Big Data Analytics

Companies today have access to vast amounts of data, data collected from business transactions, websites, mobile apps, call records, social networking services, sensors, medical records, and more. But this data isn’t worth much if business users can’t use it. Big data analytics tools and platforms enable users of varying skill levels to aggregate and analyze disparate datasets—to extract valuable and timely insights from this never-ending flow of data.

With robust yet easy-to-use, self-service data exploration and analytics tools, firms can empower users to find meaningful patterns and signals in the data and turn the flow of data into a flow of actionable insights that drive transformation. With big data analytics at their fingertips, users can make surprising discoveries and smarter decisions based on those discoveries. They can solve complex business problems fast and take advantage of emerging opportunities early.

Introducing big data analytics

Big data analytics is an iterative and exploratory process of applying specialized techniques and technologies to massive and diverse datasets in order to reveal meaningful patterns, trends, and associations. Handling and processing the data effectively requires enormous computing resources, robust infrastructure, and until now, highly skilled personnel. In recent years, affordable cloud storage, scalable multi-cloud architectures, ephemeral cloud computing servers, and intuitive, new analytics software have made big data analytics more accessible. Companies can now get on-demand access to the infrastructure and technology necessary for taking on big data initiatives.

Key techniques and technologies used for big data analytics

Solid data management is the first step in big data analytics and a foundational component for any successful data analytics program. To improve the accuracy of analytical results and maximize ROI, firms must ensure that their data is consistent, trustworthy, and high quality before using the following techniques and technologies commonly used in big data analytics projects:

  • In-memory processing: Uses system memory for analytics to accelerate data access, preparation, and processing, enabling users to obtain insights from large datasets in real-time
  • Machine learning (ML): Uses self-learning algorithms to automate analytical model building, allowing computers to analyze massive quantities of data faster and more accurately
  • Data mining: Uses statistical and ML methods to recognize and track patterns, find systematic relationships between variables, and detect anomalies in large datasets
  • Text mining: Uses natural language processing technology to transform text-based sources into analytics-ready data, helping users sift through massive amounts of text and identify meaningful trends, topics, and connected terms
  • Predictive analytics: Uses statistical analysis and machine learning to analyze historical data, build predictive models, and determine the probability of future outcomes

Trends in big data analytics

In addition to the recent shift among firms from a data warehouse to a data lake model, there are several trends in technology currently affecting big data analytics. Namely, there has been a dramatic increase in the variety of data formats and types of databases used in the enterprise, leading to a change of focus from big data to “wide data”. Companies that can quickly synthesize fragmented, varied data sources will gain a competitive advantage.

There is also increased interest in DataOps as organizations seek to improve their ability to work with data at scale and respond to events in real-time. An agile approach to data management practices enhances the speed and accuracy of analytics while technologies like real-time data integration, change data capture, and streaming data pipelines are enabling continuous delivery of insights to self-service analytics users.

At the same time, we’re seeing a powerful synergistic effect of big data and AI as AI is used to accelerate and automate data profiling, including the generation of data models*, personalize data discovery, and make interacting with analytics easier for users with smart data visualizations and conversational analytics. With AI-fueled analytics at hand, users can load, blend, and surface insights from big data sources faster than ever before.

*What is data modeling? It is a formal representation of a data landscape showing how data elements are related. It is used to determine how data is organized and exposed to the user and provides the structure for supporting analytical needs.

A modern data management and analytics platform for big data analytics

Qlik’s end-to-end data integration and analytics platform enables firms to leverage their existing big data investments, transform their data into a governed information resource, and provide users with simple, fast access to the business-ready data they need to make better, more confident decisions. Out-of-the-box connectors, open APIs, and a mature data integration and transformation layer let users quickly connect to and combine data from an array of databases, data warehouses, streaming platforms, and cloud platforms.

Qlik’s modern data management solution helps firms build a secure, enterprise-scale catalog of all their data, no matter where it is—while powerful data preparation tools automatically profile and enrich data, accelerating the process of transforming raw data into analytics-ready information assets. From a single self-service platform, users get on-demand access to trusted, governed data and can enhance, blend, and filter data using drag-and-drop functionality.

Powered by a one-of-a-kind Associative Engine, Qlik identifies every relationship across all your data and puts data in the context of your entire business. Qlik’s modern data analytics platform allows your workforce to dive in and examine these connections via interactive visual dashboards, AI-powered insight suggestions, and natural language search. They can explore massive datasets to experiment with and validate their ideas. With Qlik Sense®, the most complete BI and analytics solution on the market, you can simplify every BI use case from routing publishing of KPI reports to big data analytics.


How does big data analytics work?

Organizations can use a data warehouse or data lake as a primary repository for various incoming streams of data and use a big data management and analytics platform like Qlik Sense to combine, transform, and explore data in order to find meaningful patterns and obtain actionable insights from massive, diverse datasets.

What are some examples of big data analytics?

It is used for fraud detection, financial risk management, pricing optimization, process automation, traffic flow optimization, automated customer segmentation, social media analytics, ad targeting, sales forecasting, predictive maintenance, inventory management, market trend analysis

Who benefits from big data?

Companies in industries ranging from financial services and banking to retail and entertainment are reaping the benefits of big data analytics. Amazon, for example, uses big data to provide a personalized shopping experience to customers while American Express uses it to forecast potential churn and customer loyalty. And Starbucks reportedly uses big data to choose new store locations based on an estimated propensity towards revenue growth.

Learn more about Qlik’s big data management and analytics platform.

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