Data collection is a consolidated practice in our information society, where through websites, blogs, social networks or information systems, considerable volumes of data are accumulated. However in their raw state they bring little value. Why? Because the missing next step: Turning data into knowledge.
The digitization of organizations brought the mechanisms for information gathering, which in general are consolidated, however the work of analyzing and shaping this data in order to produce relevant business information, is something that is done at the level of large international organizations, often on massive scales as the case of Big Data, however at smaller companies these areas are approached in a generic way and without significant results.
Why isn’t more involvement in this area?
Analyzing the reason for the lack of involvement in this area, we reach a combination of three main factors:
In-depth business knowledge is essential to ensure its performance, however data-driven management methodologies are not yet fully recognized as a way to obtain this level of knowledge. This is above all a matter of perception, which needs to be mitigated.
Lack of know-how
The perception of added value already exists, but the processes are not placed into practice due to lack of experience or knowledge. This scenario is where specialized partnerships have a determining role, by demonstrating the added value of producing knowledge from information.
The fear of excessive costs or not obtaining a return on investment. This is mainly due to the outdated knowledge of the currently available solutions, since the democratization of technology has brought robust and effective solutions at very competitive costs.
So how we’re turning data into knowledge
Adjusting the solution according to complexity and size of the target organization, it’s possible to implement solutions with competitive costs, that through the existing information systems can produce outputs capable of generating new knowledge of your business, covering the desired areas in order to improve performance, effectiveness and efficiency of the organization.
This process of turning data into knowledge is divided into the following phases:
Data integration and processing
Usually described as ETL, the basic data preparation process is where the different data sources are normalized, corrected and formatted to allow their integration and aggregation.
Data analysis and modelling in order to produce information
IT aims to identify patterns and produce metrics based on data analysis and modeling, to discover new information or to deliver previously identified business information requirements, usually defined in the form of key performance indicators.
Usually the business already has a series of questions to which it wants answers, such as:
“how long does it take to produce and deliver a certain product” or “what is the average visit time to our site“, since these processes should start from the base business need to obtain relevant information for decision-making, however it’s important to emphasize that the discovery of new information from data analysis will always be an important point in this stage.
The way of representing a set of information affects the ability to draw conclusions, regardless of the what information is displayed.
A good example of this topic is the adoption of the new risk matrix to the Covid-19 pandemic, which was started out in Portugal during March this year, where through a matrix with two axes and specific coloring, greatly improved the ability to quickly understand the level of pandemic risk per region or at national level.
From the information interpretation and analysis, a new level of knowledge is obtained that potentially supports decision-making. However all steps should be reviewed in order to validate the conclusions obtained. Therefore the refining of the previous steps of this process as well as the continuous analysis of the data will produce additional information, refining this process of knowledge acquisition.
This process when based on consolidated information collection practices, is a small step towards a new dimension of knowledge, which as mentioned, already exists through the data present in business systems, in site analytics or in digital interactions. The effort to transform this data into knowledge is minimal and the advantages are obvious, especially when one considers that they are already present and are not being used.
If you wish to better understand how to use your business data, or want more information about these processes, reach us through the available channels.