The world has become digitized, as has the business environment. And within this context, the most successful companies are those that analyze existing information to develop and optimize their business areas. In other words, today, to compete and prevail, companies need excellent data management and feed on their analysis.
But this task is not easy because three main factors can hinder this process: the reliability of the data, the algorithm or software used to process that data, and the system used to collect or analyze the data. That is why enterprise data literacy is essential for the company of today and the future.
According to the Massachusetts Institute of Technology (MIT), data literacy is the ability to read, analyze, argue, and work with data. In detail, reading refers to understanding what data is and what aspect of the world it represents; working with data includes creating, acquiring, purging, and managing it; analyzing the data refers to filtering it, categorizing it, adding information to it, comparing it and carrying out another series of operations; argue with the data, which translates into using that information to support a narrative whose intention is to communicate a message or story to a specific audience.
An example of the complexity of this skill is collected in a survey released by MIT itself, which indicates that, within a universe of more than 9,000 employees in different roles, only 21% of them were confident about their handling of data literacy.
Another factor that organizations face when it comes to data management and its consequent communication is that good data management requires knowledge of skills that are not usually taught together. For example, the graphical understanding of the amount of data that currently converges is not something that can be assumed to have been part of the academic training of a company’s employees.
But this complexity is a barrier that organizations must overcome and permeate towards their employees since those who are “literate” in data management with data transformation tools make better decisions. Today companies require decision-makers to act faster than ever before.
One of the significant challenges facing business leaders today is data literacy. This challenge prevents them from flourishing in their core business and prevents them from carrying out a cultural change in data management within their organizations.
It is essential to know the new ABCs to achieve business data literacy. These are Awareness, Bias, and Callousness. Next, each of them in detail.
Within the management of data for the development of companies, information is precisely the fundamental element, the fuel on which the entire process must be built. Inadequate data, out of the appropriate context for the business, or out of date, for example, will damage the data analysis process before it even begins.
So that this does not happen, the data collection is the part of the process to which more time must be dedicated; 80% of it is to gather the right information and make sure it’s correct, permissible, and unbiased.
Cultivating high-quality data depends on the context in which it operates, that is, the data literacy culture that exists in the company. For this reason, it is essential to cultivate business intelligence based on data within organizations, which goes through education processes that must be given behind closed doors.
While spending 80% of your time collecting information and then applying it to your business model is not new, the criteria for using that information and the standard that data must meet are complicated and changing.
Companies must formalize the standards of their governance models and enforce them before including data in a project because the use of this information from users is no longer free of restrictions. Likewise, they must comply with the regulations related to the client’s consent regarding using their knowledge. The client now has the power to be “forgotten” or that their data be removed for future business models.
Bias In Data
Biased information will produce subjective decisions. Organizations must recognize that if they build a model with biases in data, even without knowing it, the work product will continue to propagate these biases automatically.
Some guidelines can help compliance officers within the organization avoid data bias and the unethical use of artificial intelligence. Since discrimination is embedded in the data, the best assumption is to treat all information as biased, suspect, and as a source that hides a set of partial data. Under this premise, the job of the data scientists and the organization is to prove why specific data fields were used and how the algorithm or software used to handle them is correct.
And although it may be thought that there are fields whose information should not go through this corroboration because it seems obvious, when entering such confusing data (unintentionally) in the analysis software, the result is biased.
In addition, the relationship between acceptable data can also unintentionally lead to bias in the data to be analyzed. These hidden data patterns in the information are not visible to the naked eye but can be detected by AI models. For this reason, it is also essential that artificial intelligence models learn the relationship between the data and not simply be told the importance of the information introduced into an analysis model.
It should also be taken into account that the information used today may be biased in the future, so what is the company’s policy regarding continuous monitoring of data bias? In other words, the tracking of information, including sufficient details, must be constant.
The machine learning models are the ones that will be in charge of analyzing the information that is collected. And usually, when something does not work, the algorithm or the analysis model is blamed when the main problem is in the data analyzed by the software.
Here are three types of things data scientists need to consider when it comes to machine learning models :
Biased sampling occurs when one of the test data does not reflect the actual environment that the machine learning model will use. For example, if you want to build an autonomous vehicle and the information that is supplied to the model is only from a video in daylight (not at night), it is an example of data bias that can be transmitted to the model, which will also be biased toward a day-only driving system.
Stereotype Bias or Prejudice
Even if the data scientist gets a good sample of data to train their machine learning model, there are still threats lurking down the road. Prejudice biases can be hard to explain, but that doesn’t detract from their ability to harm predictive models.
An example of this type of bias can be a machine learning model designed to differentiate men and women in photos. Suppose you use more pictures of women than men in spaces like a kitchen, or more images of men as building architects than women, to train the model. In that case, you are teaching the algorithm to make the erroneous assumption that gender is related to that gender. Exercise.
Data scientists need to control this kind of bias. There are a variety of mechanisms to avoid this bias, such as “manipulating” the sample by reducing the number of photos of women in the kitchen or increasing the number of pictures of men in the kitchen.
Systematic Value Destruction
This type of bias in data occurs mainly when there is a problem with the equipment used to make a measurement or observation. This type of error can lead the results in a particular direction.
For example, if one of the cameras used to train the system uses a specific filter for one of the colors, but the other cameras used are more precise. The data that the model will use will be biased and affect the result. If, in the end, it is a general lack of precision, in the case of the camera, which generates noise and inconsistencies in the data, probably the system itself excludes said information. Otherwise, the model result will also be wrong if it is a consistently wrong measurement in a particular direction.
In general, data bias can be addressed by looking at the information and understanding how an algorithm can be used and in what context and matching between the characteristics of that context and the information you want to label.
This is not easy because it is something that data scientists must learn through experience, which is acquired through working with accurate data that can be applied to problems in study centers. To avoid bias in data handling, machine learning professionals must deal with these three aspects until they gain the necessary experience. Usually, only bias in models and mathematics are taught in universities, not in those within the data itself.
Callousness in Dating
One of the objectives behind data management that must be considered today is to achieve automation in decision-making based on models that gather this data. However, many of the companies that apply these models are not robustly building them, and they carry implicit errors that harm their clients. Data-driven decisions enable automation in decision making but also facilitate large-scale bias.
For example, COVID-19 has increased, in one way or another, economic disparities in the world, so the data has changed, and many businesses have not yet assimilated the impact that these changes may have on their data models and how to use them in decision-making processes.
Insensitive leaders stubbornly continue to take model results into account because “the model said so,” without considering how data and circumstances have changed for customer groups and adjusting the use of the said model in strategy. Of the business.
When a situation of a client with biases in data is analyzed in a cruel or disinterested way, the result is the development of much more biased models for the future. For example, certain groups are more likely to default in the financial sector due to their education or type of work. When they are not correctly categorized due to insensitivity, carelessness, or data bias, entire groups of people are being pigeonholed as more likely to have committed fraud.