AI is nothing without Clean Data

Artificial Intelligence is a pervasive technology transforming the world into a place which very few of us had imagined some decades ago. It is enabling the organizations to build smart and efficient processes, simplify complex operations and create unified experiences. AI has lesser chances of making mistakes and it is convenient to use in performing the repetitive, tedious tasks and hence improving efficiency. Logical thinking without the typical human irrationalities is another advantage of using artificial intelligence over human in decision making.

One of the Artificial Intelligence leaders, “DeepMind” has come up with a neural network which plays video games in a fashion similar to human beings. It mimics the short-term memory of the human brain. The AlphaGo program developed by this advancement, has beaten Lee Sedol- world rank 2 (in terms of number of international titles) Go player. DeepMind also came up with a generic program, AlphaZero has the ability to play better than other programs such as Go, Chess and Shogi.

Organizations working in data-intensive industries such as media, technology, telecom, consumer and financial services have the opportunity to make it big using AI. But to use AI successfully, these organizations need tonnes of data. Without the data, the prediction of customer demands and service quality demands wouldn’t be possible and hence making the implementation of customer personalized experience impossible. To make the AI remarkably intelligent, successful implementation is necessary which requires relevant inputs. Only then the AI can be utilised fully.

However, consumers have definitely become more conscious about data privacy since the news about Cambridge Analytica misusing Facebook data came out.  In a similar incident, Grindr – a gay hookup service was found sharing the Medical information-their HIV status with third parties. Such data leaks definitely make the consumer hesitant to share the personal information and hence resulting into lack of data.

Some firms may process and analyse biased data and not get any conclusive results or get flawed results. Dirty or unorganised data often leads to wrong decisions which are very harmful for the organizations. To avoid this sort of mistakes, data quality management or master data management system should be in place to evaluate the data for obvious errors, including duplicate, incomplete or erroneous records.

The problem of dirty or insufficient data can be solved by using proper sampling methods. A sample is basically a subset of population which is used to represent the entire group as a whole. Hence, to get unbiased results, selection of proper sampling method holds utmost importance. There are two main sampling types: probability and non-probability sampling. The sampling method selected should be checked periodically to avoid possibility of flawed results and get superior results.

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