When Self-Service BI Actually Means Self-Service

When Self-Service BI Actually Means Self-Service

Self-service should be an approach to data analysis that grants users the access and ability to work with the information they need to analyze without depleting IT resources and time.

Chris Neumann is Founder and Chief Product Officer for DataHero.

Ease-of-use is paramount for data analysis in today’s fast moving, data-driven business landscape. However, the assumption tied to Business Intelligence (BI) is that the tools are designed solely for analysts, and are rarely consumable by the typical business user. To debunk this perception, companies are largely advertising their solutions as being self-service. But how much credibility do these claims hold? How do we truly help the majority of the business population that needs to work with data, but aren’t trained to do so?

The term “self-service” is most often used for marketing speak but fails to live up to the hype. Self-service should be an approach to data analysis that grants users the access and ability to work with the information they need to analyze without depleting IT resources and time.

We live in an era where applications don’t require users to be experts – take Dropbox and Survey Monkey as examples. Anyone can sign-up and intuitively navigate through these tools and get value immediately. As a result, companies are able to significantly improve both internal operations and how they conduct business externally. Each person is able to accomplish more in a shorter period of time, because the services at-hand don’t require training or a strong understanding of the technology.

Despite this trend, data solutions have lagged, still requiring users to be analysts or data experts, even though everyone now has access to data from a variety of sources. Traditional BI solutions work with data stored in a data warehouse but ignore the data outside of the centralized data system. The issue with this approach is that data not only lives in warehouses, but also lives in services business users rely on everyday – like Microsoft Excel, Google Analytics, Salesforce, HubSpot and more. Why not empower the “average Joe” to derive value from the data no matter where it sits? Digital marketers, sales associates and customer reps make up only a snapshot of the professions that can use data to improve their jobs and make important data-driven decisions.

Cloud software is overwhelmingly departmental in nature. Often times, different departments’ requests for reports from IT may not take priority, particularly for smaller teams. When using traditional BI tools, it is difficult to justify the cost and time that is required for IT to pull data from cloud services to analyze specific data sets for an individual department.

BI solutions need to rely less on human intervention, and more on automation to eliminate the common pain points that deter business users. Solutions that normalize and classify data automatically from disparate cloud services eliminate custom ETL (extract, transform, load) and ease the burden on IT. By automating ETL and leveraging machine learning classification engines, self-service data analytics becomes a reality. This, in turn, empowers everyday business users to make sense of their data.

The more self-service tools on the market, the less time IT spends on responding to ad-hoc requests from individuals or departments. As a result, IT teams can focus their efforts on effective strategic IT planning and oversight of performance.

In reality, data analysis shouldn’t be difficult. Most of us know the questions we want answered; what we need are solutions that are truly self-service. Luckily for businesses, solutions like Dropbox are paving the way for self-service to become more of the norm across all types of tools and applications. Now, it’s time for companies to view data analysis in the same light.

Industry Perspectives is a content channel at Data Center Knowledge highlighting thought leadership in the data center arena. See our guidelines and submission process for information on participating. View previously published Industry Perspectives in our Knowledge Library.

Hide comments

Comments

  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Publish