Tracking sleep and exercise with our smartwatches, sharing our lives on social media, watching Netflix in the evening, Googling our way through the day: In today’s society the exploding amount of data we produce every day is mind blowing and it can’t be stopped. With the growing popularity of IoT devices, the pace of the increase in data volume is only accelerating and will reach 175 zettabytes by 2025. (For everyone who is confused now: 1 zettabyte equals one billion terabytes. Pretty much, right?).
In business context data is often described as the new oil: A highly valuable resource.
But how valuable is data and how does data create competitive advantage?
In times of Big Data companies much too often reach the point of an information overload. Thus, the central challenge for companies is no longer the availability of data and the collection of data, but data reduction. To make use of data, first Key Intelligence Questions (KIQ) need to be defined, that precisely formulate the current need for information. Then, timely, reliable and relevant data needs to be extracted from the often-massive pool of data that is potentially available and be transformed into actionable knowledge with the help of various analysis methods in order to answer the specific KIQ.
Therefore, the answer to the question on how data creates competitive advantage is simple:
Data by itself doesn’t.
For data to be valuable it first needs to be transformed, like oil needs to be transformed into fuel. Only the transformation of data into actionable knowledge empowers the decision maker to make well-informed decisions in less time, creating a true competitive advantage. This process is the vital challenge for strategic decision-making in companies and is often summed up under the terms business and/or marketing intelligence.
The Data-Information-Knowledge-Wisdom model, or DIKW model, can be used to illustrate the concept of intelligence. In this model, the hierarchical relationship between data, information, knowledge, and wisdom is depicted. The focus relies on the step-by-step transformation of data into knowledge, or intelligence. The state of wisdom, i.e. absolute knowledge, is often excluded in the corporate context, as it is an unattainable ideal state.
Figure 1 DIKW pyramid
DATA
Data is understood to be individual data points that originate from objective as well as subjective data sources and can be both correct and incorrect. In their raw state, the disjointed data points are initially not usable and therefore offer no added value for the company.
INFORMATION
By cleverly linking the existing individual data points high-quality information can be extracted. However, information generated from data has limitations, as it depicts the past or at best the present and does not yet allow any statements to be made about the future.
KNOWLEDGE
The next step in the transformation process aims to combine information into knowledge through analysis and interpretation to use it as basis for decision-making. The developed knowledge is referred to as intelligence and contains in-depth insights into revealed patterns like current trends and developments, which in turn makes it possible to make well-founded decisions concerning future developments.
In this process, on the one hand, the decision-making risk is minimized. On the other hand, the quantity of data is reduced, thus counteracting information overload, since ideally only actionable knowledge is passed on to decision-makers. In summary, intelligence in the context of business and marketing intelligence refers to the preparation and processing of data into information and actionable knowledge in the business environment with the aim of improving the quality of decision-making and thereby generating competitive advantages.
Put shortly: Today, companies do not compete on the amount of data they can collect but on the ability of how well and how fast the collected data is transformed into actionable knowledge based on precisely formulated Key Intelligence Question.
Diconium accompanies its customers along the data transformation process and enables them to get the most out of their corporate data to keep them competitive in an increasingly challenging business environment.