In the digital era, data is being created at a speed never seen before, and its proper application in business intelligence is already generating incalculable value for businesses
It seems irrational to suggest that a concept like data could be more valuable than an established and indispensable product like oil, but as the years have gone by, this already seems like a reality.
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.
Thanks to the digitalization of writing and the rise of Internet searches and interactions, words have become very valuable inputs for statistical models and algorithms that allow inferring and reaching very precise conclusions about consumer behavior, among other uses.
Today, language has become the subject of big data analysis. Since the links used by Google are composed of words and the searches performed on that page are also composed of words, the study of text as data is becoming increasingly important.
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.
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."
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 "...