What it is, why it matters and best practices. This guide provides a definition of big data analytics and how it works plus practical advice to help you succeed with big data and analytics in your organization.
Big data analytics is the use of processes and technologies to combine and analyze massive datasets with the goal of identifying patterns and developing actionable insights. This helps business leaders make faster, better, data-driven decisions that can increase efficiency, revenue and profits.
The primary steps of big data analytics are goal definition, data collection, data integration and management, data analysis and sharing of findings. The advanced analytics involved in exploring and analyzing large volumes of semi-structured and unstructured data requires either an end-to-end big data analytics platform or a broad set of tools which are applied by data analysts, data scientists, or engineers.
Modern big data analytics involves the use of artificial intelligence (AI) and machine learning to automate processes, provide insight suggestions, perform predictive analytics and allow natural language interaction. Real-time big data analytics involves processing data as it arrives, which can further speed decision making or trigger actions or notifications.
Now let’s get more specific.
Once the data has been collected and you’ve clearly defined your business objective (such as improving marketing ROI), below are the key steps and processes involved:
Before conducting big data analytics, source data must be transformed into clean, business-ready information. Big data integration is the process of combining data from many sources across an organization to provide complete, accurate, and up-to-date information for big data analytics usage. As described below, big data replication, ingestion, consolidation and storage bring different types of data into standardized formats stored in a repository such as a data lake or data warehouse.
This step in the process involves exploring and analyzing the data and uncovering meaningful patterns, relationships and trends. The goals are to both answer specific questions and to discover new questions and gain unanticipated insights.
Here we briefly describe the key processes and technologies used in big data analysis.
Conversational analytics, also powered by AI, lets users ask questions in natural language and have answers presented in an easy-to-understand conversational manner.
Big data visualization makes it easier for analysts and stakeholders to understand and engage with big data and share insights across an organization.
See how to explore information and quickly gain insights.
Big data analytics is too broad a discipline for one single tool to completely cover. Naturally, big data analytics software is the primary tool, but below we briefly describe the related supporting technologies in order of the overall process.
Big data replication and change data capture (CDC) tools copy data from master sources to other locations. As described above, these tools allow for fast data access, high performance, and an accurate backup of the data.
Big data ingestion tools move raw big data from a variety of sources to a storage location such as a data warehouse or data lake.
Big data consolidation and storage tools such as a Hadoop data lake, allow for big data analytics usage by making data available to be processed and used flexibly for deep analysis.
An end-to-end self-service big data analytics tool, such as Qlik Sense, lets all users easily integrate, explore, and analyze big data without the need for writing code. The tool also makes it easy to collaborate and share insights across an organization through data visualization, interactive dashboards, embedded analytics and automated reports.
Here are three key big data-related capabilities to look for in a true end-to-end self-service big data analytics tool:
Compare top big data analytics tools Power Bi vs Tableau vs Qlik.
The primary benefit of conducting big data analytics is finding actionable business insights that increase efficiency, revenue and profits. A business insight is a deep understanding on a particular issue a user gains from analyzing data. Insights are actionable if they’re specific and relevant enough to direct actions.
With the right tools and strategy, here are four key ways big data analytics can deliver value for your organization:
Most organizations understand the potential that analyzing their big data presents. However, there can be considerable challenges in fully realizing this potential. Here are the key challenges:
These resources provide our latest thinking on big data and analytics.