SELF-SERVICE ANALYTICS

The 2022 Buyer’s Guide to Customer Experience Software

To help you sort through thousands of CX software, we have compiled this buyer’s guide that enables you to evaluate your choices.

SHARE THE ARTICLE ON

Self-Service Analytics is a type of business intelligence (BI) in which line-of-business workers are empowered and encouraged to execute queries and create reports on their own with minimal IT assistance. Self-service analytics is frequently distinguished by easy-to-use BI tools with basic analytic capabilities and an underlying data model that has been reduced or scaled down for simplicity of understanding and data access.

Reach us to understand how we can help you create a single source of truth.

WHAT ARE SELF-SERVICE ANALYTICS?

Self-service analytics is a kind of advanced analytics that allows business users to modify data to identify business possibilities without having a background in statistics or technology.

Vendors are increasingly providing technologies that enable business users to interact with data gathered from a variety of sources. The programme includes a Dashboard that allows users to query and alter vast volumes of data. Previously, skilled data analysts, now commonly referred to as data scientists, were solely responsible for such data analysis.

Self-service analytics software proponents argue that a self-service approach fills the gap left by a scarcity of trained analysts and gets data into the hands of those who need it the most – business users. A self-service strategy enables business users to make data-driven choices in real time without relying on IT employees or data scientists to prepare reports. Self-service analytics critics argue that only a skilled data scientist can reliably interpret the significance of certain data connections, and that if the analysis process is mishandled, it might lead to potentially harmful judgments. Everyone believes that when a company considers using self-service analytics, it should also have a data governance strategy in place.

Start Creating Descriptive Surveys Now

AGILITY IN ANALYTICS

In today’s industry, agile analytics are a must.

To achieve self-service BI, the first step is to aim for an agile analytics environment. The most well-known software organizations nowadays use well-established agile approaches. The majority of them have a few features in common:

Simplicity: Avoid becoming paralyzed by analysis. Quickly prototype fresh concepts.
Small, iterative, and regular releases that match the demands of users.
Collaboration and communication Business and technical personnel collaborate.
Process: Never take the Agile Manifesto’s “Individuals and Interactions Over Processes and Tools” too literally. Yes, it might be a simple procedure, but we require one.

An agile analytics environment will allow you to bring up a self-service BI application that caters to our whole audience, whether it is internal teams or external customers or partners.

ETL: AN IMPORTANT PART OF THE PROCESS

Businesses that have strong EDM policies, practices, and tools have a greater chance of maintaining their data accurate, high-quality, secure, and accessible. An ETL tool is a critical component of the EDM ecosystem since it automates the process of connecting to and extracting data from sources, as well as loading the data into a destination and making it available for self-service analytics or BI systems.

Anyone in an organization may use Stitch to deal with data sources and destinations. It’s easy, uncomplicated, and ready to use right now. In minutes, we can set up a free trial and make more of our data sources available for self-service analytics and BI systems.

PILLARS OF SELF-SERVICE ANALYTICS

They are suitable for all end-user roles and skill levels: Many businesses adopt a one-size-fits-all approach to data analytics, acquiring a solution that does not quite fulfil the demands of their consumers. Some may find it excessively difficult if they merely want to check a dashboard with high-level key performance indicators (KPIs). Or it may be too basic for others who want to interact with and study data on their own to discover fresh insights.

To be successful with self-service analytics, we must first understand how your end users’ data requirements differ depending on their skill and employment level. One group of users may just require minimal dashboard interaction to consume information. A second set of reports may be required to filter, sort, and organize data regarding teams, departments, or locations. A third group may require a higher level of data access and insights to drive our business. Whatever their requirements are, deliver customized self-service options to our end customers for a more productive, engaging, and fulfilling data experience.

They give data control and governance: Data governance and control are crucial for combining our business’s data access demands with the IT team’s need for proper data protection. The idea is to strike the proper balance.

Some businesses closely regulate data access, which may be frustrating for users who wish to run their own queries to combine data sources or construct dashboards from a single piece of data. Others put up data analytics without having any control over their data. Data may be retrieved via cloud-based apps, Excel, and other sources. With all of these data sets circulating, they no longer have a single version of the truth.

We can set the necessary security controls and auditing procedures to guarantee users have the appropriate data access credentials by using self-service analytics. Be open and honest with our IT staff so they know what data our users have access to. Furthermore, by having a solution that can inherit our current security model, we eliminate the need for redundant security management.

They work nicely with current infrastructures and technologies: Organizations frequently put up numerous tools to fulfil the varying data demands of their consumers. Problems arise when they adopt distinct solutions that do not perform well together and are difficult to sustain. These solutions may have connection difficulties with existing data sources, fail to conform to the present data format, or lack the capacity to scale on existing server setups.

Give all users access to the data they require in a single solution. By incorporating self-service analytics into our application, we can utilize our existing IT architecture and security framework while also readily connecting to our data sources. We save a substantial amount of time by not building our own solution or managing several solutions. In addition, self-service analytics scale to meet the data needs of our users and organizations as they grow and transform.

They concentrate on the requirements of our application team, end users, and business: Analytics aren’t only about what IT wants; they’re also about what the business needs. Solutions that are only focused on IT rather than the business result in lower user adoption rates and exhaust IT staff in order to offer the bespoke reports that business teams want.

Create a balanced deployment team for a successful data analytics implementation. In this approach, the IT and application teams combine their technical skills with our business users’ business understanding and requirements. Incorporate feedback from our end users, who may offer light on the sort of user and data experience they like. We can readily observe how distinct these three groups’ demands are by weighing their needs and how essential a self-service analytics solution is in meeting them.

They are simple to operate and require little training: When looking for an analytics solution, a common blunder is selecting one that is difficult to use and necessitates substantial training. In a recent Hanover Research poll, more than 85 percent of respondents rated user-friendly analytics and simple navigation as “extremely significant.” Only around half of them said their present solution offers these capabilities. Self-service solutions should be simple to use, allowing users to quickly obtain the data they want, gain business insights, and generate reports.

Empower our end users with self-service analytics that are simple to learn and utilize. Our end users may benefit from eye-catching visualizations that help them acquire insights and more value from our data. Furthermore, by integrating them seamlessly right in our application workflow, we’ll see increased user adoption, higher engagement and improved user satisfaction.

Customer Experience Insights from the world’s best

Download our guide to read the insights to supercharge your customer experience programs.

POPULAR SELF-SERVICE ANALYTICS AND BI TOOLS

Businesses may select from hundreds of self-service analytics and business intelligence tools. Stitch polled its clients, and the following seven tools were named the most:

Tableau users may utilize drag-and-drop capabilities and cross filtering to build charts and dashboards. It is intended for technical and semitechnical people and is excellent for teams that require visualization and dashboarding.
Looker has a query language called LookML, which allows complicated SQL programming to be delegated to the tool’s engine. Looker is best suited for technical and semitechnical users who need to create reports rapidly.
Microsoft Power BI is suited for enterprises with both technical and nontechnical users that rely on Microsoft or Azure ecosystem solutions.
Google Data Studio helps you to create stunning dashboards and is ideal for enterprises that use Google Cloud Platform and BigQuery, as well as teams with low BI requirements and semi-technical or nontechnical end users.
Chartio is a web-based dashboarding application that supports both drag-and-drop and SQL querying. It is appropriate for sophisticated users with SQL skills, as well as semitechnical people, for quick, one-time, ad-hoc studies.
Mode combines robust SQL, Python, and reporting features to assist in the creation of dashboards, charts, and data visualizations. Mode is designed “explicitly for analysts and data scientists,” and it is appropriate for technical and semitechnical users.
Periscope Data is a SQL-first business intelligence application with optional drag-and-drop capability. It’s best suited for technical teams and features a comprehensive data governance function.

ADVANTAGES OF SELF-SERVICE ANALYTICS

A data team dedicated to high-value initiatives

Analytics specialists in firms with outdated analytics systems may spend the bulk of their time accessing data and preparing reports. Analytics specialists, on the other hand, may focus on long-term and high-value initiatives in a company with a self-service analytics solution.

A system with no friction

Users send report requests to a data specialist or someone in the IT department while using a traditional analytics or BI system. A report may be returned in days or weeks, when it may be too late to make judgments based on outdated facts.

Self-service BI solutions are frictionless: users can access data without waiting, query data on the go, and make informed decisions based on current facts.

A low entrance barrier

Self-service analytics and BI solutions enable end users to dive into company data and draw conclusions in the same way that data analysts or data scientists do, but without the requirement for programming knowledge.

Self-service systems may combine and aggregate data from various departments, including accounting, sales, ERP, and human resources. For example, data in a customer profile may be divided between the marketing and sales teams; nonetheless, all client data is available with a single query.

Explore all the survey question types
possible on Voxco

CHALLENGES OF SELF-SERVICE ANALYTICS

The most difficulties for a company contemplating self-service analytics and BI solutions may focus around enterprise data management (EDM), which includes data governance.

Data governance is a collection of practices that ensures the appropriate individuals are allocated data responsibilities. It involves stakeholders from all levels of the organization and establishes a structure for internal and external accountability in order to simplify the flow of information.

Without a solid data governance strategy in place, data quality may suffer when all users have direct access to data on a self-service platform. Unless a data governance policy tackles this issue through rules, processes, and access restrictions, business units within a big company may establish their own data models and metrics, resulting in isolated analytics.