Data is literally defined as facts and statistics collected together for reference or analysis. It is the observations of facts, quantities, characters, or symbols on which thinking/ operations are performed by a brain/ computer, which may be stored and transmitted in the form of speech, message, electrical signals and recorded on magnetic, optical, or mechanical recording media.
Data comes in different types, has many origins and serves multiple purposes.
We mainly speak of the following types of data:
- Binary Data is the afore-mentioned Yes/No, Pass/Fail data. This most basic form of attribute data has only 2 possible values (i.e. Yes or No).
- Categorical Data is the next rung on the evolutionary ladder of data types. This is also a form of attribute data, but rather than just 2 possibilities, you now have multiple categories into which you can assign your observation.
- Attribute Data, whether binary or categorical, is usually interpreted by converting it into percentages. For example, in an A/B test, 10% of Group A converted, while 12% of Group B converted. Categorical data gives you that next level of granularity.
- Ordinal Data is a type of categorical data where numbers are assigned to each category that has some meaning or rank. Some examples would be letter grades A-F converted to values 4-1, or the number of stars assigned to an Uber driver. Ordinal data falls somewhere between attribute data and variable data.
- Variable Data assigns numbers to observations, which is why it is also known as numerical data or quantitative This is the most commonly used data type in Engineering and Science, where you are typically measuring a part, chemical reaction or some other observation that a very specific number can be assigned to.
- Discrete variable data is where the observations are always whole numbers, such as the number of customers purchasing or the number of individual pages within a website.
- Continuous variable data is numerical data that can be more specific, such as “the video downloaded in 9.18 seconds”. Depending on how you are measuring your continuous variable data can become more and more precise, down to several decimal places.
In harmony with the digital boom, data has now turned digital.
Archives are shrinking, servers and clouds are expanding in terms of capacity and performance to be able to contain this data.
The digital data, which contracts the analog data, is defined as forms of data that uses specific machine language systems that can be interpreted by various technologies.
As simple as it might sound, people so often confuse digital data with the artificial intelligence.
While the Digital data is still a type of data “of our era”, the artificial intelligence, also known as AI, is the simulation of human intelligence that is processed by machines, especially computer systems.
These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.
These two words are quite the talk of the planet as people and organizations are embracing this world of digitalization.
Bill Gates stated: ““The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” This only highlights the necessity of both Digital Data and AI to grow in the same direction in order to hit the targets.
Every organization, today, is collecting and creating huge amounts of data. Understanding that data, interrogating data to tell relevant and meaningful stories and making those insights available for everyone is how modern businesses scale at pace.
Companies invest immensely to generate value from their data. To empower staff to make Data Driven Decisions in real-time from business insights they trust. Backed by a Data Strategy and Policies to manage data and protect privacy.
Clients work with Data Analysts to deliver an organizational change, to reinvent and innovate, leveraging owned and public data sets to produce business insights that create commercial value.
The Data of today in the clarity, the instructions and the power of tomorrow!
