The world has been told that the future is in big data. A big fuss has been made and steps taken to ensure its future in our technology-focused world. The word first came around in a special research report published by McKinsey and IBM around May 2011.
McKinsey lay it out quite daringly, proclaiming that “leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers” as well as how big data will become the central focus in many economies, propelling ahead of and defeating the competition. McKinsey was one of the pioneers in harnessing and putting a name to big data, he goes on to explain that “the amount of data in our world has been exploding […] The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.”
Today, after almost a decade after McKinsey’s projections, only 26.8% of firms that participated in NewVantage Partners 2020 Big Data and Executive Survey, reported having any kind of data aware culture. Another 37.8% reports that they are driven by data in some way or another. Only 45.1% confirms that they actively compete and use data analytics in their business. This is an underwhelming reaction to something so revolutionary that was out to change the world 9 years ago. The naming of Big Data went on to inspire companies to invest hundreds of millions in the technology in an attempt to break it down and translate it into information gold. Despite numerous attempts at tapping into the wreath of information, technology has barely scratched the surface of the true potential of big data.
One of these investors, Accel Partner, commented that “we are seeing an accelerated rate of innovation in big data, with the newest generation of entrepreneurs re-imagining ways to extract the most value out of big data and fundamentally change the way we work and process information”, they are also convinced that instead of three Vs of big data which is variety, volume, and velocity, there is a fourth variable that hasn’t yet been recognized but is highly crucial to the success of big data implementation: end user value.
With this in mind, organizations which have adopted big data initiatives has tried to implement it in targeted marketing with obvious successes, but again, it’s only just the beginning and there is much more that big data has to offer than to engage customers with another video that they’d like to see, or a product that they’d like to buy, or a service that they’d employ at a particular time of the year. Target ads have become prevalent in today’s digital landscape and big data has promised to elevate it to the next level. Unfortunately, that level has not yet been established and due in part to the lack of manpower needed in order to transform that level of data into something that’s usable. Upon clearer investigation, one might come across a key sentence in the McKinsey report stating that his predictions would come true but only if “the right policies and enablers are in place”.
This has caused another uproar in the technological sector, when A.I. was introduced to act as the buffer between big data and translating it into usable information. Artificial intelligence is another industry that’s being saturated by promises and the idea of how it can be used to solve many of society’s problems. Big money is being poured into a technological entity that is expected to do everything, from helping you rank in Google by offering the best advice an seo consultant would give to completely changing the way that people are using social media and manipulating them to either use their phones less as highlighted on social issues currently faced generationally, to manipulating users to use their phones more to keep them engaged and buy something. Ideally, it would make you use your phone less and buy more.
The shortcomings of big data is reflected in how it isn’t user-friendly and cannot easily be interpreted for companies to benefit off. But rather, it requires large volumes of data and even large volumes of processing power which doesn’t yet exist in order to dilute the information into easily digestible tid-bits.
End consumers of BI reports, analyses, data sets, and other data-driven products have questions that they regularly ask: Where did the data come from? How has it been aggregated and transformed? Who has used it? What is the quality of this data? How trustworthy is it?
If users do not receive credible answers, they will not trust and consume the data and BI products. Hence, it behooves data management and analytics professionals to put data lineage solutions in place that can accurately answer these and other questions about data origins, history, transformations, use, condition, and trustworthiness. You need data lineage tool functionality so you have accurate information about data available quickly when you are questioned by users, developers, auditors, governors, and managers.
Companies understand that gathering data is important. But they often neglect to do anything with it. The essence of a good omnichannel strategy is making your data actionable to improve your customer experience. Unless you can connect your data across teams and platforms, much of it is useless.
The University of Texas found that increasing data’s usability by only 10% would, on average, boost revenue by $2 billion annually.
Big data gives you a leg up on customer behavior and preferences. It also uncovers your own team’s strengths and weaknesses to help you boost your strategy.
It should be no surprise that collecting and studying customer data will reveal things about your customer base and behaviors. Surprisingly, most companies don’t do this well. Only 8% of retailers say they have a holistic view of their customer base.
Companies that don’t have a grasp on their customer base are spinning their wheels when it comes to improving customer experience. You make improvements based on intuition, but you don’t have a firm grasp on what your customers actually want.
The more data a company collects and analyzes effectively, the better are its chances to stay ahead of the competition and deliver state-of-the-art products and services.
You can easily see Big Data analytics in action when you are watching your favorite video, checking your social media feed, or even selecting an insurance plan. There are millions of videos on YouTube and you can check how many people have viewed them over the years. Similarly, you can keep a track of the number of likes that you get on your posts, how many people follow you on Twitter, the number of downloads of any mobile application, reviews regarding any product on e-commerce sites, and so much more. Companies are collecting this massive amount of data and trying to better understand customer needs and achieve increased customer satisfaction through their offerings.
Different industrial sectors are using Big Data to offer sophisticated business solutions for their customers. Manufacturing, education, healthcare, stock markets, aviation, and transportation lead in harnessing the power of Big Data. These sectors offer various job opportunities in the field of Big Data like Big Data engineer, Hadoop developer, data analyst, machine learning engineer, and business intelligence analyst.
The powerful combination of AI and machine learning is being used by companies to transform the unwieldy Big Data into an approachable stack. This would enable businesses to witness the algorithmic magic through applications like pattern recognition, fraud detection, video analytics, dynamic pricing, and more. Analytics-driven organizations are also leveraging AI to enhance data quality.
The IT industry is now highly interested in fragmented, widely distributed data structures created by incorrectly formatted data. This is the reason the number of databases for a wide variety of data types has increased significantly over the years to promote the meaningful synthesis of data. The combination of data synthesis and data analysis will further promote the effective usage of data.
According to Forbes, the implementation of big data will take multiple years with many missteps and failures because only in failure do we grow to become more effective, efficient, and innovative. It’s a complex web that’s being influenced by all factors, external and internal. How the end-consumer continues to consume content and data, whether the economy will stay consumer-based, and if this industry will continue to stay relevant. It all takes time and practice before it can reach its final form of a mass-marketing monster. The key problem is that we all expected too much, too soon, based on a dream that would only take hold many years down the road. Think about Tesla, or any scientists in an olden age, many lived with their brains in the future and simply lack the technologies to bring their ideas to life. It is the same case with big data, but it soon wouldn’t be.
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Source: https://datafloq.com/read/big-datas-shortcomings/10958
