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3 Best Practices for Becoming More Self-Sufficient with Self-Service

What do you need to support analytics democratization? How can your organization help users become more self-sufficient? Here are three technology-focused best practices that can help.

My colleague Dave Stodder and I recently lead a Webinar in conjunction with our best practices report about becoming a data-driven organization.  Audience questions included several about self-service. In particular, attendees were interested in how to make self-service more accessible to managers and leaders in their organization. 

Let me set the stage for self-service analytics usage -- specifically for visual analytics and data preparation.  On the one hand, most organizations regard the move to self-service as an important analytics strategy for becoming data-driven.  On the other hand, although many analysts are able to perform self-service data access and visualization, we see that self-service analytics is not that widely utilized across organizations (e.g., by everyone in the organization). 

For example, we see in our research that although the vast majority (typically about 75 percent) of respondents to our surveys are using self-service visualization tools somewhere in their organization, these tools are (on average) less than 20 percent utilized by members across the organization.  Additionally, in our most recent best practices report, about one quarter (25 percent) of respondents said that users could perform data preparation steps, including for data quality, consistency, and transformation on their own. Just 19 percent can integrate or blend data on their own. 

This was an area of dissatisfaction among respondents. Further, just 10 percent of respondents said that users can access and query data on Hadoop, Spark, or other big data platforms in a self-service fashion. This speaks to the general lack of experience in enabling users to have direct access to big data from BI and visual analytics solutions; mostly, it is data scientists and analysts with programming experience who access and interact with big data.

This data suggests that there is a problem with users in an organization being self-sufficient enough to perform self-service analytics on their own.  Although some executives may not want to be self-sufficient and would prefer to get the insights they need from their teams, other managers and executives do want to be self-sufficient. What is needed to support democratization among users for analytics?  How can organizations help their users become more self-sufficient?  Here are three technology-focused best practices that we discuss in the report.

Best Practice #1: Build a solid data foundation

Self-service analytics for most business personas requires a reliable, accessible, and trusted data source that doesn’t require much manipulation. Good data quality along with the ability of users to easily access data are key to helping make users more self-sufficient. 

For some organizations that might be a data warehouse.  For others, it might be a governed data lake that contains layers of data for different personas.  It might include a multiplatform environment that provides an integrated view across data sources.  In general, management of this platform falls to IT with collaboration from the business. If the data foundation is shaky, the whole self-service house falls apart. 

Best Practice #2: Investigate modern data preparation tools

Data preparation processes are often slow, tedious, and hard for those who are not that technical -- especially if data is siloed and of poor quality, hence Best Practice #1. For organizations to use data to drive decisions effectively, data preparation processes must take advantage of trends toward smarter and more automated technologies that embed more advanced analytics, such as machine learning into the tool. Organizations should evaluate new data preparation technologies, including self-service data preparation to support self-service BI and visual analytics. 

Best Practice #3: Provide tool training

Organizations need to help personnel develop skills such as how to use tools, engage in critical thinking, and communicate results.  This means training. Whether conducted in-house of via a third party, training is important.  Some organizations offer this through their Center of Excellence, which can be an effective way to help your organization disseminate information, provide training, or maintain governance. 

Experts in the center can help to perform analysis as well as train users.  They can help make other decisions.  For instance, uses are executing data preparation processes their own way, data chaos could increase. As organizations evaluate self-service data preparation solutions, business and IT leaders need to sort out which data preparation activities users can safely and productively perform themselves and which ones a centralized IT function should execute.  A center of excellence or governance committee composed of business and IT stakeholders can be a good forum for such a discussion.

A Final Word

These are but a few of the technology keys best practices to follow to improve your self-service.  Just as important are the organizational best practices that can help drive a culture that embraces self-sufficiency. 

Self-service is covered in detail at our Las Vegas conference in February. Whether you’re looking for training to help you better perform self-service analytics or you’re looking for best practices to broaden the adoption of self-service in your enterprise, this conference can help. You may find the session Self-Service Analytics: Organizational, Architectural, and Governance Success Factors of particular interest.

About the Author

Fern Halper, Ph.D., is well known in the analytics community, having published hundreds of articles, research reports, speeches, webinars, and more on data mining and information technology over the past 20 years. Halper is also co-author of several “Dummies” books on cloud computing, hybrid cloud, and big data. She is the director of TDWI Research for advanced analytics, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and “big data” analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her at fhalper@tdwi.org, on Twitter @fhalper, and on LinkedIn at linkedin.com/in/fbhalper.


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