What Is The Use Of Statistics In Big Data?

Big data refers to extremely large and complex datasets that are difficult to process using traditional data processing applications. With the exponential growth in data volume, velocity, and variety, big data is increasingly being generated from sources like social media, smartphones, sensors, log files, and more.

The big data holds great promise, it also presents challenges in terms of capturing, storage, search, sharing, analytics, and visualization. This is where statistics plays a crucial role in utilizing the power of big data. In this article we.ve discussed main points where statistics is used in big data.

Exploratory Data Analysis

The first step towards analyzing big data is exploratory data analysis (EDA). It helps in getting first impression of the data, detect patterns, spot anomalies, check assumptions, and determine optimal factor settings for further analysis. EDA techniques commonly used on big data include:

Descriptive statistics – Measures like mean, median, mode, standard deviation, quartiles, crosstabs, and correlations allow summarizing large datasets with single representative values. They provide concise overviews and help spot outliers.
Data visualization – Visual representations like histograms, scatter plots, heat maps, and network graphs identify patterns, trends, and associations that go unnoticed in endless rows and columns of numbers. Visualizations make big data easier to interpret.
Sampling – As exhaustive analysis of voluminous big data is infeasible, sampling extracts smaller representative datasets that are more manageable. Descriptive and visual techniques applied on samples allow cost-effective explorations.

Statistical Modeling

Exploring the dataset is the prelude to building statistical models that generate actionable insights from big data. Some key modeling techniques are:

Regression analysis – Regression modeling finds relationships between variables. With huge datasets, regressions become more precise in predicting impacts. Big data allows the inclusion of diverse features in regressions.
Machine learning – ML algorithms automatically learn from data and improve with experience. They reveal intricate patterns that enable accurate forecasts. ML methods like neural networks capitalize on big data volumes that can have billions of training examples.
Data mining – It finds novel, useful, and unexpected patterns like associations, sequences, classifications, and clusters within big data. This allows businesses to uncover hidden insights like customer preferences.
Multivariate analysis – Examining interdependencies between multiple variables brings out insights that univariate analyses fail to capture. Big data allows synthesizing observations from various sources.
Sentiment analysis – It automatically determines subjective opinions and attitudes behind text data. Sentiment analysis on tweets, reviews blogs allows brands to monitor customer satisfaction and campaign results.
Time series analysis – Dynamic time series modeling uncovers trends and cyclic components. It enables forecasting based on historical time-stamped big data. Time series analysis helps anticipate future demands and trends.

Statistical Testing

After developing models, statistical testing evaluates their accuracy and validity on big data samples. Common techniques include:

Hypothesis testing – Statistical hypotheses about data distributions, correlations and differences between groups are tested to arrive at mathematically quantified conclusions. Tests like z-test, t-test, chi-square test prevent false inferences.
Resampling methods – The robustness of models built from sample data needs confirmation. Resampling techniques like bootstrapping reuse the sample data to simulate modeling on several representative datasets and assess variation in outcomes.
Significance testing – Statistical significance quantifies the probability of observations occurring randomly if the hypothesized effect was absent. This prevents ascribing unwarranted importance to commonplace effects. Significance tests counter distorted insights from small samples that temper big data.

Optimization

Big data analytics fuels data-driven decision making across functions, be it targeting advertising, personalizing recommendations, improving equipment reliability or predictive maintenance. Statistical techniques optimize decision outcomes:

A/B testing – Trying out multiple alternatives to determine the optimal digital experience, marketing campaign, warranty period, procurement policy etc. Statistical significance deduces the best performer.
Multivariate testing – Varying combinations of diverse factors allows determining the blend that maximizes sales, social media engagement, coupon redemptions, or customer loyalty.
Regression – Quantifying impact relationships between variables through regression modeling is leveraged to optimize pricing, inventory, staffing levels, risk exposure limits etc. to best business objectives.
Simulation – Simulating scenarios by substituting alternative input parameters into computational models predicts performances under changed conditions. This enables choosing the ideal parameters.

Thus statistical thinking and methods catalyze deriving value from big data at every stage right from exploratory analysis to optimizing decisions and outcomes. The exponential increase in data analytics pipelines with big data owing to the emerging potential of statistics evidences the integral role of statistics in use of big data.

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