DataPillar

Data Accuracy

Why business should focus on Data Accuracy?

Data Accuracy:

Data is a vital piece of a business, which is captured and can be utilised to make business decisions. The volume of data accumulated over time can grow rapidly over time and become unmanageable. The accuracy of the data that influence the decisions made by the business is extremely crucial. 

Do we care if the data is incorrect or the quality of the data is poor? Consider the scenario where the performance of the business is measured based on the data gathered about the sales of a product by region and category. If the accuracy of the data is poor in this instance, the decision taken based upon the incorrect data will result in poor management decisions. The number of employees working in each region for a product category could be changed based on incorrect data, resulting in a long-term impact on employee satisfaction and poor resource management. 

How does a system predict using AI?

Prediction is based on the result generated by an algorithm after it has been trained on historical data and applied to new data when you’re trying to guess the likelihood of a particular outcome. The algorithm will generate possible values for each record in the new data, allowing the system to identify what that value will most likely be.

AI model predictions allow businesses to make decisions based on predictions generated by the system. The prediction is based upon historical data, which provides rich insight into the business, enabling the business to target the most relevant audience with specific custom information designed for a target demographic.

Is data the real asset for a business?

Data is no longer a technology-owned artifact. Data is rapidly becoming a centerpiece of corporate value creation. Today most businesses are data-driven in one way or another. Data contributes not only to brand equity but to what constitutes product and service delivery in globally connected and hyper-competitive markets. Failure to understand the value of data may therefore woefully undervalue the importance of data captured and maintained by the businesses, which could result in data misconduct due to misevaluation of data. 

Data is the most valuable asset to any business. As businesses grow, more data is generated.  The effective use of data will help predict the outcome which will provide valuable business intelligence to make an accurate decision. One of the common challenges faced by every business is the ability to store, share, protect, and retrieve the ever-increasing volume of data. A robust mechanism to store and retrieve data will give a competitive edge to the business and accelerate its success the business

Garbage in, Garbage out!!

AI requires an enormous amount of data to learn and train the predictive models. To feed the system with a huge volume of data, business invests in new tools for gathering, storing, and processing the data. Most of us assume the data is of high quality and feed our data into machine learning engines and expect insights and recommendations from the engine which could help the business. Let us take a look at some of the challenges faced by the business involving data accuracy and revisit the process.

As the saying goes “Garbage In, Garbage Out”. If the quality of the data fed into the AI engine is poor, then the resulting predictions are less likely to be reliable and accurate. Data accuracy is a fundamental factor for producing robust models, which could generate reliable predictions. If the data is incomplete, outdated, and inaccurate, the models created from this data source have little chance of being reliable. 

Achieving data accuracy is quite difficult and the reality is quality of data is influenced by various factors such as, how data was captured, where it is sourced, when it was captured, and from whom it was collected. It is extremely important to understand and analysis the key areas including the uniqueness of the data, category and data type, incomplete and missing data, and finally the sample data that represents the larger entity. Understanding these areas will help in figuring out an approach to achieve data accuracy and build reliable predictive models. 

AI system with powerful prediction capability:

Artificial Intelligence systems are predicting the possible outcomes of the future, which is mind-blowing and unbelievable at times. AI’s prediction capability has grown drastically in the past few years, such as the way that AI is applied in various domains including financial services, banking, agriculture, and health care.

The predictive capability of AI has proved to be accurate and even better than humans in recent years. AI can consume huge volumes of data in minutes (even seconds) by rapidly analysing millions of data sets and help the business make smarter and quick decisions. It is close to impossible for the human workforce to analyse massive amounts of information and generate predictions. A powerful AI predictive system opens up new opportunities by identifying growth areas and helping to uncover unknown data. 

Since the AI capability has improved multifold, it is time for organisations to take advantage of the predictive capabilities of AI application. In this journey towards the implementation of AI capabilities, it is important to focus on key areas such as data accuracy [prediction is only as good as the input data], representative data [sourcing data from multiple reliable sources] and leverage deep learning.