Self-service analytics lowers skill barriers for data use but does require training and financial investment. Evaluate the pros and cons before deciding if it's the right fit.
Data teams no longer handle every data request. Rather, they facilitate the democratization of data across an organization. They manage the risks of putting self-service analytics into the hands of all users to enable real-time, data-driven decisions.
Self-service analytics is a type of BI tool that allows users to manipulate and visualize data. Self-service BI tools typically feature user-friendly interfaces that are suitable for non-technical users, so anyone can extract insights from data and discover actionable next steps.
Selecting and implementing a self-service application is a more complex decision than it appears. Placing the data into the hands of everyone presents its own risks, especially privacy and security concerns. Weigh the pros and cons of self-service analytics to determine if self-service analytics is the best tool to improve your decision-making.
Self-service analytics pros
Self-service analytics provides a variety of benefits and competitive advantages that make data more accessible and easier to work with.
Democratizes data
Traditional analytics tools require the expertise of a data team to be used effectively. For example, a business user submits a request to pull or analyze certain data; a data scientist or analyst completes the request and sends the results in a digestible format to the business user.
Self-service analytics enables business users to run data analyses on their own. The data team prepares much of the work for self-service tools in advance, such as integrating data sources, sanitizing data, formatting premade data sets and preparing data pipelines. Business users can query and automatically pull data into the analytics software on their own to transform it for their needs. They do not need to wait for the data team to process the request and return the results.
Self-service tools also provide a variety of preformatted templates for data visualizations. For example, a business user can transform data into a chart, graph, report or interactive dashboard. Self-service functionality lowers the skill barrier for data analytics, allowing anyone across the organization to engage and interact with data.
Provides real-time insights
Self-service analytics connects users with real-time data streams. Access to real-time data empowers users to make more timely, accurate and relevant decisions. They can explore patterns and trends during data collection and make real-time course changes if necessary. In a traditional analytics system, the data team must parse information first. By the time they deliver results, the data might not be up to date.
Self-service tools can integrate and automatically sync with real-time data, creating a more responsive and agile decision-making environment. Business users don't need to go through any extra layers of a data workflow. They can simply refresh their data and visualize it immediately.
Self-service BI puts traditional BI elements into the hands of all users.
Supports scalability
Traditional analytics tools have limits depending on the size and bandwidth of the data team. Self-service analytics reduces team limitations by automating tedious tasks, such as data requests. Automation frees up the data team to focus on more strategic priorities, such as improving data delivery speed and streamlining data pipelines.
Self-service tools can often support as many users as needed. As an organization grows, it's easier to integrate new data sources and scale operations with self-service analytics infrastructure than traditional tools. Many self-service tools make it simpler to start small and ramp up as needed over time.
Self-service analytics enables the entire business to access, visualize and learn from data at scale. No one department must fulfill everyone's data needs; all users can make informed decisions on their own. It helps avoid bottlenecks, leading to increased productivity and more impactful actions on a larger scale.
Allows customization and personalization
Self-service analytics tools enable users to explore a variety of data visualization formats, from graphs and charts to templatized reports and even fully customizable dashboards. Each user can create the unique visualization they want. They can easily plug data into different formats and explore what works best visually.
Personalized data is better at conveying information to the user and different audiences, such as clients. Everyone learns differently, and having the flexibility to customize data as needed can help them digest information.
Tailoring insights also engages users. When business users feel like they have control over a tool, they are more likely to use it regularly. For example, someone might find it useful to generate automatic reports at the end of every month or create a data dashboard they can check every morning. The more flexible a tool, the more utility users can get out of it and adapt their analytics use to meet job-specific requirements and preferences.
Encourages experimentation
The freedom of self-service analytics encourages users to engage with data more often. Business users experiment more often when they have easy access to data analytics. Instead of only receiving data reports upon request or on a strictly recurring basis, they can run analyses based on whatever data suits their needs whenever they want.
For example, consider an employee planning to pitch an add-on service to a customer. Ahead of the meeting, they can run a quick analysis of how the service affects other customers in the same industry. In a matter of minutes, they can have an easy-to-understand visualization for the customer and use the data-backed insights to better convey the value of the add-on.
Improves data governance
Self-service analytics tools can simplify data governance. Self-service analytics and governance require data teams to clean, preformat and prepare data to use self-service tools. During the preparation process, data teams can set strict user permissions and apply governance policies.
Handling data preparation and governance simultaneously can create a smoother road to compliance. It ensures that only accurate, reliable data can enter self-service pipelines. Data teams can conduct data audits, identify anomalies, protect sensitive data and adhere to regulations as needed.
Self-service analytics cons
The lower skill floor for self-service analytics does not mean employees will not need training or development. Self-service analytics also increases the risk of security breaches and reinforces the importance of governance.
Requires training
Self-service analytics has a low barrier to entry, but business users might require training or support to use the tool. They need a short demo or tutorial, and data literacy training, which is key to understanding what data to pull and how to interpret results.
A basic level of data literacy helps a user identify when the next steps don't quite make sense and how to extrapolate findings beyond the initial outputs. It also takes some skill to understand how to visualize data and what formats work for certain scenarios, such as communicating data insights to others in the organization rather than clients.
Users who don't understand the context behind data can make inaccurate assumptions and incorrect conclusionswhich can harm rather than help decision-making.
Needs employee buy-in
Self-service technology might intimidate business users and cause them to avoid it. The primary goal of self-service analytics is to expand the power of data to the entire organization, but if too many users aren't engaging with it, the value goes down.
Management must demonstrate how analytics affects daily work. They should explain to users how they can make the most of the tool using demos or dashboard showcases, highlight which features are easy to use and provide ideas about how they can check data on a regular basis.
Good data-driven culture integrates data into the decision-making process. Higher user engagement increases the utility of self-service analytics, fostering more informed decisions at scale.
Increases security and privacy risks
Increasing the number of people who can access data opens it up to more risk. Not all business users have stringent data security training, and they might unintentionally increase the risk of breaches or leaks. For example, someone can accidentally share information with the wrong person or store downloaded data in places that aren't secure.
Self-service tools require strict protections, starting with access controls. The best self-service analytics tools allow IT administrators to set user permissions down to a granular level. Only authorized users can access the right data at the right time in the right place.
It's also worth asking how an analytics vendor encounters your data, if at all. How does it store or access data? Does it integrate with other software or share data with third parties in any way? The answers help your security team understand the risks they're facing and develop security measures to better protect data.
Requires data preparation
Self-service analytics does not mean self-setup. It might seem like you can simply integrate with an analytics tool and immediately start pumping out valuable visualizations, but that's not the best idea. The data team must first clean the data, which includes preparing, cleaning, processing and modeling data.
Self-service analytics does not mean self-setup.
Poor data preparation can cause business users to engage with raw data or try to pull in data that's not right for their simulations. It can cause users to derive inaccurate insights and make poor decisions as a result. It can also put sensitive data at risk.
The data team should prepare and preformat data for analytics processing. Doing so ensures the system pulls from correct data sets and streamlines the analytics process overall, which improves data integrity and delivers better performance.
Complicates data governance and compliance
The proliferation of data analytics and data sources within an organization can complicate data management and governance. Data preparation is only one piece of the governance puzzle. Organizations must ensure data is trustworthy, maintains its integrity and is consistent across systems.
To prevent data errors and ensure accuracy and consistency across business units, organizations must establish policies for data quality, collection and usage that adhere to data privacy laws and other relevant regulations. Data laws are evolving with the rapidly growing field of data analytics -- especially as AI integrates further into analytics applications. Keeping up with such standards requires frequent reviews and updates.
Creating and enforcing data governance infrastructure is difficult and time-intensive, but it will help ensure the data foundation is secure and reliable.
Demands upfront and long-term investment
The aforementioned factors require a certain level of investment. Some self-service tools might offer a free version, but most organizations will need to invest in business versions of the software to unlock its full potential. In addition to vendor costs -- which can vary from paying per seat for a wall-to-wall tool or on a subscription basis over the long term -- organizations must also account for user training, governance compliance, security management and data preparation costs.
In short, self-service analytics requires upfront investment. The amount depends on the size of the organization and its industry. User engagement and utility matter to determine if it's worth the cost. To achieve ROI in the form of competitive advantages, employees need to engage with the tool and use it regularly.
Jacob Roundy is a freelance writer and editor specializing in a variety of technology topics, including data centers and sustainability.
