What is big data analytics?

By Rachel Lee - October 11, 2021

Big data analytics is the practice of processing high volumes of data (think terabytes and petabytes) which businesses use to reveal insights, patterns, and actionable decisions. Such data comes from multiple sources and can be structured or unstructured.

What makes data “big”? There’s data, and then there’s big data. Today’s businesses rely on the power of big data to get ahead in today’s fast-moving world. But they also need some truly advanced analytics and data science to understand and make the best use of large datasets.

That’s where big data analytics tools come in. Big data technologies like communications service providers (CSPs), diagnostic tools, and financial analytics are built to tackle the high volumes and varieties of data, and the velocities with which big data sets get populated.

Let’s dive deeper into the advantages offered by big data analytics over smaller-scale traditional data analytics.

Big data analytics vs. data analytics

More than just a large amount of data, big data encompasses data sets with a scope beyond that of traditional relational databases. Data scientists tend to think of big data in these terms:

    • Volume, with terabytes and even zettabytes stored in such repositories as data lakes and data warehouses
    • Variety, including structured data, semi-structured data, and unstructured data
    • Velocity, the speed at which data is generated

Besides handling high volume and variety of data, big data analysis also requires the ability to process data quality, considering aspects such as:

    • Veracity, determining how reliable and authentic data and its source(s) are; such data sources include artificial intelligence (AI), the Internet of Things (IoT), mobile, and social media
    • Value, determining what different data sets have to offer
    • Variability, to observe how quickly, how often, and to what extent your data changes and how that factors into the observations you’re trying to make

Types of big data analytics

Today, most data analysts deal in five types of big data analytics, which include:

Prescriptive analytics

Working in the present, prescriptive analytics recommends the best actions to take for a situation as it is unfolding.

Diagnostic analytics

Using techniques like data mining, drilling down, and correlation, diagnostic analytics helps data scientists understand why something happened in the past, such as identifying consumer and market trends.

Descriptive analytics

Similar to diagnostic analytics in its focus on the past, descriptive analytics cover what happened in the past.

Predictive analytics

Predictive data visualizations and other types of predictive analytics forecast what’s most likely to happen in the future, using modeling, data mining, and machine learning to do so.

Cyber analytics

A rising specialty within data analytics, cyber analytics use a data-driven approach to protect against cyber attacks.

Benefits of big data analytics

Businesses can benefit greatly from such enhanced data processing:

    • Enhanced decision-making: Since businesses can find hidden patterns in raw data across their entire ecosystems no matter the data platform and volume of data, they can make truly informed, smarter big-picture decisions to propel their organizations forward.
    • Cost optimization: Thanks to algorithms that allow for superior automation, big data analytics help businesses reduce the costs associated with the more time-consuming and error-prone human staffing needs of data management. Add in cloud computing, and businesses can even save on hardware needs like servers and other forms of physical data storage.
    • Improve efficiency: Another benefit of automation? Near real-time speed and accuracy. Users can also enjoy the convenience of accessing company-wide data from a single platform.
    • Boost sales: Using big data analytics to forge a deeper understanding of the customer experience, businesses can hone in on their customers’ needs, solve user problems through targeted product development, and secure customer loyalty.
    • More opportunities to innovate: Fast, holistic business intelligence means organizations have both the knowledge and the time to focus their energies on paving the way to even bigger futures.

Big data analytics is made easy with Sisu

In our globally connected world, incomprehensible volumes of new data are generated every second. That means businesses have to be able to not just process new information, but also be ready to make new decisions and prepare to shift gears faster too.

Beat the competition in just a few clicks with Sisu. With our Decision Intelligence Engine, we can tackle big data analytics so you can stay fluent in the language of your organization’s past, present, and future at all times.

See just how much you can save in costs, time, and energy for yourself by scheduling a demo with Sisu’s Decision Intelligence Engine today.

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