Big Data has no bias, agenda or opinion. Yet even while most organizations realize the significance of big data, very few are truly seeing the full impact of it on their businesses. A recent study by investment bank Goldman Sachs revealed that only 25% of businesses currently understand the full impact of analytics in their decision making process. A new report by financial firm Morgan Stanley makes the same claim, that even if the trend continues at the current pace, at least 70% of companies will not fully maximize the potential of big data analytics.
Analytics, in general, can benefit all industries. However, data analytics specifically can improve the quality and quantity of all industries’ products and services. At present, only a few industries that make use of data analytics are those in the consumer and retail sector. But in time, perhaps other industries will take advantage of the potential benefits of big data analytics.
Analytics, of course, cannot be limited to the major verticals such as retail, consumer, and wholesale. Data analytics can also provide insights for organizations that are vertically integrated, i.e., those that are involved with multiple vertical markets. This broader perspective provides even more opportunity for big data insights.
Today’s marketers, both large and small, face a host of challenges. The current climate has changed dramatically, making it necessary for organizations to evolve with the times. Organizations need to establish a strong strategic mindset that is willing to embrace change. As the organization becomes more technologically capable of collecting and utilizing big data, it must also develop strategic ways to aggregate and manage that data. These are just the beginning of many challenges organizations will face as they evolve. However, by adopting different types of tools, a company can take advantage of emerging opportunities and at the same time, manage its relationships with customers.
Big data analytics can help achieve these goals by providing insights that organizations can use to address various challenges. One of the primary ways to analyze big data is to build and utilize an appropriate predictive technology platform. This platform must have the ability to provide predictive insight on changes in consumer preferences over time. The platform must also be able to provide insights on competitor activities and market trends. Such a platform can significantly enhance marketing strategies and aid business leaders in realizing their strategic goals.
Another advantage is that big data analytics provides a ready supply of ready information. This can be leveraged to support tactical decisions and actions. For example, data collection via social media platforms provides ready information on how people use these platforms to plan their buying activities and the activities they perform when they do these activities. Thus, data collection on Twitter and Facebook can be leveraged to monitor and track the behavior of customers and users of these platforms to determine which ads and promotional campaigns work better. Similarly, data collection via online surveys and questionnaires can be leveraged to understand customer’s sentiment regarding particular products.
The third advantage lies in the ability to evaluate and measure big data insights. Various types of analytic insights come with varying degrees of relative precision and may not always be applicable to all situations. Hence, a company may not be able to make sense of important data-driven insights unless it has analyzed and measured its relevance. This ability to measure means that companies need to have measures in place to ensure that collected insights are meaningful and useful.
Lastly, big data analytics presents unstructured data in structured forms. Traditional data mining requires a company to sift through large volumes of unstructured data in order to extract relevant details. However, unstructured mediums such as social media have massive amounts of data and the task of sorting through unprocessed data to extract usable insights is too vast for most companies. On the other hand, a traditional data mining technique such as mining requires a company to take on the costly process of collecting and processing raw data in order to make use of the insights it brings.