Foot traffic data consists of spatial data (GIS), and is at the core of building intelligent strategies, transportation routes, processes and decision making in both public and private sectors.
What is it?
Foot traffic data associates people's movements with physical locations, and can be collected in different ways, such as WiFi signals, GPS from mobile devices and sensors, providing useful information for sectors like retail, real estate, agriculture, financial services, insurance, tourism, sports, entertainment, among others.
Big data analytics is an effective solution for identifying behavioral patterns and establishing strategies to help detect and prevent fraud in various business sectors.
Most companies are not aware of the information they have and how to leverage, analyze and understand it, which can result in the loss of a large amount of potentially useful data by normalizing fraud and other criminal activities in their processes and make them difficult to prevent and detect.
Data has proven to be a competitive differentiator in different business sectors. The performance of any organization is highly correlated to the maturity of its data, so it's important to know in which level you are in this process.
What is Data Maturity?
Data maturity is a measure used to determine where a company's data quality, structure, distribution, security, and analytics are in their progress.
Analyzing satellite photos to estimate the production capacity of an area or evaluating images of a product being sold on the streets to calculate its market-share are some of the business solutions that arise from the transformation of images into data.
Traditionally, when teachers or businessmen wanted data, they requested surveys. Data would be ordered, in the form of numbers or boxes checked on questionnaires.
Does it make sense to keep doing surveys to evaluate, for example, the ranking of a brand, when all the real, honest, and unbiased information can be inferred from people's behavior on the Internet?
"... Traditionally, when teachers or business people needed data, they commissioned surveys. They obtained data in an orderly fashion, either in figures or in boxes marked on questionnaires.
To effectively apply data analysis tools on a large scale, the proper structuring of the information is essential, otherwise the cost that the company will have to incur to reverse the errors will be very high.
Data governance, which encompasses the set of processes, functions, policies, standards and measurements that ensure the effective and efficient use of information, becomes relevant to enterprises, which increasingly benefit from the use of machine learning tools and statistical analysis.
The ability to identify and understand hidden patterns and correlations in large volumes of data and use them to make business decisions is becoming a strategic competition for companies for the future.
Machine learning and statistical analysis are some of the most popular techniques used today in artificial intelligence (AI) applications. Automatic learning is the AI technique for identifying hidden patterns and correlations in a large amount of data that humans cannot identify on their own, explains Brian Ka Chan, technology strategist and researcher at Smart City.
Having the required resources to manage the data needed to make decisions is crucial to the success of businesses in today's environment.
Today's data savvy organizations, those with a top-down approach to decision making, do a better job of extracting value from the data, explains a Coursera publication.
The report notes that according to Andrew Ng, Stanford professor and co-founder of Coursera, using "...data in the right way can be the path to solving critical business problems, which is the mission of business."
Using information without defined objectives and not integrating it across the entire company are part of the mistakes that organizations can make when analyzing large volumes of data.
Although there are still many companies that have not begun to analyze the information they accumulate in their operation, there is a risk that the efforts they make in the future will not achieve the expected results if the mistakes that some organizations tend to make in the process are not avoided.
The failure of polls on the presidential election in the US shows that in order to get the right information, data must be collected and analyzed with scientific rigor, free from any bias caused by the personal interest of pollsters and analysts.
EDITORIAL
Only 1 out of the 20 main pollsters, newspapers and television stations in the United States who possessed all the tools needed to properly manage the demographic data and surveys, was right in indicating who the next president would be.
The availability of data, a new generation of technology, and a cultural shift toward data-driven decision making continue to drive demand for big data and analytics technology and services.
Availability of information, new technologies and cultural change towards making decisions based on data is changing the way we do business.
According to estimates by the firm International Data Corporation (IDC), at the global level "...
The increasing speed and ease of direct access to the information needed for decision-making, is drastically reducing the number of middle managers who used to be hired to provide them.
In companies of the last century when a sales manager needed information about last year's sales in order to take a relevant decision, he or she had to contact their assistant manager, who was responsible for collecting information and presenting it to the director. Nowadays, the direct and easy access to all information concerning day-to-day business allows the same director to touch a screen, get the information and make the decision, reducing costs and saving time.