What is Self-Service Analytics & How to Implement it

With quintillion of data generated every day, consuming data is both an opportunity and a source of frustration. Luckily, analytics can tame this data chaos. However, when analytics is restricted in the hands of a few technical experts, organizations cannot fully exploit the potential of their data. Self-service analytics opens avenues for business decision makers to access data easily and use actionable insights to help improve their decisions and work.

What is Self-Service Analytics?

Self-service analytics is the ability provided by data analytics platforms to business users for accessing, analyzing, and interpreting enterprise data easily and instantly, with minimal support. Self-service analytics comprises technologies like business intelligence (BI), artificial intelligence (AI) and machine learning (ML) algorithms that leverage automation and personalization to enable users to interact, analyze, and explore data without the manual involvement of technical resources.

With self-service analytics, anyone can ask their questions in a simple language, without using SQL queries or complex syntax, to get instant insights with visualizations and summaries. For example, “Why did sales increase in 2021?” or “What was the customer churn last quarter?” or “Show me the most used coupon codes by transactions”. It is useful for all types of users, especially the non-technical business users who use insights on a daily basis to make decisions.

Who Should Use Self-Service Analytics?

Through self-service analytics tools, organizations can now empower every business user to become an augmented consumer of data insights. When organizations extend analytics capabilities to their users, they empower them with insights to reach their goals better. Research by Harvard Business Review Analytic Services establishes that organizations can substantially improve business performance by giving frontline workers modern self-service analytics tools to enable fast and intelligent actions.

Self-service analytics tools have found applications across industries and address use cases in various business functions including marketing, retail, customer success, or revenue operations. Users can interact with, discover, and consume insights on demand and at the point of decision-making. Thanks to artificial intelligence, insights are now more personalized and actionable than ever before, leading to optimized efficiency and costs for a data-literate organization.

Benefits of Using Self-Service Analytics

Organizations across industries have been seeing the benefits of self-service analytics tools. Here are some of the most important benefits.

1. Greater Data Accessibility

Self-service analytics tools are a requirement for data literacy. Data democratization initiatives must provide a friendly interface for all users to consume insights easily, without requiring extensive training or technical knowledge. In order to successfully create a data literate workforce, users need tools that help them dive deep into data and generate deep insights that aid decision-making. Here, taking advantage of AI-powered analytics boosts the usability of data by augmenting decisions with personalized, timely insights.

2. Data Resource Optimization

By reducing dependency on IT, self-service analytics frees up valuable human resources, such as Data Scientists, to focus on larger projects that require manual effort. Their expertise can be put towards complex challenges such as revenue forecasting models, competitive intelligence, and predicting market trends. On the other hand, business users can quickly automate less complex activities, such as data visualization, exploration, and periodic reporting.

3. Greater Data Accuracy

Self-service analytics encourages a single source of truth as all consumers receive data from a centralized data source updated in real time. Today, ad hoc analysis is often done offline in Excel sheets or static reports, resulting in data silos and outdated insights. Self-service instant insights break through the data silos that may hamper accuracy.

4. Improved Decision-making

In addition to basing business decisions on live data, self-service analytics further improves data-driven decision-making by expediting data access. Business users no longer have to wait for input from IT or data analysts, and can immediately tap into personalized insights to gather actionable recommendations. Organizations can now become insight-driven, instead of simply data-driven.

5. Cost Efficiency

Advanced self-service analytics tools make businesses cost-efficient by saving manpower, streamlining access to data, and helping users make the right decisions at the right time. In addition, high user adoption means better scalability across the organization and faster time-to-insight for new employees. Many companies are also turning to modern cloud analytics for rapid onboarding and deployment across multiple business units.

Implementing Self-Service Analytics in Your Organization

1. Prioritize business workflows that require data-driven insights

Start by separating vanity metrics from the actual business metrics that are most important for your business. Prioritize the business workflows that are complex, time-consuming, recurrent, and need the latest data. Consider implementing self-service analytics for such processes to expedite actions, minimize risk, and reduce delays. Review the outcomes and apply the learnings from these workflows to further apply self-service analytics to other business workflows gradually.

2. Identify data sources for business workflows

Different departments use different stacks for collecting and storing data. Decisions made based on incomplete and outdated data can lead to incorrect actions and disastrous outcomes. Perform a thorough inventory of all structured and unstructured data sources. This provides clear visibility of available data and uncovers silos for consolidating all data. Look for direct data connector capabilities in self-service analytics tools that offer better data integration and prevent data duplication.

3. Opt for low-prep/no-prep onboarding of high-quality data

Automating data processes can save significant time and efforts otherwise spent in manually cleaning and preparing data. Modern data analytics tools that offer automated data cataloging, entity relationship building, and metadata enrichment can ensure low-prep/no-prep onboarding. Once data is integrated, check for any missing or incomplete values, inconsistencies, and errors. If the data itself is incorrect, has duplicate or missing values, and is not consistent in nature, it loses its credibility to provide accurate insights. So verifying the quality of data is a must to get accurate insights.

4. Democratize access to data and insights

Don’t restrict data-driven decision-making abilities only to high-level executives. Consider enabling it for the larger frontline workforce too such as account associates, sales representatives, marketing teams, customer success teams, or retail store managers. This ensures better decisions and actions at all levels within an organization. Put in place clear data governance policies to ensure that the teams get access to relevant data based on their roles, work, or geography. This helps bring transparency and accountability while democratizing data and insights for everyone in the organization.

5. Provide better data exploration and visualization capabilities

Traditional analytics tools, with their complex syntax, SQL queries, and steep learning curves, make analytics feel like an overhead, especially for non-technical business users. When users are empowered with intuitive interfaces, search-based data querying, interactive visualizations, and audio-visual data stories, they can converse with data naturally and understand insights better. Self-service analytics removes technical barriers by offering natural language search, conversational user interfaces, and decision intelligence, thus making users self-reliant in adopting analytics.

6. Choose a self-service data analytics platform that is right for your organization

When evaluating a self-service analytics platform, look for abilities such as easy data integrations, self-service capabilities, customizable AI and machine learning models, embeddable APIs, and natural language processing. Additionally, a simple and intuitive user interface that enables business users to interact with data naturally ensures that they can do their own analysis without learning new skills or depending on analysts. These abilities will offer good returns on your analytics investments.

How Self-Service Analytics Impacts Decision-Making

Self-service analytics encourages business decision makers to become not only self-reliant but also data-driven. According to PwC research, highly data-driven organizations are three times more likely to report significant improvement in decision-making. High-impact decisions such as where to invest profits, which channel to select for maximum reach, when to launch a new product, or which new region to venture into require a thorough study of the situation, a detailed examination of facts and numbers, comparison of current and past events, and correlation with influencing factors. Self-service analytics guides decision makers to do all of such thorough analysis effortlessly in a shorter amount of time.

Self-service analytics also creates a data-driven culture across the organization. Instead of relying on guesswork, intuition, or hearsay, users can base their decision on insights, since insights are readily available through self-service analytics. This also leads to improvement in the quality of decisions, ensures better outcomes, and minimizes the chances of failure. This way, self-service analytics helps organizations reap the benefits of data-driven decision-making.

Empower Decision Makers with MachEye's Self-Service Analytics

With its intuitive user interface, intelligent search, and interactive visualizations, MachEye empowers any user, regardless of skill level and industry, to search, analyze, and interact with complex data in seconds. MachEye’s self-service analytics platform leverages the power of natural language processing and data storytelling to enable quick and easy conversations with data. It enables users to go beyond answers to gain actionable insights on anomalies, analogies, trends, and clusters. MachEye makes insights actionable and consumable and ensures that users are also updated with the What-Why-How of business events.

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