THERE is no doubt the world is growing more competitive. Nations and businesses compete. But, the criteria for competitiveness have changed. Notwithstanding, marketing remains a critical success factor.
In the past, only big companies with financial muscle enjoyed better market access through investment in strategic marketing. Smaller businesses often had difficulty allocating enough resources to support marketing. Marketing was not cheap. It is different these days. With the Internet and e-commerce, there are less expensive tools to reach out to targeted customers. The Internet technology has, in a way, empowered smaller companies to compete.
The difference, instead, lies in how best one can follow changes in the market place. Businesses that are good at predicting constantly changing consumer preferences and always-evolving technologies in the market have an edge over competitors. The same applies to institutions and even governments. Those that can read and analyse the massive amount of information out there will be the ones developing the right strategies to move their agenda. And with the Internet and other supporting networks, many avenues are available to collect the information we need.
Add to that the proactive surveys and measurements undertaken by various parties, and we have to deal with increasingly complex dimensions of information. We need to constantly deal with what is termed “big data” in decision-making. This is what big-data analytics is all about.
We have to accept the fact that the skills to analyse big data are increasingly important in today’s world. What exactly is big-data analytics? In simple terms, it is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Such data could include Internet data, social media content, social network activity reports, customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the “Internet of Things”. Many such data cannot be tapped by conventional business intelligence.
The findings from data analytics allow for better marketing and business decisions. They can lead to many business benefits, including new income opportunities, better customer service, improved operational efficiency and advantages over rival organisations. Big data can be analysed with commonly used software tools, such as data mining, analysis of texts and statistical analysis. But, semi-structured and unstructured data may not fit well in traditional data warehouses. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or continually. For example, real-time data on the performance of mobile applications, or oil and gas pipelines. As a result, many have turned to a newer class of technologies.
Admittedly, there are potential difficulties for organisations dealing with big-data analytics. These include a lack of internal analytics skills and the high cost of hiring experienced professionals. The amount of information typically involved can cause data-management headaches, including issues of data quality and consistency.
There is no doubt that the growth of big data will present challenges as well as opportunities for organisations in the coming years. With growing data volumes, it is essential that real-time information of use to the business can be extracted and analysed. Competitors that use data to deliver better insights to decision-makers stand a better chance of thriving in the difficult economy. The aim is to be able to use real-time data for real-time decision-making to become a real-time business.
There are now companies that collect and analyse data for better decision-making by clients. In the oil palm plantation business, one start-up company uses the Unmanned Aviation Vehicle to collect and analyse data on fertiliser run-off to help companies save on costs through a more precise mechanism for fertiliser application. A project to develop a reliable early-warning system for disaster risk reduction is also looking to big-data analytics for support.
The list is growing. Dealing with big data is set to assume more prominence in the coming years. The message is: “Ignore big data at your peril.”
The writer is a fellow at Academy of Sciences Malaysia and Universiti Malaya adjunct professor