AI And Privacy: Safeguarding Data In Era Of Advanced Analytics
Advanced analytics is the analysis of data using sophisticated techniques and tools, typically beyond those of traditional business intelligence – to discover deeper insights, make predictions, or generate recommendations.
By Tarun Dua
Advanced analytics is the analysis of data using sophisticated techniques and tools, typically beyond those of traditional business intelligence – to discover deeper insights, make predictions, or generate recommendations. Some commonly used advanced analytics techniques include data/text mining, sentiment analysis, simulation, complex event processing, network and cluster analysis, machine learning, pattern matching, forecasting, multivariate statistics, and neural networks.
Types And Applications Of Advanced Analytics
Advanced analytics uses different types of business analytics including descriptive, predictive and prescriptive. Descriptive analytics examines what happened in the past, for example, an annual revenue report. Predictive analytics aims to predict likely outcomes and make forecasts using historical data, for example, an e-commerce business using a customer’s browsing history for product recommendations. Prescriptive analytics uses data to identify possible future outcomes and show the best option, for example, calculating client risk in the insurance industry to determine what plans should be offered.
Use Cases Of Advanced Analytics
Marketing metrics – marketing organisations can create targeted marketing campaigns and avoid wasting money on ineffective strategies.
Supply chain optimization – can help an organization factor demand, cost fluctuations and changing consumer preferences to create an agile supply chain.
Advanced analytics tools - The tools are either open source or proprietary. Open-source tools are generally inexpensive to operate, offer strong functionality and are backed by a user community that continually innovates the tools. Proprietary tools – vendors like Microsoft, IBM and others all offer advanced analytics tools.
Strategies To Safeguard Personal Information
In this era of widespread use of advanced analytics, businesses have several technology-based options to protect privacy.
Data minimization, or reducing the data collected – No personally identifiable information is collected unless a compelling purpose is defined, thereby reducing privacy.
De-identification, or making individuals less identifiable – Datasets are stripped of all information that could identify an individual, directly or through linkages with other datasets.
Differential privacy – Random ‘noise’ is injected into the results of dataset queries to guarantee that the presence of any one individual in the dataset is masked.
Synthetic data – As long as the number of individuals in the dataset is large enough, it is possible to generate a dataset composed entirely of ‘fictional’ individuals or altered identities that retain the statistical properties of the original dataset while ensuring ‘noise’ guarantee.
User access controls – A set of processes that grant or deny specific requests to obtain information and is combined with other security measures to safeguard personal information.
In 2023 and beyond, privacy concerns are going to be at the center of data analytics and data security, propelling companies to take active measures to safeguard their clients’ data.
(The author is the CEO of E2E Networks Ltd, an accelerated cloud computing platform.)
Disclaimer: The opinions, beliefs, and views expressed by the various authors and forum participants on this website are personal and do not reflect the opinions, beliefs, and views of ABP Network Pvt. Ltd.
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