Self-Service Analytics: How to Avoid the Pitfalls

Scaling AI Bhawna Krishnan

Self-service analytics ensures comprehensive coverage of an organization’s data initiative. It is bottom up and puts data in the hands of everyone in the organization, empowering them to use it to drive day-to-day decisions. While organizations that rely only on self-service analytics are agile and can answer quick, small-scale questions, they may lack focus on more significant business questions and problems that have the opportunity to make large-scale changes and projects driven by machine learning (ML) or AI.

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Incorporating self-service analytics into a more comprehensive data strategy can be challenging. Let’s say someone on your central data team is struggling to service many requests and prioritize the highest-value work. The idea that they can get other people involved is very attractive. 

The business users, though, need to find the time to do the work themselves without the expertise of the data experts, so it’s a hard sell to those stakeholders, especially because “self serve” as a concept doesn’t necessarily scream “positive value story” and might make them feel like they’re on an island. The following sections will focus on what it takes to bring self-service analytics to life in an organization successfully, through the lens of why they most often fail.

Efforts to Implement Self-Service Analytics Often Fail — Why?

To successfully execute self-service analytics in the enterprise, it is first valuable to understand why efforts to implement self-service analytics often fail. Self-service analytics can sometimes become expensive and not deliver enough value. This happens when:

  • Data access is an ongoing problem. Often, data exists and is centralized in a data lake, but most individuals need help accessing it because they need more technical skills or the proper access rights. Moreso, self-service analytics requires opportunistic use of data from a local database or even a spreadsheet, which can be challenging to cross-reference or use along with centralized data. Implementing a solution that doesn’t rectify this issue can render a self-service analytics system useless.

  • Self-service analytics tools and processes are not tailored to the users’ daily needs. When self-service analytics doesn’t suit its users, those users will often have to face a system that requires them to go through repetitive or complex steps to perform the analysis suited to their jobs and responsibilities. This can equate to users who don’t support the use of the system and who also might use it less and less if it’s not convenient for their workflows, slowing (or halting) adoption.

  • There is no confidence in the data. By far, the main reason self-service analytics efforts fall flat is a need for more trust in the data. This could be because data is not updated in real-time or because data and data formats change, but the company hasn’t devoted ongoing resources to self-service analytics platform maintenance. Or it might be because business users have access to datasets with no context on the data, where it comes from, or what it means.

    The problem quickly balloons when managers or executives don’t trust the results of projects completed using self-service analytics because of data quality or context issues. If self-service analytics systems are not adequately maintained and monitored, ensuring the data business teams are using is validated, contextualized, and in the proper format won’t be useful to the enterprise. It’s important not to confuse self-service with self-sufficiency — one cannot build and then forget self-service analytics.

  • There is confidence in the data … but there shouldn’t be. Perhaps worse than no data confidence is when self-service analytics users have confidence in incorrect data given to them and use it to create projects and deliver insights. IT must then validate (and continue to monitor) data provided through a self-service analytics platform to ensure continuous accuracy. The onus also falls on the self-service analytics platform users to ensure they understand the data they are using and ask questions about any doubts in quality or accuracy.

  • Data security suffers. Issues of data security and data confidence are partially related. Self-service analytics solutions often don’t provide a centralized (virtual) workspace that allows for control over who can access what datasets and prevents datasets from being downloaded locally and manipulated on employees’ local machines. This matters because, ultimately, self-service analytics projects can be shut down if they don’t have a solution for controlled, monitored access.

  • Self-service analytics is too centered around small data. Self-service analytics solutions built for small data are doomed to failure. Business users around the company who want to use self-service analytics should be able to efficiently work with large datasets in meaningful ways by applying ML models or doing predictive analytics projects. Too often, self-service analytics is limited to simple dashboarding without the inability to combine datasets to extract more meaningful insights.

Onwards and Upwards With Self-Service Analytics

Self-service analytics is not a new idea, and modern organizations still find it to be quite useful. The analytics industry as a whole is evolving, particularly in terms of how people are producing insights. The problem in the past was that creating insights required lines of business to filter and modify data products created by IT personnel or possibly to combine two of those products (e.g., "Show me sales split by region"). But as we move closer to what we call Everyday AI, more complex analysis and reporting will become standard practice, especially as more and more people gain the confidence to produce their own data products rather than relying on a central team. They desire greater ownership and control over the entire process of creating data products.

Although advanced analytics won't be essential for everyone in every business unit, the tools are now available for a much wider range of people to take on these challenges on their own, whether they are analysts who want to generate basic reporting improvements or ones who want to go beyond that and fulfill a citizen data science role and co-build models with data specialists.

Inadequate self-service analytics results in using the data that the data team provides in the self-service app or dashboard, even if people in lines of business may still have questions or require more details. Instead of repeatedly asking for the output to be revised in order to make it meet their business purpose or need, they will be able to work with the data team upfront. Additionally, the teams in lines of business have complete control over how the raw data is processed into the finished data product, and can view all the data sources that were used, and may adjust it.

Enabling team collaboration while assuring data integrity and support is how organizations may avoid the pitfalls associated with self-service analytics. This strategy not only gives the company more freedom to experiment on its own, but it also does so while minimizing the risks. Instead of throwing business people into the deep end with no assistance, data and IT teams should provide adequate direction and contingencies by giving them trustworthy tools and documentation to rely on for guidance. By doing so, involvement is encouraged from the initial stage through the execution of the project, which may give a better sense of ownership.

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