Self-service analytics seems like an obvious way for organizations to scale busy data teams. In most companies access to data is a fairly significant bottleneck, so opening up data to everyone is an attractive option.
The Rise of Self-Service Business Intelligence
The popularity of self-service analytics software is in large part due to the increased demand across all business roles. But how can companies successfully use self-service analytics in businesses? And are they able to maximize their potential to create a positive outcome for their companies?
According to Gartner, more companies will have access to the data they need for business and analyze it in the next few years. Companies will need to invest in self-service tools to help their employees analyze data just to keep up.
Challenges with Self-Service Analytics for Businesses
Even though democratizing access to internal data can have huge payoffs, making the transition can sometimes present challenges.
Data Quality When Transitioning to Self-Service Tools
A company first needs the capability to sustain broad usage of self-service tools.
According to Daniela Sawyer, Founder and Business Development Strategist of FindPeopleFast.net, "Since the process involves more manual work like transferring data from the particular folder to our system then processing with the proper tool followed by KPI analysis, this often becomes frustrating by doing the same manual work again & again. It also lets us down when the process gets interrupted due to system hang or connectivity issues."
Educating Business Users in Your Organization
Self-service analytics primary goal is to empower non-IT employees by using self-service analytics tools. Organizations need to educate their in-house analysts and coordinate it with other team members to understand better the data they collect and store.
If the organization has been used to manual reporting, or accessing data via requests to the data team, employees may have to be flexible in how they get access to data. In particular, as the data team transitions to supporting self-service tools instead of ad-hoc requests, employees consuming data will need to pick up the new tools or be left behind.
According to Daivat from Force by Mojio, "[Self-service] has been a useful way to bring data into our leadership decisions without requiring constant access to people with well-developed coding and analytics skills to translate our findings. The main frustration with self-service is that no matter how good the platform you're working with is, there will pretty much always be a knowledge gap, and the platform itself will take time to learn."
The success of your organization will come from utilizing the right self-service analytics tools that fit the need of your company and with the proper training and teamwork to maximize coordination with your teams to use the technology.
April Maccario, CEO of Ask April, mentions that "This challenge with this is the need for sufficient knowledge and training for the management to understand and manage to interpret the results. Of course, even if it is made for self-service, appropriate information and the right instructions should still be given to users. It can't be expected that all people know how to understand and interpret analytic results with nominal IT support."
Ensuring Valuable Data Quality
As far as ensuring that the data your organization collects help you with business making decisions, you and your team of analysts need to make sure that the information you have will be helpful to your company's growth.
Data collected is only as good as your people's and technology's ability to understand and interpret it. To get the information and insights that are valuable to you, it's crucial to build coordination and knowledge to understanding and using the data to your leverage. When people can see the quality of your data, it will be easier to discard information that is just taking up space.
The CEO of Pixoul, Devon Fata, has experienced having a hard time "getting reliable data from some clients, and also struggle to separate signal from noise: what are design elements I can control, and what are the results of my client's decisions after the site went live?"
At the end of the day broader access to data won't be helpful if the data isn't high-quality to start with. As a general rule, any problems with data infrastructure or process will be worse with broad adoption of self-service tooling.
Another challenge that needs to be considered is there will be times that your company’s data won’t be understandable to a person that’s not a data expert. If not addressed, it can lead to wasted time, money, and effort that will affect your data quality.
Martin Luenendonk, CEO of Founder Jar, says that "Self-service tools force enterprise customers to spend lots of time sifting through data, but a typical employee can't be trusted to reliably self-identify the intricate patterns in that data that could be a crucial early indicator or even nothing."
This means there's a fine balance between making data available to everyone, and just leaving everyone to their own devices. The data team will still be deeply involved in doing in-depth analysis for the organization overall, supporting core company KPIs, and acting as a resource to help understand and put into context the data exploration of other team members.
Embracing the Challenges of Self-Service Analytics
Whether you're still thinking about shifting to using self-service analytic tools or you're already encountering these challenges, there are many ways where you can overcome these and create opportunities for your and your organization to thrive with the use of this technology.
Self-Service Data has come a long way in adding value to the business decision-making of many companies. And like any other innovative technology out there, it has a lot of potential and benefits to help businesses thrive.