Data Analytics

What is Data Analytics?

Data analytics is a broad field that entails the processing of raw data into new and useful insights that can help transform businesses, accelerate innovation, and predict future outcomes.

The science of data analytics can help companies and organizations understand their past and inform—if not determine—their future. In other words, proper data analysis can be the difference between success and missed opportunities.

What’s the history of data analytics?

While the mainstream concept of data analysis has been around since the 1940s, technological advances in recent decades created an exponential increase in data generation, whether from Internet game-changers like YouTube or Facebook, or the rapid rise of smart technology in and outside of the home. What is left are virtual mountains of data, just waiting to be used.

What can we do with these mountains of data?

That is where data scientists come in. These professionals interpret many forms of data, using methods such as data mining, data management, and statistical analysis. These tasks involve rigorous extraction and scrubbing of structured, semi-structured, and unstructured data. And when they uncover insights that are ready to share, they convey their findings in the form of data visualization, which helps stakeholders easily digest the new information.

Each step of the process plays a critical role in creating meaningful new directions and innovations across any number of industries.

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What are the types of data analytics?

While the exact method of data analytics varies from application to application, they each fall into one or several categories: prescriptive, diagnostic, descriptive, predictive, and cyber.

Prescriptive analytics: Prescriptive analytics helps identify the best possible recommendation for a scenario in real time, much like a near-term version of predictive analytics.

Diagnostic analytics: This category determines why something happened, using techniques like data mining, drill downs, and correlations to identify trends and make decisive actions.

Descriptive analytics: Much like diagnostic analytics, this method combs historical data to find fresh perspective. But instead of answering why something happened, descriptive analytics fleshes out what happened in more detail, using tactics like statistics, clustering, and segmentation.

Predictive analytics: As the name implies, this technique forecasts future outcomes based on statistics, modeling, data mining, machine learning, and other forms of data.

Cyber analytics: One of the newest forms of analysis, this methodology combines aspects of cybersecurity and data science to identify potential vulnerabilities and existing cyber threats.

No matter what, data scientists use the method that will help them unpack the question they are trying answer, utilizing any number of analytical tools and platforms available today.

How is data analytics used?

Modern data analysis can shed light on large volumes of data, transforming it from missed potential into powerful insights. In-depth data analysis lets organizations better understand the world around them, including their own internal work environments. In fact, data is a top reason why companies can take meaningful, informed actions and launch innovative products—often at accelerated rates. Today, data analysis is always in motion across a plethora of industries, combing a variety of human and technological sources at any given moment.

Mapping behaviors across any number of sources, marketers can get a closer look at potential and existing customers, which can lead to more personalized experiences in digital and traditional spaces. These experiences can be virtually anything, from targeted email and social campaigns to meticulously crafted in-store displays and signage to more relevant product recommendations. Insights can even lead to new products and services.

Analyzing data can help identify inefficiencies and detect mechanical failures. In a manufacturing scenario, assembly lines and other machine performance can be monitored in real time and compared to past data. If performance falls below an optimal level, on-site personnel can be alerted in time to avoid potential failures. Highlighted inefficiencies can then be leveraged to improve workflows and processes.

Moving beyond reactive decision-making, the financial sector often deploys data analysis to predict market performance, evaluate trends and assess risks, and evaluate fraudulent claims and credit applications.

However, it is important to remember that not all data stems from humans; it can collect from any number of data points. The Internet of Things (IoT) and machine learning make it possible to collect petabytes of raw data from remote sensors placed anywhere in the world—from the edge of a thermal geyser to a smart-home thermostat.

HPE and data analytics

HPE helps industries of all kinds, from healthcare to safety and surveillance, solve challenges, accelerate business outcomes, and maximize the value of their data using customized data analytics solutions and platforms. The current HPE offering includes a robust enterprise portfolio of compute and storage technologies, Big Data solutions for larger-than-life sized volumes of data, artificial intelligence (AI) and machine learning (ML) services, and comprehensive platforms like Apache Hadoop, HPE Ezmeral, and HPE GreenLake for ML Ops.

HPE GreenLake for ML Ops offers a scalable, pay-as-you-go model for managing an ML infrastructure, helping enterprises find insights right at the source without moving data to and from the cloud—less risk, more reward. HPE experts manage the solution throughout the entire lifecycle from planning to deployment to maintenance. That way, data scientists can focus their energy and resources on data science rather than complex and routine IT infrastructure.

Simply put, HPE makes the data-driven world more accessible, easier to navigate and predict, and a better place for business.