September 26, 2019 Big Data and The Challenge And Opportunity for Modern Businesses
By Tom Wilde
Amidst the uprising of modern technology and the rapid increase in digital data consumption, companies have become increasingly reliant on not only marketing themselves online but also have a never-before-seen degree of exposure to information and an opportunity for potential business growth. Naturally, this also implies that all of your competitors are aware of the pitfalls and have access to the opportunities as well. The world of business and capital growth has become a tighter than ever race where the leaders hungrily seek out the latest and most advanced resources to keep them ahead of the game. Big data has become the buzz term that heads of companies have come to understand as the tsunami that is either on their side or the face of what may well crumble them in the near future. So where does this leave the modern business, considering that we’re fast approaching the precipice of a world entirely operated by and controlled through data in all its various forms? The answer is not unlike what happened back when typewriters were replaced by computers and businesses had to quickly convert whatever printed and written information they had into a digital format. Today, however, it’s a far more complex and yet powerful transition. Companies accrue data in various forms and from multiple sources, which is all stored in data warehouses. Generally, this information isn’t processed in real-time and, a lot of backlog can occur. This also means that a lot of the data accrued never translates into usable information or statistics to indicate pitfalls and opportunities for growth. Now that companies operate mostly online and all information, leads, reports, research and other necessary elements of operation are digital, this data moves a lot quicker and needs to be analysed and translated even more expediently to make timely decisions. The challenge – and opportunity – of big data is in mining all of this information, while also doing market research in real-time, and producing statistical reports and solutions to optimise the operation of the company and identify tactical ways to spearhead new developments in that particular industry.
Challenges of Big Data
This highly competitive evolution brings with it its own challenges, in that large corporates with ample capital are more capable of seeking out and employing highly qualified specialists to aid them in navigating this new ocean. Data scientists are extremely educated and have a broad range of experience in various new data developments as well as being adept in research and business operations management. They are not easy to find and, as a result, come at a high price. While the cream of the crop gets picked out by the titans of industry, what remains for the other companies hoping to compete is the opportunity to accelerate the education of younger, talented data engineers and seasoned SQL data scientists. In light of this huge demand for a very, very new specialist, companies have begun to develop machine learning solutions which they sell as ready-made packages. While these types of software solutions are useful to gain a foothold in big data, they cannot quite compare to a team of expert data scientists as utilised by large corporations. Meanwhile, the data of every person who has ever been online is steadily being collected and sold off to aid companies in developing better marketing tactics and improve targeting for future sales. For the average company that doesn’t fall within the echelons of Fortune 500, their best move to keep up with the times is to invest in data analytics and do as much research as they possibly can to improve their offerings. Whether there will be a balancing point in future, considering global user privacy laws being called for and the enforcement of restrictions through new legislation catering for a digital era, remains to be seen. To get a better understanding of data and data science, check out our previous blog post on Business Intelligence and Data Science.