Big Data Analytics

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:

1. Big data integration and management

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.

Big data replication

Big data analytics requires fast data access, high performance, and having an accurate backup of the data. To make this happen, the process of data replication copies data from master sources to one or more locations. This process can even happen in real time as data is written, changed, or deleted by using change data capture (CDC) technology.

Big data ingestion

Raw data from a variety of sources needs to be moved to a storage location such as a data warehouse or data lake. This process, called big data ingestion, can be streamed in real time or in batches. Ingestion also usually includes cleaning and standardizing the data to make it ready for a big data analytics tool.

Big data consolidation and storage

For big data analytics, data is stored in a data lake or data warehouse. The Hadoop data lake open source software framework is now popular because the framework is free and its distributed computing model can quickly process big data.

Governed big data

Big data analytics tools should also provide a governed enterprise data catalog. This allows IT to profile and document every data source and define who in the organization can take which actions on which data. This allows users to more easily find, use and share trusted data sets on their own.

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.

• As stated above, the Hadoop data lake framework is popular because the open source software framework is free and its distributed computing model can quickly process big data. There are two key components of the Hadoop framework. The first is MapReduce, which filters data to nodes of the cluster and reduces the results from each node for a given query. The second is a cluster management technology called YARN which assists with job scheduling and resource management across the cluster.

• A NoSQL database can also handle a variety of data models and is another good option for raw, unstructured big data. This is because its non-relational data management system doesn’t require a fixed scheme.

• In contrast, a data warehouse typically stores structured, filtered data that’s been processed for a specific purpose and is therefore not as popular for the often highly unstructured nature of big data. Learn more about data lake vs data warehouse.

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:

• Free Form Big Data Exploration. Your tool should allow anyone in your organization to act like a data scientist and freely explore their big data in any direction using an associative engine rather than a linear, query-based approach.

• Smart Big Data Pipelines. Modern big data analytics tools should also have data integration capabilities that automatically move, consolidate and transform big data from a variety of data sources while maintaining lineage.

• Open Platform. Third, your tool should have an open data integration platform and many out-of-the-box connectors. This will let you quickly integrate data from an ever-growing ecosystem of new technologies such as Hadoop, NoSQL, Databricks, Snowflake, Amazon Redshift, and Google BigQuery.

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:

• Conduct faster, more accurate analysis, planning and business decisions

• Reduce expenses by optimizing business processes and performance

• Increase customer loyalty and lifetime value through a deeper understanding of customers and customer behavior

• Identify new markets and sources of revenue by analyzing market trends and purchase behavior