Surely you are wondering what the meaning of the word is insight, so first of all we will explain what an insight is and when this term is used both in Big Data and in digital marketing. Do not miss the opportunity and get your training in big data.
What is an insight?
Like most of the terms used in the context of Big Data, it is of English origin and we can find several meanings for it. According to a first definition, we could say that it is like a internal vision of something, its perception or understanding. So an insight is the ability to understand and understand in depth something that is part of a problem or a complex situation.
Other definition of insight is “see with the mind’s eye”but it would be a vision based on knowledge, understanding how the eyes of the data expert are capable of seeing the hidden keys in the data, behavior patterns, trends and details that can manifest behaviors with anomalies.
On the other hand, the word insight, as we have already commented, is one of the most used terms in the context of digital marketing and it is the key that allows us to find the solution to a problem. A concrete piece of information that suggests how to solve this complex situation, that is, it will take us on the path to find the solution to the problem.
Insights are used in the world of content marketing to understand the consumer and get to know them better in order to offer them products that meet their needs. An insight, in marketingis obtained after a deeper investigation of the brand and the consumer and that allows us to better understand how to connect properly with all of them.
Insights in Big Data
At this point, the next question that comes to mind is What is the relationship between insights and Big Data? Well, it will be Big Data who will help us obtain these insights that we will later use in digital marketing.
Big Data combines a series of techniques and each of them specialized in one of the data processing processes. Some of these techniques are Artificial intelligence and specifically the techniques of Machine Learning They are the ones that will intervene in this process since they allow applying the vision of deep understanding that defines an “insight” in large data sets whose volume is so large that it makes it impossible to treat them manually. With these techniques we will be able to compare, order, put the data into context and turn it into insights or keys that can be translated into concrete actions on which we will base our business strategy.
The business strategy will be based on the “insights” that we have obtained through the application of techniques considered in Big Data.
Monetize insights with Big Data
The current situation is that some companies seem dissatisfied with their Big Data capabilities and continue to increase the budget and investment with new tools, new software and capacities that generate data considered as assets of value for the business when they would have enough with the Big Data technologies available to them if they make the insights profitable.
If we make the generation of these insights profitable, we will have this key information that the business needs to guide its strategy.
Let’s look at some ways to monetize insights.
- Determine what is actionable. We must have a clear concept of what is actionable for the business so as not to waste time and resources exploring data that will not be useful. You will start by defining the problem before looking for ideas to improve productivity.
- Simplify the process by focusing on decisions. It is recommended to simplify the search for insights and actionable ideas by focusing on the important decisions of stakeholders.
- Take into account the stored data. 90% of data will never be used, according to a Gartner prediction. This excess storage creates a danger in losing a level of discipline about what should be collected, what not, and why. As in any data warehouse we must take into account: What do I need to know? What data do I need to answer this question? How can you put in a store available that is fast enough and query capable enough for the team?
- Have human resources. The gathering of insights is not an automated process yet, but it is still necessary to use intelligent humans, as well as real data; Automation is still buggy and can end up creating more problems than it solves if there are no humans in the process.
- Understand consumer and business needs. Before looking for data-based information we will have to talk to those who use the product or service to get qualitative insights.
- Data balance. We must use high-quality data to find useful information. Which means not getting lost in a quantitative data warehouse when sometimes it’s better to talk to customers to get more nuanced and qualitative information.
With some frequency it will be necessary to clean the central data warehouse on data (Data Warehouse) that no longer provide any value.