Self-Service analytics: definition, challenges, and best practices for successful business adoption

Organizations possess growing volumes of data. Turning this information into business value is critical to success at an operational and strategic level. That means making data easily available and consumable across the business.

However, often data is siloed and only accessible through centralized technical tools operated and controlled by experts in IT and data teams. To access data, business users have to request reports and information from these experts, creating bottlenecks, slowing response times, and adding to data team workloads. Data is not democratized or consumed at scale.

Self-service analytics (SSA) software aims to solve this issue. Also referred to as self-service business intelligence (BI) solutions, they provide business users with direct access to relevant data, in the right format for their needs. Designed to be easy to use by non-experts, self-service analytics solutions transform how data is consumed, releasing its value through greater productivity, agility and decision-making.

This article provides an introduction to self-service analytics, the challenges to adoption, and best practices for success. It explains the key role of data products and data product marketplaces in democratizing access to data and maximizing its value.

Self-service analytics (SSA) is an approach that enables business users to access, explore, and analyze data without requiring support from technical experts. Also called self-service business intelligence (BI), it allows business professionals to perform queries and create reports (including visualizations and dashboards) independently and autonomously.

This empowers business users, such as those in finance, marketing, sales or operations, to use data in their working lives. It therefore improves performance and productivity. Importantly, it also increases efficiency for data teams, freeing them from the need to create reports for the business. This enables them to focus their time on higher-value, business-critical data requirements.

While there are a range of different self-service analytics tools available, they normally share key capabilities, with users able to:

• Create dashboards and other visualizations through a drag and drop interface
• Perform ad-hoc data queries
• Automate the production of insights, such as creating regularly-updated reports
• Share and download data, including importing it into their normal business tools, such as spreadsheets
• Compare and combine different data assets to deliver context and deeper understanding

However, while many analytics solutions aim to be self-service they still require training and some level of technical understanding. Their user interface may be simpler than more complex tools, but is not intuitive and engaging to drive wider data consumption.

As we’ll explain later in this article, data product marketplaces overcome this issue through a user experience modeled on e-commerce marketplaces. They provide everyone with a familiar, accessible interface, often further simplified by the use of AI and natural language search.

Organizations face increasing pressures to manage and benefit from their data:

• The volume, range and speed of data creation is growing rapidly
• Data is crucial to delivering competitive advantage, but is difficult to access directly by business users
• IT and data teams become overwhelmed with requests for reports from the business for information, causing bottlenecks and delays, and wasting resources
• Slow access to insights risks missing opportunities. It hampers agility and competitive advantage in a volatile business environment

Directly connecting business users with the data they need through self-service analytics platforms delivers a range of benefits:

• Faster, better-informed and more accurate decision making
• Greater productivity as users don’t have to manually search for data or wait for data teams to provide reports
• Improved collaboration between departments through data sharing
• Reduced operational costs and greater efficiency, particularly for data teams
• Higher data literacy across the business as users trust and harness data in their working lives
• Increased innovation as data is used to create new services and revenues

Demonstrating these benefits, 60% of respondents in a Dresner Advisory Services study say that self-services is critical or very important to their organization.

While the benefits of self-service analytics are clear, there are a range of challenges that hold back adoption and success, spanning technology, cultural, and organizational obstacles.

Users must trust the data that they are accessing. If data is unreliable, out of date or inconsistent they will simply not use it, reverting to requesting reports from the data team. Bringing together data from across the business and making it available to all also brings governance issues. Access to data, particularly sensitive information, must be controlled for compliance and governance reasons, although this can prevent usage at scale.

Many business teams are not familiar with using data. This lack of data literacy means they are not confident when using self-service analytics tools, particularly if they do not trust the data they are viewing. Users may misinterpret data or ask the wrong questions, especially if definitions are unclear, leading to inaccurate decision-making.

While they are designed to provide a simplified interface, many self-service analytics platforms still require training and support to use successfully. Interfaces are unfamiliar and often organizations have multiple tools, leading to overlap and confusion around which to use.

Chief Data Officers (CDOs) and their teams understand the benefits of democratizing data through self-service analytics. However, they are also responsible for ensuring compliance and governance. This can lead to fears that providing self-service access will expose confidential or personal data, breaching regulations and risking data security. Not knowing how data is being used brings concerns about losing control of data and its consumption.

Traditionally data was siloed within departments, each of which used different definitions to describe data. For example, a “customer” might mean something different to sales, marketing or finance. Bringing data together and sharing it across the organization exposes these conflicting definitions, leading to confusion, misalignment and an inability to agree on a single source of truth.

Simply rolling out self-service analytics tools does not guarantee usage. Deployments need to be backed by a full training and change management program that explains the benefits, particularly if tools are not intuitive. Often this enablement phase is lacking, meaning adoption stagnates and rollouts fail to deliver success.

Widening access to data should drive greater usage. This can increase pressure on existing technology infrastructure, leading to poor performance and slow response times. In turn, this frustrates business users, meaning they fail to adopt self-service analytics as they cannot see the immediate benefits.

Realizing the benefits of self-service analytics software requires a focus on these five best practices:

Users need to trust data, while data teams must protect it from unauthorized access. Data must therefore be reliable, accurate and consistent, and clearly explained in non-technical terms. It has to be cleansed and processed to ensure it is standardized, particularly around definitions. Implementing a business glossary of data terms delivers consistency across the organization.

Data governance has to be robust, but flexible, with granular, role-based management and seamless workflows that enable users to request access. Full audit trails show who has retrieved data, with lineage then able to demonstrate how and where it has been used.

Organizations need to build confidence amongst business teams when it comes to consuming and harnessing data. They need to build data literacy skills by exposing people to relevant data and showing how it helps them in their working lives. Highlight data champions that are successfully using self-service analytics to drive wider adoption. Encourage a culture of curiosity and experimentation, such as through awards and communities of practice.

Self-service analytics tools must meet different needs, from more confident power users, to those that are less familiar with data. That means they must be intuitive and easy to use, but also contain features that benefit all groups. For example, while some people in the business will simply want to query data for a one-off answer, others will want to create ongoing reports or dashboards. To ensure consistency, all of these requirements should be handled through the same tool, with different visualizations, download formats and API integrations to cater for all audiences.

Above all, self-service analytics tools have to be intuitive for non-experts. They have to be empowered to easily and confidently discover, access, and consume relevant data in ways that match their needs. As well as an inviting user experience, tools need to include AI to help automate the customer journey, supporting employees to harness data without requiring human technical support.

Data products connect users to data at scale, providing essential information to a broad business audience in a ready to consume format. Incorporating data contracts to guarantee service levels and control how data is used, they build trust through their quality, reliability, and ease of use. By making data products available, organizations complement self-service analytics solutions by delivering information in a business-friendly format to a large number of people.

Implemented correctly, self-service analytics platforms will dramatically increase the consumption of data, adding to pressure on your data infrastructure. To avoid performance issues ensure that you pick tools or solutions that scale to meet peaks in demand, through resilient cloud-based architectures. Check with potential vendors on how scalable their solutions are, and talk to existing customers to uncover any potential performance issues they have experienced.

Many existing self-service analytics solutions are adapted from technical tools. That means that while they may offer a simplified interface, they aren’t immediately accessible to all non-technical users.

Data product marketplaces overcome this usability challenge. Featuring an interface inspired by e-commerce sites, they deliver an intuitive, self-service user experience that connects business teams with the data they need, in the format they want. Data can be quickly visualized, turned into dashboards or downloaded in common file formats. Built-in granular access management ensures security and governance, while maximizing data access.

Users can browse a structured catalog, search for data based on their needs, or discover relevant information through AI-powered search, agents or similar data recommendations. Data product marketplaces put information in the hands of the business, increasing consumption and driving value

Timely access to reliable, accurate data is critical to successful modern organizations. Self-service analytics, particularly through data product marketplaces, empowers everyone across the business with relevant, trustworthy information. This ensures greater productivity, agility and competitiveness, creating successful, data-driven organizations now and in the future.

Learn more about how data product marketplaces can meet your self-service analytics requirements – contact us to book a meeting with our experts.