3 Benefits of Self-Service Analytics

In data-driven organizations, self-service analytics or business intelligence (BI) systems enable nontechnical users — such as executives or marketing staff — to access data, perform queries, and generate reports so they can make effective business decisions.

In organizations with traditional analytics and BI systems, when a nontechnical user wants to run an analysis or generate a report, they submit a request to a data professional, who examines the request, locates relevant datasets, constructs and runs a query, validates the results, and creates a visual report. The process might take as little as a couple of days or as long as several weeks.

Self-service analysis tools, on the other hand, automate much of this process and allow nontechnical users to explore data and share visualizations, while maintaining security protocols to protect sensitive information.

Needed: a single version of truth

The data silos that exist in most organizations can hold duplicate and inconsistent records. Before implementing self-service analytics, an organization must commit to "a single version of truth" by integrating data throughout the organization.

Most businesses accomplish this by using ETL software to extract data from the source systems and load it into a cloud data warehouse, where data analysts can transform it, analyze it, and report on it.

When data is integrated and stored in a data warehouse, a business can leverage the advantages of self-service analytics:

Advantage #1: A data team focused high-value projects

In organizations with legacy analytics systems, analytics professionals could spend a majority of their time querying data and generating reports. In an organization with a self-service analytics system, by contrast, analytics professionals can focus on long-term and high-value projects.

Advantage #2: A frictionless system

With a legacy analytics or BI system, users submit report requests to a data professional or someone in the IT department. A report might come back in days or weeks, when it might be too late to make decisions based upon old data.

Self-service BI solutions are frictionless: users can access data with no waiting, query data on the fly, and be confident in the decisions they make based upon current information.

Advantage #3: A low barrier to entry

Self-service analytics and BI tools allow end-users to delve into enterprise data and come to conclusions just as data analysts or data scientists might, but without the need for programming expertise.

Self-service systems can integrate and aggregate data across all departments — such as accounting, sales, ERP, and HR. For example, data included in a customer profile may be split between the marketing and sales departments; nevertheless, all of the customer data is accessible from a single query.

Popular self-service analytics and BI tools

Businesses can choose among dozens of self-service analytics and BI platforms. Stitch surveyed its customers, and these seven tools were mentioned most often:

• Tableau allows users to create charts and dashboards with drag-and-drop functionality and cross filtering. It's suitable for teams that need visualization and dashboarding, and is appropriate for technical and semitechnical users.
• Looker features a query language called LookML that outsources complex SQL programming to the tool's engine. Looker is suitable for technical and semitechnical users who must build reports quickly.
• Microsoft Power BI is appropriate for organizations that have both technical and nontechnical users who rely on tools in the Microsoft or Azure ecosystems.
• Google Data Studio is well-suited for organizations that use Google Cloud Platform and BigQuery, and teams with minimal BI requirements and semitechnical or nontechnical end users.
• Chartio is a web-based dashboarding tool with options for both drag-and-drop and SQL querying functionality. It's suitable for advanced users with SQL expertise, and for semitechnical users, for fast, one-time, ad-hoc analyses.
• Mode combines powerful SQL, Python, and reporting capabilities to help produce dashboards, charts, and data visualizations. Mode is built "explicitly for analysts and data scientists" and is suitable for technical and semitechnical users.
• Periscope Data is an SQL-first BI tool, with optional drag-and-drop functionality. It's best for technical teams, and it stands out with a sophisticated data governance module.

Challenges of self-service analytics

The biggest challenges for an organization considering self-service analytics and BI systems may revolve around the field of enterprise data management (EDM), which includes data governance.

Data governance is a set of disciplines that ensures that the right people are assigned the right data responsibilities. It includes stakeholders throughout the enterprise and it creates a structure for internal and external accountability to streamline the flow of information.

When all users get immediate access to data on a self-service platform, data quality could be compromised without a strong data governance policy in place. Business units within a large organization could create their own data models and metrics, creating siloed analytics, unless a data governance policy addresses this scenario through the rules, procedures, and access controls.

ETL: an important part of the process

Businesses with robust EDM policies, procedures, and tools have a better chance of keeping their data accurate, high-quality, secure, and available. An ETL tool is an important part of the EDM ecosystem that automates the process of connecting to and extracting data from sources and loading the data into a destination, making it available for self-service analytics or BI systems.

Stitch makes it easy for anyone in an organization to work with data sources and destinations. It's simple, straightforward, and ready to go right out of the box. Set up a free trial in minutes and make more of your data sources available for self-service analytics and BI systems today.