the changing paradigm and want each and every insight related to their business and industry. They want these insights to help them improve customer experience by providing better service, product, relevant offers and everything in between that can give their business an unprecedented push.
Among everything that has the potential to give such a tremendous insight, Big Data is gaining quite the attention (if not exactly the popularity). However, some implemental challenges present themselves strongly to be overcome despite the evident importance of the collection and analysis of Big Data.
Let’s understand the idea that is Big Data and its various relevant aspects.
Data in general is understood; Big Data is just the extension of that general idea, only much bigger in variety, volume, velocity (the 3V’s popularised by Doug Laney in 2000) in addition to complexity. Analysts also are concerned about the value and variability aspect of Big Data. Every kind of data has some intrinsic value but getting it decoded can be an issue, especially when we are talking about data in unprecedented volume; think in terms of Terabytes, Petabytes and Exabytes.
An inflow of data has always been there in the businesses in an attempt to make it better, both for them and their customers. However, the number of sources from which data is getting generated has gone up quite seriously, both from traditional sources and now from the Artificial Intelligence. Big Data is a cluster of structured (categorised and organised), semi-structured (some part of data is structured and the other is in text doc, email, audio and video format or in any other form) and unstructured.
Big data in itself is not importance but right Big Data analytics tools that derive meaningful insights after much processing make it important. If the insights receives are not in sync with the business goals or vision, then that particular data is of no profitable relevance to the business.
Industries like hospitality, retail, education, banking, manufacturing, government and health care are the ones trying to get most of this technological advancement, which mostly depends on the Big Data analysts. Every industry has its issues to deal with and Big Data is giving them at least some answers if not all.
Meanwhile the businesses are trying to fill in the gaps to better address some of the business issues, the data is getting ever complex. Newer and better Big Data analysis tools are thus coming to the rescue of IT as well as business professionals but the demand of bettering the technology is only going to get escalated!
Below mentioned are some benefits big data can provide when some value gets extracted from it:
Filling gaps in customer service: From taking feedback to implementing strategies that can really help a business grow, especially in the direct customer service businesses, Big Data analytics has helped to remove many roadblocks. And it is continuing to do so.
New product development: Insights from analysing the data reveal layers of customers’ wants and desires, things that even they have never been able to define per say. Businesses are thus equipped with such knowledge to come up with new product to scale up their business and attract more revenue.
Better budget management: Well, that’s primarily about reducing the investment in business wherever possible or manageable without compromising on the satisfaction level of the customers. The Big Data analysis generally ends up allowing business to find more efficient ways to do business (no resultant side effect!).
Enhanced understanding of the market: Big Data is often termed as the future of IT and the industries that are deriving benefits from IT. The same could be stated as true when it comes to trying to gauge upcoming trends in any business by analysing the current scenario; literally seeing the future to be a step ahead from is competition.
Online reputation management: Since Big Data also contains secret to the customer sentiments which are revealed by sentiment analysis, no wonder companies and brands would be better equipped to handle and control online reputation management. Monitoring and improving online reputation have thus become quite convenient as compared to yesteryear.
Time management: Big Data tools are really working around to make life of businesses easier. They are trying to make them see bigger and clearer picture depending on their current business model. Open source platforms that have come up to serve them include names like Hadoop and Spark, which work toward making learning-based decision quick and accurate.
Since the Big Data is…Big, it is bound to pose some challenges and people who have just started to allow the idea of Big Data to sink in got to have some concerns lurking inside their minds. Here’s what are the most common among these:
Value evaluation of the data: Since the data is coming from all around, no wonder most of that remains untouched since its intrinsic value still is unrevealed. Such data is just getting accumulated without delivering any outcome; hence a waste of resource is likely to occur.
Data storage and analysis: Big Data could not be worked upon with traditional ways like historical analysis. As 80% of the data is unstructured, the challenge is even more daunting. When the amount of data being generated is in Petabytes, archiving and retrieving the data when needed becomes next to impossible, even with the latest tools and technology.
Data curation and preparation: Data on its own means nothing. It needs cleaning, curating and preparation before it could be used by analytical tools and that along takes 50-80% of the time required by the data scientists.
Cost involvement: We have talked about the sophisticates skills and tools needed to store, curate and derive meaningful insights from the data but this comes at a cost which could not still be accessed by most small and medium sized businesses. Even with the technology getting cheaper by the days, SMEs are not easily convinced to at least take a look if they can invest in it or not.
Data misuse: With data collection comes the concern of data misuse. More and more technologies are demanding data feed and customers are not really comfortable in doing so but do to get the service/ product they need. And as a possibility, data misuse cannot be denied leaving businesses with the responsibility to protect the data of their customers. Understanding and handling the Big Data in any industry is still in evolution phase presenting them with newer opportunities and challenges but never in the history of mankind human beings were so empowered. Big Data analysis is the future of tremendous potential. Staying with it is the only course of action we can take!
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