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Q&A: Agile and Self-Service BI

Lyndsay Wise of WiseAnalytics explains the connection between two important business intelligence technologies.

[Editor’s Note: Lyndsay Wise, president and founder of WiseAnalytics, will lead the session The Promise of Self-Service BI and Agility: Separating Fact from Fiction at the TDWI World Conference in Las Vegas February 23-28, 2014. Here she gives us a preview of key points from her presentation.]

BI This Week: What overlap exists between self-service BI and agile BI?

Lynsday Wise: Many organizations look at both increased agility and self-service access when trying to develop more efficient BI. Both fit together to create a complete solution for businesses. What this means is that self-service BI represents the way in which people interact with BI, whereas agility addresses interaction with technology and data.

For instance, self-service BI brings analytics to the hands of anyone who needs to access BI by making solutions more interactive and user friendly. Agile BI enables data access when it is required through the development of an infrastructure that supports real-time, diverse analytics access, etc.

What are the circumstances that allow one to exist without the other?

Developing a holistic approach to BI requires looking at both IT infrastructure and front-end BI use. However, many organizations do deploy BI at the departmental level, meaning that BI architecture takes a back seat and the focus is on capabilities and ease of use. This means implementing self-service BI independently of agile BI. Although data is always important, for these types of deployments, self-service will exist without looking at agility.

The opposite can also be true. For organizations looking at big data implementations or advanced analytics, there will be a focus on developing an infrastructure that can support agile delivery of data. This means looking at a set of solutions that support large and complex data storage, real-time information delivery, and complex calculations without addressing how information access will be achieved.

What are the key requirements for developing an agile BI environment?

BI agility requires a flexible infrastructure. This involves looking beyond traditional BI architectures that only enable batch processing of data or limited amounts of data storage. Organizations must be able to stream data in real time (or “right time” depending on the needs of the business), enable processing of large and complex data sets, consolidate multiple types of data, and gain quantifiable value from their analytics use.

Agile BI may look different within organizations depending on the needs of the business, but the overall premise should apply within any organization by providing flexible access to information assets and support to the types of analytics and data access required for decision support.

Which types of BI infrastructures are more conducive to agility and why?

Big data is a huge trend in the market place right now. The benefits of big data adoption include the consolidation and maintenance or large, varied, and complex data sets. All of these data types are the kinds needed within a BI infrastructure.

Agility requires easy access to this type of data, with the added ability to develop additional analytics. Although some organizations choose to use their big data platforms as the access point to BI, others look at big data platforms as a data source to their BI environment. When this is the case, the BI platform itself needs to offer the flexibility to deliver data that supports business requirements. Options include:

  • Cloud BI platforms offer flexible storage and delivery and, in some cases, big data storage benefits built-in
  • Appliances provide plug-and-play feel with targeted delivery goals depending on the options selected as well as the option to scale.
  • Analytical databases enable organizations with complex analytics requirements to select a data warehouse offering a mix of advanced analytics capabilities with flexible delivery and access points.

What are the top three considerations for self-service BI environments?

Here are three important considerations when looking at self-service BI:

  1. Gaps in current environment: Organizations need to realize that BI use does not automatically translate into self-service BI interactivity. Self-service BI means letting business users guide their analytics experience by letting them look at information the way they want in the way that best meets their needs. Therefore, it’s important to make sure that the current solution can support this type of design and delivery or that a complementary solution can be deployed.

  2. Validity of use: The challenge with self-service delivery is that there is a risk of giving business users access to data in a way that enables them to create any set of analytics -- whether they are valid or not. It is important to develop a way to verify the accuracy of analytics being created.

  3. Understanding of user needs: Different users have different needs. Developing an approach that looks at how users will interact with BI is key to making sure that development matches self-service capabilities.

How do we account for different types of users? When should we build one set of dashboards with different access points versus multiple dashboards to meet the needs of different users?

The answer to this question really depends on the individual needs of the organization and the types of users who will be interacting with the solution. In some cases, one set of dashboards with varying levels of access will work, whereas if both power users and casual business users are going to be developing their own self-service interactions, developers may want to deploy different dashboards that reflect the needs of both of these groups.

For instance, casual business users require guidance and flexibility with data connections already being made. On the other hand, data scientists, power users, business analysts, and similar users need to be able to make the data connections they require and interact with data in a more complex way. In some cases, developing a single set of BI access points will limit this type of interactivity.

What are the key considerations to take into account when developing self-service for current use while being open enough to support future expansion and development?

In many cases, organizations develop self-service interactivity at the departmental level. A business pain is identified and businesses need to give casual BI users broader access to information in an easy way so they can address issues proactively as they occur.

The success of these types of applications generally leads to expansion of use and adoption more broadly across the organization. Consequently, organizations need to make sure they take future scalability into account by understanding data, users, and the implications of each. This means:

  • Making sure that additional data sources can be integrated easily
  • Identifying current data storage requirements and building a solution that supports future expansion/growth
  • Understanding that adding users can affect licensing and support structures
  • Making sure product capabilities can meet the needs of a more diverse audience
  • Understanding that different users and departments have different BI goals that might not be able to be accounted for within one solution

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