In such a period where big data and business analytics is on the agenda, there is a fact that is ignored. The data produced and analyzed cannot create the expected effect if it is not supported by “an accurate story”. It can be understood from the terminology used by several experts who succeed in business analytics: “Tell the story with data”. The word “story” will be mentioned frequently in this article. Therefore, I would like to clarify the meaning behind this word to prevent a possible misunderstanding: Here, story refers to obtaining implications from the findings at the end of the effort, which move along from hypotheses to models, from models to analyses, and from analyses to findings, and transferring these implications by interpreting them in a result-oriented manner to the persons who will benefit from such implications. In fact, one may consider that a person who performs data analysis should already be expected to interpret the analysis process itself and the results obtained. However, it is not a practice which is as common as thought. This, on the other hand, is not something that begins with the involvement of business analytics and big data in our corporate life. This is already an ongoing habit for decades in a considerable amount of reports produced in the companies. Moreover, it is one of the black holes in relation to performance management in business life. We nearly expect the figures to express themselves to convince us. On top of that we are increasingly moving away from “telling the story,” in a sense; along with the empires established on business analytics and data. Why do we have to enrich our fondness for data with our appetite to interpret it? Here are five good reasons for this: • Stories have always been an effective method to convey the human experience in communities. It does not matter if they include data and analysesregardless of how detailed they are. Consider them just as the new and up-to-date versions of past experiences. Stories serve as the tools that make a complex world simple to be comprehended. They provide intuition, perception, and interpretation. These make the data more meaningful as well as making business analytics much more comprehensible and interesting. In addition, this is the only possible way that reasoning can be engaged in this process. As such, dialogue and communication to be established with the counter party are built upon a more solid foundation. • When it comes to analytics, our aim is to help others, changing their way of decision making and taking actions. We endeavor to convince, establish trust, and trigger the change with these strong tools that we have. No matter how impressive your analysis or how quality your data is, you cannot reach a conclusion without ensuring that your addressees comprehend your work. Your analysis needs to be supported and completed by a story, whether it be visual or audial. There is no doubt that people who read such reports and evaluate relevant figures are deeply intelligent (e.g. shareholders, senior executives, etc.). However, we need to bear in mind that their time is much more precious from the perspective of the company, no matter how important our time is. Moreover, these people who are engaged in several issues at the same time need to be provided with such works in the most effective and efficient manner. Figures without interpretation are like giving someone pills without water. • Many people may not comprehend and internalize the details of business analytics as well as you can. They, on the other hand, may want to have evidence about analysis and data. Under such circumstances, stories including data and analytics will be more convincing instead of the ones built upon personal experiences and anecdotes. In fact, digitizing an argument would be a highly proper step as numbers make it easier to understand some issues. In other words, data itself does not matter unless we process it to obtain information which, then, needs to be turned to smart information. • Data preparation and analysis often take time and require effort; however, brief and metodosimple descriptions need to be included for the ones who will benefit from the data. Big data may sometimes become too big to be managed while it provides companies with several opportunities. Conveying every detail of a numerical work to the shareholders takes too much time as well as being a tedious effort for both sides. Analysts need to find shorter and more remarkable methods to present prominent findings, for which stories based on well-chosen proof and interpretations are substantially suitable. • Another important point is how many stories can be told through the obtained results to decision makers. Experiences show that this partly depends on the storyteller. Analyses can be explained and used in different ways. There may be tens of associations, considerations, and implications that can be made through the analysis. However, it should be noted that expressing already apparent points is superficial, and consequently inadequate (e.g. “Here the situation is; this value increased/decreased by this/that etc.). We need to tell what is more than the apparent, in a different way from any other person. Analysts diversify their capabilities and styles as long as they gain experience in this matter. Sort of developing their repertoire... They may even take much more pleasure in what they do. Why are many analysts still unsuccessful in setting and conveying stories, even though we outline the benefits of having a story under five main topics and highlight its importance persistently? Furthermore, the results are not negligible. This means that the reliable effect and contribution required to be on decisions and actions cannot be created. This means that the time and money spent to obtain, manage, and analyze the data go to waste. Now let’s look into the reasons for this unwillingness and/or habit: First of all, professionals of business analytics, though exceptions, mostly do not like a lifestyle based on written-oral communication. Sometimes they cannot succeed in this matter even if they aspire. Their academic formation does not encourage this much. Such experts who lean towards structural, well-defined, steady fields such as mathematics, statistics, and computer engineering during their school years may enjoy the interaction with numbers more than their interaction with people throughout their entire career. Of course, it would not be fair to see all numerical analysts from this perspective. Nevertheless, we cannot ignore the effect of their academic (and even personal) formation. If they do not tend to go beyond the numbers, this is possibly because that they were not much educated on this matter during their school years. In addition, curriculum and instructors do not approach this matter that much. After all, options such as going through a story may be considered as an obstacle and waste of time by the academicians of such discipline while there exist many methodologic approaches. On the other hand, we need to say that such preference may not generate accurate results. According to a recent research, communication is the prior capability recognized by the executives who want to employ the graduates of such departments in their companies. Some analysts think the time to be spent for telling and interpreting stories is nothing but disregard their value and misuse their precious time, considering their technical capacity and skills. They usually argue (justifiably) that many people may be good at telling stories but very few can create and operate regression models including unstable variance modifications as capable analysts do. While it makes sense to some extent that they believe the best option for all parties is to direct their brain cells and time to numerical analyses, and interpretation, if absolutely required, can be made by someone else, an operation where the numerical results need to be translated by others will be excessively effortful and high-cost for such a process. Look after your own work! Don’t let anyone else explain an analysis which is made by you? On the other hand, there also exist the advocates of the opposite view. Those who consider both as different disciplines being performed by separate persons will bring better results in terms of quality, efficiency and effectiveness. It may even lead to a new occupation: Analytics translator... At the first stage, an equation, in the simplest term, is made but this equation needs to be solved at some point which refers to the stage associated with how complex business problems can be solved. For example, do we have an analysis of campaign efficiency? The findings will provide us with the hints with which we need to make interpretations in regard to past and future. Here comes the basic question: Should the same individual do them both? The last factor is the question of how much creativity, effort and time will be required to interpret the results efficiently. Researches show that analysts performing both spend nearly half of their time for the sake of communication which they build to convey their work. Many experts, naturally, are not eager to spend that much time for this even if the effect they will create is much more. However, doing this will make them professionals who understand their company, industry, and market where they strive for better. This will open the door to building models more accurately in the future. We are living in a digital era. We have information coming out of the taps! The thing is whether we are ready to benefit from the information sufficiently and appropriately. Time will tell ...