The importance of data for better business decisions
Posted: Thu Dec 26, 2024 4:40 am
Using data analytics to understand the state of the business is no longer viable. New intelligent capabilities have given rise to a new vision of how data is used, making software the engine of business.
Business data analysis has become increasingly important for managers. However, the transformation of this data into results has been done in a very descriptive way, looking at what happened and highlighting its history.
The importance of data analysis for business management
This analysis is of great value, especially for companies without a department specializing in this analytical area. This allows them to obtain a summary of their productivity balances and gain insight, in the form of KPIs or more complete reports, into the current state of their business . This view falls within the perspective of the traditional Business Intelligence (BI) area.
In today’s era, the traditional BI approach, in which data is simply kazakhstan whatsapp number database to understand the state of the business, has lost relevance, giving way to Augmented Analytics. Gartner defines this more advanced analytics as:
"The use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment."
Evolution of analytical information processing
Traditional BI, which translates into descriptive analytics, is being replaced by a much more comprehensive analysis (in the sense of predictive and prescriptive analytics), in which data “comes to life” and is transformed into alerts, predictions and pattern discovery. We are currently transforming a utopia into reality , in order to meet the production of autonomous management software that assists in business decisions. In an analogy to autonomous vehicles, these intelligent resources have gradually been helping their drivers, with the ultimate goal of transforming the machine into the “driver”.
Data Science is not a recent field of study, but it has only started to be used in the business world in recent years. The advantages are obvious, but their implementation requires a large investment. The wide variety of Machine Learning algorithms does not always adapt to the reality of each company. Therefore, it is necessary to invest a lot of time, validate each case and have a vision of continuous improvement of these systems to obtain the best performance. In this science, the ideal is not to follow a trend, as in Deep Learning, but to apply the best technique according to the desired end goal . Without all this pre-analysis and knowledge, all the effort of applying Machine Learning algorithms, a posteriori, may be compromised by a result far from what is desirable.
The potential of the machine
Currently, there are several outputs presented in the form of insights, predictions or even suggestions to change the direction of the business. This last option is the most daring, but it is also the one that makes this entire system more appealing to the business world.
Having the results of intelligent algorithms at our fingertips – results that are impossible to detect through analysis with the human eye – as well as suggesting changes that benefit the business, is something that benefits any company.
At first glance, even though the data may seem to convey very little information, its full meaning can be exposed and its “anomalies” can become the revelation of a new business opportunity . Some concrete examples that can considerably improve the performance of companies include providing services such as:
Automatic stock adjustment based on forecasting greater demand (supply chain analytics);
Generation of an automatic budget based on external factors (global economic situation, competition or other external factors);
Early hiring of new resources, avoiding an increase in their market value (pricing analytics).
The evolution of data analytics has been geared towards making human interaction increasingly unnecessary, handing over process management to the “machine”. Initially, processes were more “artisanal”: created and maintained over time by the IT team. However, as data is managed more autonomously, new ways of interpreting it are also beginning to emerge, replacing “human” intelligence and helping to support decision-making.
Data-driven decision-making
Data-driven decision-making
Data-driven decision-making is a process that has been gaining more and more followers in the world of advanced data analysis . According to one study, while 91% of companies say that data-driven decision-making is important for their business growth, only 57% of companies said they base their business decisions on data. This disparity highlights the importance – and potential business impact – of developing software with advanced analytics that analyzes and presents data more effectively, drawing meaningful conclusions from the data to aid in decision-making.
This is a process that involves collecting data based on measurable goals or KPIs, analyzing patterns and facts from that data, and using them to develop strategies and activities that benefit businesses in various areas. In this process, data scientists need data in two very distinct dimensions: quality and quantity , both of which are fundamental to making a decision guided by that data.
This mindset enables companies to create and drive new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights. One of the most interesting ways to do this can be to search for patterns or clusters in data. Identifying these “invisible” relationships can have a big impact when the focus shifts away from one variable or another and ultimately identifies the true source of change.
Supervised Machine Learning (with feedback)
The application of a data science architecture can be seen as an ongoing dialogue between data and business. One of its main objectives is to drive business and decision-making operations to maximize profitability or efficiency metrics . One way to achieve this goal is to promote a supervised approach. In this architecture, the main focus is on user interaction with the software. The opportunity to include user feedback allows all current and future analysis to be much closer to reality. Between explicit feedback (when the user is asked directly about their level of satisfaction) and implicit feedback (through the natural activity of using the application), the latter gains greater importance since it is integrated during the User Experience, without resorting to a more invasive approach, which could have a negative impact and influence the user's response.
Business data analysis has become increasingly important for managers. However, the transformation of this data into results has been done in a very descriptive way, looking at what happened and highlighting its history.
The importance of data analysis for business management
This analysis is of great value, especially for companies without a department specializing in this analytical area. This allows them to obtain a summary of their productivity balances and gain insight, in the form of KPIs or more complete reports, into the current state of their business . This view falls within the perspective of the traditional Business Intelligence (BI) area.
In today’s era, the traditional BI approach, in which data is simply kazakhstan whatsapp number database to understand the state of the business, has lost relevance, giving way to Augmented Analytics. Gartner defines this more advanced analytics as:
"The use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment."
Evolution of analytical information processing
Traditional BI, which translates into descriptive analytics, is being replaced by a much more comprehensive analysis (in the sense of predictive and prescriptive analytics), in which data “comes to life” and is transformed into alerts, predictions and pattern discovery. We are currently transforming a utopia into reality , in order to meet the production of autonomous management software that assists in business decisions. In an analogy to autonomous vehicles, these intelligent resources have gradually been helping their drivers, with the ultimate goal of transforming the machine into the “driver”.
Data Science is not a recent field of study, but it has only started to be used in the business world in recent years. The advantages are obvious, but their implementation requires a large investment. The wide variety of Machine Learning algorithms does not always adapt to the reality of each company. Therefore, it is necessary to invest a lot of time, validate each case and have a vision of continuous improvement of these systems to obtain the best performance. In this science, the ideal is not to follow a trend, as in Deep Learning, but to apply the best technique according to the desired end goal . Without all this pre-analysis and knowledge, all the effort of applying Machine Learning algorithms, a posteriori, may be compromised by a result far from what is desirable.
The potential of the machine
Currently, there are several outputs presented in the form of insights, predictions or even suggestions to change the direction of the business. This last option is the most daring, but it is also the one that makes this entire system more appealing to the business world.
Having the results of intelligent algorithms at our fingertips – results that are impossible to detect through analysis with the human eye – as well as suggesting changes that benefit the business, is something that benefits any company.
At first glance, even though the data may seem to convey very little information, its full meaning can be exposed and its “anomalies” can become the revelation of a new business opportunity . Some concrete examples that can considerably improve the performance of companies include providing services such as:
Automatic stock adjustment based on forecasting greater demand (supply chain analytics);
Generation of an automatic budget based on external factors (global economic situation, competition or other external factors);
Early hiring of new resources, avoiding an increase in their market value (pricing analytics).
The evolution of data analytics has been geared towards making human interaction increasingly unnecessary, handing over process management to the “machine”. Initially, processes were more “artisanal”: created and maintained over time by the IT team. However, as data is managed more autonomously, new ways of interpreting it are also beginning to emerge, replacing “human” intelligence and helping to support decision-making.
Data-driven decision-making
Data-driven decision-making
Data-driven decision-making is a process that has been gaining more and more followers in the world of advanced data analysis . According to one study, while 91% of companies say that data-driven decision-making is important for their business growth, only 57% of companies said they base their business decisions on data. This disparity highlights the importance – and potential business impact – of developing software with advanced analytics that analyzes and presents data more effectively, drawing meaningful conclusions from the data to aid in decision-making.
This is a process that involves collecting data based on measurable goals or KPIs, analyzing patterns and facts from that data, and using them to develop strategies and activities that benefit businesses in various areas. In this process, data scientists need data in two very distinct dimensions: quality and quantity , both of which are fundamental to making a decision guided by that data.
This mindset enables companies to create and drive new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights. One of the most interesting ways to do this can be to search for patterns or clusters in data. Identifying these “invisible” relationships can have a big impact when the focus shifts away from one variable or another and ultimately identifies the true source of change.
Supervised Machine Learning (with feedback)
The application of a data science architecture can be seen as an ongoing dialogue between data and business. One of its main objectives is to drive business and decision-making operations to maximize profitability or efficiency metrics . One way to achieve this goal is to promote a supervised approach. In this architecture, the main focus is on user interaction with the software. The opportunity to include user feedback allows all current and future analysis to be much closer to reality. Between explicit feedback (when the user is asked directly about their level of satisfaction) and implicit feedback (through the natural activity of using the application), the latter gains greater importance since it is integrated during the User Experience, without resorting to a more invasive approach, which could have a negative impact and influence the user's response.