History is replete with examples of popular companies losing their edge due to improper, inadequate analysis of the market, current trends, and the competition. Consider, the American tire company Firestone for instance, which was once upon a time the leading maker of traditional bias tires. Firestone lost its glory when Michelin came up with radial tires – which were safer, long-lasting and more economical than bias tires- that later went on to dominate the US market. Although Firestone had predicted that radial tires will be widely accepted by the automakers and consumers in the US, it couldn’t do much to outdo its new competitor. It invested several million dollars in the production of radial tires but clung to its old ways of working. Rather than redesigning its production processes, it just tinkered with them. The end result? Radial tires of an inferior quality that kept piling up in their warehouses, plants running at an anemic 59 % capacity and tremendous loss of money and resources. In the end, all of Firestone’s intense analysis and action were for naught and the company was ultimately acquired by Bridgestone from Japan. Lack of proper insight on current market trends and improper competition analysis led to Firestone’s downfall.
The famous camera company Kodak too missed the rise of digital technologies. It couldn’t keep up with the changing times – its cameras missed essential features like connectivity with file sharing apps and printers. Its stock price went painfully low – as much as 96 percent below the peak it had once hit.
Today, although a humongous amount of data is available, there isn’t much being done to leverage it. For data analysis and interpretation, businesses rely on dashboards and BI tools – which are inadequate given that they analyze data at a superficial level and tend to miss out on certain inconspicuous observations. To add to it, dashboard interpretation is time-consuming and requires technical expertise. Also, such manual efforts are biased, erroneous, and incomplete, in most cases. This results in missed insights, lost opportunities, lost dollars, misallocation of resources, and a negative impact on the reputation of the organization.
Enter Artificial Intelligence (AI). Natural Language Generation (NLG) – a branch of AI – is used by innovative BI solutions like Phrazor by vPhrase to generate crisp, coherent, and complete summaries from data within seconds. Such a solution dives deep into the massive heap of available data to extract insights most relevant to the organization. The results are presented in the form of Intelligent Narratives coupled with graphical elements and statistical summary.
Consider the snapshot below:
This is taken from the financial statement analysis of a leading bank. As we can see, the graph is supplemented with easy-to-understand narratives that give a clearer picture of the bank’s financial health in no time.
Platforms like these help better understand the context, perform predictive analytics, and also provide recommendations. These aid in faster decision-making and optimal resource allocation, while significantly boosting organizational productivity and cutting downtime and costs.
Companies today know the importance of understanding the context of the prose in their reports and knowing the subtleties involved in data analysis – and also the fact that this job is better automated with modern BI tools that are gaining popularity each passing day.