“Know yourself and know your enemy. You win all battles” – so states Sun Tzu in his Art of War. Other prominent leaders have also stressed the importance of good information. Knowing and understanding your business is essential for good decision making which has a direct impact on its performance.
One could argue that many businesses and organizations actually fail to follow this path. Yes, definitely gigantic amount of data is collected and stored. This data covers the organizations themselves, their clients, the market and competition. However, having the data and knowing are two separate dimensions. Data provides insights into problems and allow discovering solutions to them. In order to understand what the data tells us, we must employ methods and tools to analyse and transform it into information. This is what data intelligence is about: Intelligent Data Analytics is a interdisciplinary study dealing with the extraction of useful knowledge from data. Those extraction methods benefit from statistics, patterns recognition and other fields, including the rapidly evolving Artificial Intelligence.
When talking about Intelligent Data Analytics it is important to understand that this term covers a variety of data related aspects. It refers to analysis, classification, extraction, organization and finally method to transform it into useful knowledge. In general, those can be grouped into three or four stages: preparation, mining, validation and explanation (some researchers combine the last two into one). Each of those steps is important and all of them are performed in a sequence. While the aim of intelligent data analysis is to obtain knowledge, it is not always straightforward to choose the methods to tackle the density of the process.
Data preparation step is focused on integrating required data into a regular form of a dataset. This in turn will be utilized as the basis of data mining. In simple words this second step is about examining the gathered databases to spot and generate new information and identify patterns. The data is manipulated in search for explanations or predictions. In the third step which is validation the researchers will verify the results produced by data mining. The end result of an Intelligent Data Analysis process are insights coming from the original data. This is produced as an outcome of data explanation stage where results of previous steps are intuitively communicated, hopefully providing a solution to the business problem that triggered the process in the first place.
The ultimate goal of all data analytics processes is to be able to make well educated decisions that are good for the business and the organization. When “double clicking” the data for insights, we do not aim to find information that is appealing and serves our narrative. Instead, we are focused on making sense of it, understand what it means and produce useable input for the decision-making processes. It is not the data nor the information in itself that is of use to us. It is the ability to use it further to make conscious, educated decisions that will allow the business to grow and organization to develop.
It is important to note, that while intelligent data analytics may seem a very sophisticated field, data analysis does not necessarily need to rely only on quantitative methods such as arithmetic or statistics. In fact, it is observed that much of data analysis that employees in various organizations perform are handled with methods no more complicated than calculating a mean. What is essential in every analysis is being able to compare (for example values or patters). This activity is performed simply by looking at it with nothing else than our eyes. Visualization also referred to as charting helps with this task. In accordance with the name of the idea, the data is presented in some visual form in order for the user to be able to see and identify areas where the data could be examined more for information. Visual data analysis techniques bring a great deal lot of value in exploratory data analysis.
When thinking about Intelligent Data Analytics, we should consider two factors: on one the amount of data stored by organization is increasing exponentially and becomes overwhelming. On the other hand, Artificial Intelligence and Machine Learning becoming more and more available. Leveraging from the latter organizations start to use the state of art tools to analyse huge volumes of data, which with traditional, human-centred, methods would require too much time and money to bear.
We at Brickendon are happy to support your organization in the planning as well as implementation of projects aimed at data analysis. Development and integration of technologies and know-how in the field of data analysis is always accompanied by training of the employees. We are always open to consultations to help you unlock the potential of data and shape strategies for its use. In the end, it is not the data itself, but the ability to grasp the knowledge within it, that will drive your business success.
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- J. Shi, S. Zeng, X. Meng, “Intelligent data analytics is here to change engineering management”, Higher Education Press, 2017