You, the enterprise and AI - Part 2: Data Science vs Artificial Intelligence

Last week we dwelled on the introduction to the concept of artificial intelligence as relates to the individual and the enterprise.?You, the enterprise and AI - Part 1. In this edition, we shall consider the clear line of distinction between the data science and artificial intelligence. There is a very strong need to establish the signs that our organizations are data – driven, given that the future of the entire world is around data and artificial intelligence irrespective of various orientations and practices. Note that the 4th industrial revolution is dawned on us already.

The operations of every organization, irrespective of the size, generates data. That employee salary paid generates data, office expense and income, inventory items and a host of other business activities. This form of data is known as structured data. In a similar manner, devices such as mobile phones, automobiles, generators, trains and other also generate data, which is known to be unstructured data.

A particular instance is the case of someone using mobile telco A, and placed a call to another person who uses mobile telco B. This scenario must have generated call logs and associated financial data in the two operators’ environments. For the telco A some transaction line would have generated both on the account of the sim owner who initiated the call. Other the hand, the other telco B also recorded some transaction line on the account of the sim owner domiciled in the environment. All the associated data generated needs to be analysed for efficiency and other business reasons.

The financial transaction lines generated in both telcos’ environments could be called structured data because they have labels and can be used in tabular forms. However, the call logs generated are never structured, that is, the data cannot be directly placed in a table form with headers. This latter data type is termed unstructured data, also known as Big Data. It is the responsibility of the data engineer to ensure that there is a design that flows the data to the appropriate storage or device (servers and the like) in the right location and environments.

Data Science

The data scientist identifies and accessed the data as must have been stored by the data engineer, for the purpose of analysis. This role owner is also responsible for the following:

• Collecting both structured and unstructured data.
• At some point, sources for the information on the possible identified missing data.
• Transform the data into a usable format
• Conduct various analyses by building data models. These analyses include but not limited to predictive analytics which organizations requires for forecasting, descriptive analysis for understanding the performance or position of an organization at point in time.
• Building machine learning algorithms for decision making.

?Artificial Intelligence

Artificial intelligence specialist who develops a machine either software or otherwise to think, work or reasons like human. The major prerogative of artificial intelligence is train computers or similar devices such robots from user experience to perform various tasks based on this knowledge. The training process is usually based on the data generated by a process interest. In our mobile user example above, there could reasons for either of the parties to need to speak to customer care to log a problem or issue. The customer care agent could be a robot (Artificial Intelligence product). The robot would have been trained on what a human being is expected to do and various other possibilities.

Artificial intelligence summarily uses intelligent and smart systems to support organizations’ drive for continuously increasing profitability. Enterprise use cases of artificial intelligence include:

1. Voice Assistance: software that carries out tasks using voice command. Example google assistant, amazon Alexa, and apple Siri.
2. Chatbot: Automated customer care agent that responds to callers just as human. It provides answers to all queries that it must have been trained with.
3. Automated Recommendations: Used for recommending possible actions that could be taken based on previous activities. Netflix uses this system for suggest to what you are likely to watch based on your previous watching patterns. Amazon uses this system to suggest what you are likely to buy based on your previous buying patterns.
4. Language Translation: supports both voice and written translation of information from one language to another.
5. Image Recognition: A technique also known as computer vision. It allows machines to interpret and categorise what they see amongst images or videos. This is used to identified a wanted criminal in a crowd. This is the principle used for identifying traffic light rule violators.

?Major Difference between Data & Artificial Intelligence

• Artificial involves a high degree of scientific techniques and processes Data Science does not.
• While data science is interested in the establishing the pattern embedded in datasets. These datasets could have been either automatically generated or otherwise. Artificial intelligence is bordered about how to the process autonomous and independent of humans.
• Data science flows through the process of data gathering, pre-processing, analysis, visualization and prediction. Artificial intelligence develops models for predicting or forecasting future possibilities.
• Data science involves lot of statistical techniques while artificial intelligence is much more involves as lot of mathematical, statistical and computer algorithms.
• Data science develops and applies statistical models while artificial intelligence is for building models that emulate cognition and human behavior.

In part 3 next week, the edition shall uncover insights to answer the following: Can your organization survive the 4th revolution? Why do your organization require Data Science & AI? Are your organizations data – driven? How do you implement a data culture?

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