Data scientists are masters of wrangling large, unwieldy sets of structured and unstructured data into powerful, succinct strategic insights. However, turning raw data into actionable information is only half the battle. 

The other half? Getting the right data to the right person at the right time and in the correct format.

Despite the many advancements that have been made in recent years, adoption rates remain very low for data analytics dashboards and tools. According to our own research, 61% of companies say that less than 25% of their organization has access to data and analytics. Even amongst companies who make concerted efforts to democratize data, adoption rates often remain low. 

Are self-service analytics too good to be true? 

Similar to low-code automation, self-service analytics tools enable business users (even those with limited data science experience) to create their own BI reports with easy-to-use components like drag and drop. The goal of these platforms is to make data and analytics available to everyone across an enterprise. By allowing users to direct their own data science projects, the hope is that they’ll unlock previously untapped strategic insights with nominal help from IT. 

Very generally speaking, effective self-service tools typically incorporate the following 5 components:

• Semantic layer — enabling business users to articulate relevant data queries across multiple datasets in a clear-cut way
• Strong role-based access control — ensuring the proper level of access rights to the right people
• Data governance — a framework for managing the availability, usability, integrity and security of the data in enterprise systems
• Customization — allowing business users upload their own data and build their own models
• Prototyping support — enabling users to clone existing analytics visualizations, dashboards and environments to prototype new capabilities

However, despite their promise, self-service analytics tools and applications have a number of pitfalls.

To start, just because a tool does not require software coding and data processing knowledge to operate, doesn’t automatically mean it’s easy to use. In fact, most well-known self-service analytics tools require weeks of training. Even after a person is trained, some of these tools can still be very time consuming to use. One major issue: slow data processing and query speeds. 

Secondly, though these tools don’t require data science expertise, they do require data literacy skills. Putting data in front of a person isn’t enough, they have to have the skills necessary to interpret, analyze and action the data. 

Embrace User-Centered Design

One of the misconceptions about low-code technologies such as self-service analytics is that it actually requires more close collaboration with IT than traditional methods, not less. Technical architects and non-technical users must work closely together to design the tool to ensure it is not only user-friendly and aligns with business needs, but is also in line with an organization’s larger business objectives. 

As such, many data engineers and scientists are embracing user-centered design (UCD) techniques. As the name suggests, the goal of UCD is to optimize the product around how users can, want, or need to use the product so that users are not forced to change their behavior and expectations to accommodate the product. It also takes into account everyone that uses or is impacted by a service including the employees who build, maintain or rely on it. 

According to usability.gov, the various steps or phases involved with UCD are:

• Specify the context of use. Identify the people who will use the product, what they will use it for, and under what conditions they will use it.
• Specify requirements. Identify any business requirements or user goals that must be met for the product to be successful.
• Create design solutions. This part of the process may be done in stages, building from a rough concept to a complete design.
• Evaluate designs. Evaluation - ideally through usability testing with actual users - is as integral as quality testing is to good software development.

Become a AI Data Analytics NETWORK member today!

Premium content. Expert insight. Instant access. All for FREE.