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Dive into Your Data Lake with Self-Service Analytics

Blog: NASSCOM Official Blog

The concept of self-service is one that dominates much of our lives today — we can ring up our own groceries, pump our gas, and answer support or inventory queries with the help of an automated system. Self-service is also sweeping through the business world, with promises of increased employee productivity and more accurate reporting.

Self-service analytics help solve a critical problem many organizations face: the unbridgeable gap between the demand for data support and the existing capabilities of a data team. This gap — what we refer to as the Activation Gap — occurs due to the combined increase in the number of users, user expectations, use cases, data volume and variety, and data security concerns. Today, organizations simply don’t have a large enough supply of big data skills or IT budget to make data available to everyone, especially when it’s stored in a non-conventional data store such as a data lake.

Increasing the Value of Your Data Lake

Companies with data lakes are collecting vast amounts of data every day, but hardly using most of it. Industry reports indicate that less than one percent of collected unstructured data is used. Why? To start with, the unstructured nature of data stored in a data lake requires specialized skills on the part of anyone who wishes to access said information. In a typical organization only a limited group of people (the data team) are able to access and leverage that data, creating an unavoidable bottleneck that disrupts the chain of data access.

As machine learning initiatives grow more widespread, the data kept in a data lake will become increasingly valuable to larger portions of a company. It is becoming imperative that businesses eliminate the data accessibility bottleneck — for the success of their data-related projects and the broader organization as a whole. Self-service analytics alleviate many of the pain points that naturally occur with a data lake, giving control back to the individual user and increasing productivity across the organization.

Four Key Characteristics of Self-Service

With self-service, data users gain the ability to analyze predetermined data sets as well as discover, query, and visualize virtually any type of data. Through self-service, users can perform four steps that traditional business intelligence (BI) and analytics tools may lack: discovery, ad hoc querying, visualization, and collaboration. Below, we outline the benefits of each of these steps.

1. Data Discovery

Data discovery is a critical piece of the puzzle, because you can’t begin analyzing data without collecting the right information. This function lets you discover data sets and run queries without waiting for data administrators to provision compute clusters and resources. Discovery also encourages cross-functional problem solving with built-in ACLs (users, groups, and accounts). Ideally, data users will be able to easily access data and metadata for discovery purposes as well as review notebooks without running clusters.

2. Ad Hoc Data Queries

Ad hoc queries let data users work autonomously without requiring specialized configurations from the IT team. Ad hoc querying eliminates the need to predict cluster size and helps you avoid query overruns. Of equal importance, ad hoc queries also enable you to choose the right big data framework or engine for your workload type — whether that’s Apache Hive for batch processing, Presto for interactive queries, or Apache Spark for stream processing.

3. Data Visualizations

Review and interpret data at your convenience, without needing to decrypt complex tables. With data visualization, you can create pre-defined schedules and preview notebooks even while offline. The right platform will let you tailor visualizations using third-party tools, JDBC/ODBC connectors, and APIs. Of equal importance is being able to access your preferred business intelligence tool, whether that’s Tableau, Looker, PowerBI, or another tool.

4. Collaboration

Make data visually consumable and available to everyone with built-in collaboration that encourages a data-driven culture. Users can interactively run queries by changing parameters, ensuring everyone’s questions get answered. Plus, users have the ability to collaborate on data from scheduled and ad hoc queries in Hive or Presto using dashboards or a preferred BI tool.

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