Self-Service Analytics Defined: Best Practices and Strategies

Business leaders need information to make critical decisions and anticipate and respond to industry and market changes. In theory, today’s vast stores of data should make gaining insights easier. But too often the reality is that getting relevant data requires a request to an IT staff already juggling multiple responsibilities.

Self-service analytics changes the game for businesspeople by replacing the gatekeepers of IT tickets, data extracts, and report requests with technology that allows nonexperts to gather and manipulate data, apply advanced techniques, such as machine learning (ML) and artificial intelligence (AI), and generate their own visualizations and reports. The result is an organization where business users can follow their hunches and curiosity to uncover the answers they need, all in a timely way that ensures findings are still relevant and actionable.

What Is Self-Service Analytics?

Self-service analytics is technology that lets people without IT or data science experience comb through operating data and find timely, relevant insights. With self-service analytics, business users, such as salespeople, marketers, and manufacturing teams, can harness the power of an analytics platform without support from data scientists or IT pros.

To enable self-service analytics, a company implements an analytics tool, often residing in the cloud, and then connects it to a repository of data. In traditional analytics, IT teams often had to handle requests from business users to create and download data extracts. Similarly, sometimes sales and marketing would turn to business intelligence or data science teams to produce summaries, reports, or analysis. The “self-service” aspect of self-service analytics refers to business users being able to handle both tasks without assistance. The data is directly connected to the analytics software, so users can select the right data themselves, and the platform’s tools let them run their own analysis and visualizations.

With self-service analytics, business users can perform many tasks that previously required specific expertise, including processing data sets, generating insights, designing dashboards, and creating visualizations. Some self-service analytics tools have built-in AI and ML capabilities that quickly sift through very large data sets to find insights and uncover hidden patterns. In particular, the recent integration of AI and ML has unleashed a transformative impact on the capabilities of analytics. By ushering in automation, nontechnical users are empowered in the discovery process. Simply connecting an analytics application to a source creates an automatic profile of the related data—skipping numerous steps to help users find they're searching for. In many cases, this allows users a freedom of movement in data discovery when they don't even have a particular query in mind.

Key Takeaways

• Self-service analytics empowers business users to generate reports, visualizations, and analyses easier and faster, helping them respond more effectively to changing conditions.
• By letting teams directly access data and tools, business users can iterate their research and add new data sources as needed.
• Because no tickets or assistance are required, employees have more freedom to pursue what-if scenarios and quickly address new problems or opportunities that arise.
• Self-service analytics reduces the burden on IT staff, giving them time to explore opportunities such as integrating new data sources or finding new tools for analysis and governance.

Why Does Self-Service Analytics Matter?

Whether in finance, HR, operations, or sales and marketing, success often depends on generating clear insights on what’s happening and changing, then swiftly formulating and executing response plans. What stands in the way of fast action? Often, it’s that line-of-business teams must rely on other parts of the organization to run analytics so they can clearly understand the situation.

Self-service analytics changes that scenario. Instead of filing a ticket or sending an email, a user goes to the self-service analytics platform to directly access data sets, select parameters, and then use provided tools to generate data-driven insights and create visualizations and reports. The resulting analysis is loaded and performed within the tool itself rather than using an application such as a spreadsheet to collect data. That minimizes opportunities for manual errors or accidental data deletions. Another improvement is that self-service analytics makes it much easier to iterate—to find a nugget in the data, and then follow that idea with different paths of analysis without having to wait on an IT team to respond.

Analytics don't have to exist separately from an organization's applications. In fact, studies have shown that analytics uptake increases significantly when users can access embedded tools directly within an application. Why is this? Simple human behavior: When it's easier and needs fewer steps, people are much more likely to try it. In the case of embedded analytics, when an environment supports analytics, data export/import hurdles disappear to encourage working with it right then and there—and when they do, they can generate further insights faster and more frequently. A common example of comes from the web, where analytics data and reports are often embedded in an article or page to grant instant access.

11 Self-Service Analytics Best Practices and Strategies

Implementing self-service analytics involves much more than purchasing a cloud-based tool and flipping the “on” switch. Successfully rolling out this approach across an organization requires a number of business strategy and technology considerations, including training employees and creating data standards. The following are key best practices and strategies to successfully bring self-service analytics into an organization.

1. Assess your needs

Before a company acquires a self-service analytics platform, leaders should identify their most important data-driven processes and brainstorm on how those might be improved with stronger analytics capabilities. Operational teams should create a list of internal and external data sources they need to support that vision, along with areas that might benefit from additional data sources or more powerful techniques such as AI-powered analysis and modeling. These insights will inform which analytics platforms offer the necessary capabilities.

2. Find the right platform provider

With a needs assessment in hand, IT leaders can make a short list of data analytics platform providers. IT should partner with business groups that will use the tools for product review and selection. Book a demo to show employees the UI and review customization options. Bring in finance: Will you go cloud or on-premises, and is the cost structure aligned with your needs? Loop the security team and legal into the process to assess data security and governance features.

Look for key features such as the following:

• Self-service drag-and-drop data loading, data transformation, and business modeling.
• Analytics engines powered by AI and ML to increase accessibility among nontech users.
• Built-in graph analytics to visualize relationships and connections between data entities. For example, will your CFO be able to see all supplier costs and internal headcount associated with a given customer project?
• Spatial analytics to answer questions such as, Where did bad weather impact our deliveries, or which customers might respond to a specific upsell offer?

3. Start with quick wins

Driving wide adoption of a new self-service analytics platform can be one of the hardest steps. We're used to our familiar processes, however imperfect they are. The best way to get employees to fully utilize your new platform is to show how it helps each team accomplish one frequent, time-consuming task more easily. Examples include analyzing campaign conversion rates (marketing), sales growth by territory (sales), and inventory turnover (operations).

4. Expand to more advanced uses

The key to successful self-service analytics is for users to take gradual steps into more complex analysis. These platforms make it easier to work with multiple data sources, large data volumes, and advanced capabilities such as machine learning. Using one of the examples above, sales leaders can add a dimension to a growth analysis by importing marketing campaign data to see how various territories benefited from campaign support—without the copy/paste concerns that come with manually integrating data.

5. Encourage experimentation

Self-service analytics platforms come with powerful features that make it easy for business users to access deeper analysis, such as queries via natural language processing, one-touch visualizations, and predictive modeling. To ensure teams take advantage of these features, launch the self-service platform with a high-level tour of features, along with examples of how to apply them in specific use cases. Nurture power users with dedicated support resources. Ensure employees understand that this platform is much more than a spreadsheet replacement; ideally, they can use the analytics platform for the entire analysis workflow from data to decision. If your applications embed analytics within their environments, this overcomes user adoption hurdles and can increase uptake, leading to faster and easier experimentation.

6. Identify data gaps

As teams become more familiar with working in an analytics-driven organization, they will identify new data sources that would improve results, whether by filling in gaps or replacing sources that are incomplete, outdated, or difficult to work with. Encourage teams to look for gaps and identify new data streams. Have a process for people to communicate their needs upstream. This will allow IT's data curators to assess new data sources or transformation techniques to fill those gaps.

7. Build data readiness

“Data readiness” refers to having accurate, complete, and deduplicated data formatted for use in self-service analytics and other tools. The biggest benefit of self-service analytics is that it lets business users and other nonexperts derive insights from data sets. The flip side, though, is that those users won’t have the expertise of database managers or data scientists, so data readiness issues such as format problems or missing data must be addressed before data is made available to self-service analytics tools. Data sources should be validated for accuracy and cleansed to meet standards for formatting and definitions. Data readiness should include training for power users in business units that describes potential issues and how to flag them for IT staff.

8. Plan for growth

When rolling out self-service analytics, the underlying infrastructure must be capable of handling broad adoption across teams as well as support for and management of incoming data sets. What’s required to scale will differ among organizations based on the number of users, types of analysis they’re doing, size of the data sets, and how many sources are configured. Additional practical considerations include governance issues and whether data sources contain structured or unstructured data. Structured data may come with requirements such as specific data warehouse needs that can make it more costly to expand. In many cases, organizations choose to gradually roll out self-service analytics by department rather than organization-wide to balance the technical and training factors involved in scaling up access.

9. Establish organizational data standards

As business users gain experience with self-service analytics, they’ll start seeing more exciting possibilities, and this mindset should be encouraged. On the practical side, IT teams need to develop organizational standards for data, including formatting, data ingestion, completeness, and organization. Forcing users to resolve inconsistencies in elements such as date/time format and significant digits will dampen enthusiasm. Instead, set standards to ensure uniformity and encourage teams to bring in new sources of insights.

Data standards simply make it easier to use and share information. For IT staff, having standards means minimizing work spent normalizing data while also making it easier to spot anomalies. Organizational-level standards should focus on high-level data policies—data definitions, transformation processes, data sourcing. On an operational level, companies can also set up standard report formats to help both creators and readers know what to expect while still giving them the freedom to create custom reports, if a self-service platform supports that. For example, setting up a standard report output for certain machine learning algorithms can help teams more swiftly integrate that analysis into user-created reports.

10. Make data security, privacy, and compliance a priority

Giving more employees more access to more data for self-service analytics can require the organization to take steps to avoid risk, from disclosure of customer information or sensitive operational data. Compliance and privacy requirements such as GDPR or country-specific data residency rules mean companies must stay on top of regulations. Addressing security also entails checking that sensitive data is not seen by the wrong parties or disclosed on public-facing sites, which requires setting up granular access levels based on user role and data sensitivity.

11. Know self-service analytics’ limits

Self-service analytics can be a boon for productivity and creativity, but not all data is appropriate for a self-service environment. Some data sets might be so large that analyzing them puts a strain on the entire infrastructure. Some sources might require too much upfront cleaning without sufficient benefit, while others contain sensitive data that shouldn't be disclosed in a self-service environment. Line-of-business teams should identify what currently unavailable data sets would be most useful to their groups and collaborate with IT on the costs of adding them in terms of staff time, infrastructure use, and security.

Revamp Your Business with Self-Service Analytics

To implement the self-service analytics best practices above, organizations need a platform that works for everyone who relies on data—especially power users, business unit leads, IT teams, and executives. Ideally, a self-service analytics platform provides an intuitive interface that lets business users jump right in, features that support complex projects for data scientists and advanced users, easy connectivity with data lakes or data warehouses, and modeling and insights using artificial intelligence that encourage experimentation.

Oracle Analytics delivers this range of capabilities to support self-service analytics. Oracle Analytics integrates into data repositories while offering a suite of features that let people with a broad range of skill sets get results. Oracle Analytics delivers ready-to-use capabilities—including self-service analytics, real-time streaming analytics, and data visualizations—to pull actionable insights from all types of data, whether in the cloud, on-premises, or in a hybrid environment.

Don’t let governance and standards concerns slow your embrace of self-service analytics. If business users cannot explore and analyze data on their own, overburdened IT and data science teams will always have a huge backlog of requests, and employees may become discouraged and abandon their search for new business insights.

Tools that facilitate free-form and ad hoc data exploration will pay off in new, timely insights as well as increased data literacy and an evolution from just reporting on “what” is happening in the business to understanding the “why.”

Self-Service Analytics Best Practices FAQs

Who is the ideal user for self-service analytics?

The ideal user for self-service analytics is someone who understands the value of data but does not have the technical expertise to manage and sift through huge data sets. In most cases, this is a business user, such as someone in marketing, sales, finance, supply chain, or manufacturing. Those types of users understand the potential provided by data; they simply need an easier way to analyze information to generate insights.

How is self-service analytics different from traditional analytics?

A traditional analytics process requires a business user to put in a request for a data set with a particular goal in mind. That request might sit in IT’s queue long enough that a business opportunity is lost. By moving to self-service analytics, that user can launch a tool, load a data set, define dimensions and parameters, and manipulate the data to see what types of insights, visualizations, and reports result.

What is the difference between structured and unstructured data?

Structured data comes with defined formats and nomenclature, such as a date field that specifies a YYYY-MM-DD format. Unstructured data does not have a set format.

An example of structured data is a medical insurance form with defined fields for customer account number and procedure and billing codes. Examples of unstructured data are an MRI scan, a doctor’s notes on a visit and treatment options. These will require the addition of tags and other metadata to describe the asset and provide context.

How do AI and machine learning help self-service analytics?

Artificial intelligence and machine learning (AI/ML) features can identify insights that traditional, rule-based analytics systems may miss. Machine learning algorithms get better at spotting patterns as they’re exposed to more data over time. This saves time for business users while opening the door to previously missed insights. As AI-powered analytics tools come online, users will be able to ask questions using natural language search and have the system select the right data sources to generate responses.

How can natural language processing support self-service analytics?

Natural language processing (NLP) when used in a self-service analytics platform lets people ask conversational questions and get back answers based on a given data set. NLP is comprised of natural language understanding (NLU) and natural language generation (NLG), both of which increase usability and accessibility of analytics. With NLU, the application can understand questions posed in natural language rather than using technical queries. This could be an HR staffer asking, What were the top five reasons people left the company last year? or a marketing pro asking, Which search-driven advertising campaigns delivered the highest conversion rates in the last six months? With NLP, the output can arrive in automatically generated reports for easy-to-understand summaries of insights and findings.