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What are pros and cons of self-service data analytics tools?

by Daniel J. Power
Editor, DSSResources.com

Many managers and employees want more direct access to proprietary organization data. Vendors promote more direct analysis and offer to sell tools to provide the access. The fundamental issue is who should have access to what data and what analyses and tasks can and should an individual authorized user be able to perform. Investigating this broad question should highlight more specific pros and cons for a given organizations. More access is not necessarily an answer to gaining more value from expanding data sources. This column discusses the pros and cons more generally which should help guide the debate on data access and self-service analysis in a specific organization. One size does not fit all.

According to Pal (2016), "Self-service analytics can be defined as a simple form of business intelligence (BI), where business users are empowered to access relevant data, perform queries and generate reports themselves with the help of easy-to-use self-service BI tools. The entire self-service process is simplified or scaled down for better usability. The purpose of self-service analytics is to enable business users to perform their day-to-day analytics tasks themselves ..."

Pal argues that self-service analytics will have a number of advantages including: 1) Democratization of big data, 2) Leverage the power of analytics by empowering business users, 3) let Data science team concentrate on the core analytics tasks while business users perform less intensive tasks, 4) Improve productivity. Business users can help themselves. Risks Pal discussed include: 1) Lack of proper training, 2) Skill limitations of business users, 3) Not checking for data errors, 4) Data inconsistency, and 5) Lack of proper data governance.

Boost Labs staff (2017) note "Self-service business intelligence is simply accessing and gathering your own data using business intelligence (BI) tools. BI tools are used to pull, analyze and visually display data for decision makers. Common BI tools include Domo, Tableau, Zoho, SAP Crystal Reports and Microsoft Power BI." The Boost Labs comentator is concerned that with self-service BI the "data story is shallow, incomplete, confusing, and sometimes inaccurate".

Self-service BI and analytics is not a recent development. In 2005, Stephen Swoyer discussed the pros and cons of self service Business Intelligence. Swoyer noted "IT pros question the wisdom of putting too much power in the hands of users." The major objections Swoyer heard from IT pros were inefficient queries and compromised security concerns.

Steve Walsh (2008) also addressed the issue of giving business users more access to data. Walsh argued "Most business users donít have access to the data they need to make better decisions ..." He noted the "problem is not just the complexity of BI tools, but the need to understand the data ó where itís stored, what itís called, and how itís structured". His solution to both problems that limit access was creating "a natural language processing (NLP) layer that maps and translates the conceptual view of an average business user to the logical view of the structured data, while at the same time surfacing reporting assets ..."

In a discussion of a 2016 survey of 560 organisations, an Aberdeen Group analyst noted "Self-service analytics consume fewer IT resources and reduce project completion time and residual work ... before anybody gets carried away with the numerous benefits of self-service, it is important to talk about data governance. Ö Too many users are running wild and engaging data as they please.Ē

So the "big picture" pro arguments are 1) better and faster access to data and 2) more data analyses. The con arguments are 1) the cost for training and 2) data misuse. Self-service does not eliminate the need for more sophisticated data analyses by professional analysts and data scientists. Be wary of GIGO, garbage in and garbage out.

References

Aberdeen Group, "The pros and cons of self-service analytics," Press Release, July 29, 2016 at URL http://dssresources.com/news/4833.php

Boost Labs Staff, "Pros and Cons of Self-Service Business Intelligence Data Stories," March 17, 2017 at URL http://www.boostlabs.com/pros-cons-self-service-business-intelligence-data-stories/

McCafferty, D., "How Businesses Benefit From Self-Service Analytics," CIO Insight, 09-01-2016 at URL http://www.cioinsight.com/it-strategy/big-data/slideshows/how-business-benefits-from-self-service-analytics.html . Also see https://hbr.org/resources/pdfs/comm/alteryx/19817_HBR_Alteryx_Report_6.pdf

Pal, K., "Advantages and Risks of Self-Service Analytics," KDnuggets, April 2016 at URL http://www.kdnuggets.com/2016/04/advantages-risks-self-service-analytics.html

Swoyer, S., "The Pros and Cons of Self Service Business Intelligence," TDWI, 8/10/2005 at URL https://tdwi.org/articles/2005/08/10/the-pros-and-cons-of-selfservice-bi.aspx

Walsh, S., "Lesson from the Experts: Giving Business Users the Power to Make Data Actionable," TDWI, October 29, 2008 at URL https://tdwi.org/articles/2008/10/29/lesson-from-the-experts-giving-business-users-the-power-to-make-data-actionable.aspx

Last update: 2017-10-09 01:20
Author: Daniel Power

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