In the beginning, analyzing massive datasets on open source Hadoop was a complex process best left to PhDs. But over the past few years, that has dramatically changed. Today, cloud platforms, paired with powerful business intelligence tools, have ushered in the rise of self-service analytics, enabling data analysis power users—along with users lacking a technical background—to gain valuable business insights quickly.
Gartner defines self-service analytics as, “a form of Business Intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.”
Self-service analytics is the natural and necessary solution to two mounting problems, namely the ongoing deluge of data that organizations are experiencing, and the subsequent shortage of data scientists to capture, manage, and analyze it all. In an effort to bridge the analytical literacy gap—and to take business analytics beyond what decades-old tools such as spreadsheets, desktop databases, and reporting tools could do—many organizations have sought out self-service tools to enable the data workers they have to extract more value from ever-mounting data volumes.
Fortunately, technology has risen to meet the mounting needs of modern business, providing self-service analytics tools that can improve the productivity of business analysts, at the same time empowering other users in specific groups within the organization with analytic capabilities they would not otherwise have. This gives business decision-makers an edge by enabling them to analyze data problems more deeply and effectively themselves, whether they are trained programmers or not. As a result, decision-makers gain deeper insights quickly—insights that translate into faster and better decisions that create competitive advantage.
Adoption Barriers
Like all new technologies and tools, self-service analytics faces some barriers to adoption by organizations. Among these is a centralized model of data analysis within many organizations that restricts access to corporate data by other decision-makers. This is not surprising, being that much of this data is of a sensitive nature. And as data volumes increase within an organization, millions and even billions of dollars are potentially put at risk. Clearly going forward, businesses will need to find ways to allow the broader sharing of data for self-service analytics, while at the same time ensuring that data remains secure. The reality is that the self-service analytics model of more open data consumption benefits organizations by enabling other key players to manipulate, analyze, and gain actionable insights from their data without having to rely on external experts. After all, who better to ask the right questions of their data—the questions that can lead to real business-changing insights—than those who have the business acumen in their specific business domain that IT specialists just don’t have?
Self-service analytics is about helping businesses leverage all of their big data assets across the entire organization. In order to accomplish that purpose, what self-service analytics isn’t about—according to Gartner—is pairing commercial Hadoop-as-a-Service platforms with the same old customary BI tools. In fact, statistics show that revenue growth for traditional BI tools is flatlining, while growth for data visualization and business discovery tools is enjoying double-digit status.
Data Power Users
A number of the newer self-service analytics tools enable more skilled data analysts or “power users” to explore, merge, and visualize disparate multi-source data to generate new, valuable business insights. In turn, BI tools such as Tableau allow less tech-savvy or “casual users” within an organization to analyze and visualize data for insights, and to share those insights with other decision-makers quickly and easily.
The supportive partner of these new and better BI tools is cloud-based Hadoop-as-a-Service. After all, the analysis and visualization of big data necessitate the ability to capture, manage and store vast lakes of data, all of which Hadoop does very efficiently and affordably.
Qubole’s big-data-as-a-service offers standard mechanisms to connect to the majority of BI and visualization tools in the market, including Looker, and Tableau. Qubole works to continuously grow its ecosystem of BI/Visualization partners because we believe the success of big data projects lies in answering business questions using the right processing tool combined with the right business intelligence/visualization tool.
As data lakes grow wider and deeper and the data scientist shortage continues, more organizations will turn to self-service analytics technology and BI tools as viable solutions to meet their big data needs.
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