Self-Service Analytics

Turning Big Data into Big Insight

Self Service Analytics Overview

Why is Self-Service Important?

Only when users can access AND trust their data sources are they able to build competitive advantage, make key business decisions, and deliver business insight.

The benefit of users accessing AND trusting their data sources is that empowers them to use their data to build competitive advantage, make key business decisions, and deliver business insight.

According to an HBR study, lack of data credibility can result in up to a 50% loss of user productivity. Data must be available, searchable, trustworthy, and usable by any users charged with making data-driven decisions. However, data must be accessible only by those who have the rights to do so to maintain confidentiality and comply with internal and governmental regulations.

Get Ahead of the Problem

The challenge ahead is that data is not stagnant. Every market forecast includes a significant growth in the amount of data we use and store in the foreseeable future. Self-service analytics tools require machine learning and artificial intelligence to sift, tag, manage, and analyze this huge growth of new data without compromising privacy or security compliance. Empowering self-service from an automated, AI-based data catalog makes data more usable, while giving users data governance peace of mind.

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Key Attributes of Self-Service Analytics Tools

Powerful

Marrying data lineage, metadata management, and rationalization through an AI and machine learning framework

Intuitive

Promoting greater data usage by offering an interface that is both intuitive yet powerful

Responsive

Make better and faster decisions and derive competitive advantage

Self-Service Analytics and Governance

All company data, including siloed data from different systems, must be paired with other relevant information regarding the data’s journey, such as where it’s used, how it’s used, its security rights, relationship with other data, etc. This is where data governance is critical.

Data governance is an automated framework for defining and managing policies throughout the organization. It ensures that users can consistently access high-quality data by understanding the data’s lineage, ownership, ratings, terminology and peer reviews in a collaborative environment. Equally important is information security governance. Self-service analytics doesn’t mean everyone gets access to all of an organization’s data. In fact, because more people are accessing data, the need for setting data permissions and guidelines becomes even more important. It is imperative that sensitive data needs to be secured and access limited to those who are expressly given permission to see or use specific data.

An End-Around on User Pushback

After data is consolidated, tagged, sorted, reviewed, and collaborated on, a human nature component may present a barrier. Organizations with employees who fight to maintain departmental data silos because they fear that a global data perspective will have negative consequences on their roles may need to change tribal knowledge mindsets through a more formal change management strategy. It’s often the only way they know how to facilitate an organizational shift from “tribal knowledge,” or “the old way of doing things,” to data amalgamation.

However, by building a self-service analytics tool like the Waterline data catalog, organizations can assuage user fears through a better user experience, deeper and more trustworthy insight, and governance protocols that protect data access permissions in place and in motion.

Data Catalogs Democratize Data

Data catalogs remove the complexity of a data lake and make data palatable for business and technical users. AI-driven data catalogs are agnostic because they look beyond business units and departments, schemas, terminology, and systems. From an organizational perspective, they deliver a uniform look and enforce data governance for all data regardless of its creed. From a user perspective, an AI-driven data catalog delivers a new tool and new data for them to make better decisions. These types of data catalogs also provide a collaboration point for data users to share their experiences, and review and comment on different data sets to help others in the organization navigate the myriad data sources. It’s a unique combination of analytics, crowd sourcing, and organizational discipline (through governance).

The Bottom Line

Enabling users with self-service analytics in an AI-driven data catalog:

  • Makes all data available while managing access permissions on sensitive data
  • Unifies data from disparate sources and siloes
  • Results in more accurate insights based on trustworthy data
  • Provides a streamlined user experience for easier search and analysis
  • Overcomes organizational fears that have stranded data in siloes for too long

 
Read more about how Waterline data catalog improves productivity while increasing efficiency and accuracy of self-service analytics.

 

Resources

Ultimate Guide to Data Catalogs

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Data Catalogs for Analytic Self-Service

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