February 01, 2018

Business Intelligence On-Demand

For the past decade, the most prominent trend in business intelligence (BI) and analytics has been a move towards self-service. However, this year that’s changing. In 2018, industry experts expect to see a growing list of “smart” capabilities powered by machine learning and artificial intelligence, moving users beyond the limits of self-service.

Some capabilities are already available today, while others are starting to appear and/or are expected in the future. For example, natural language processing (NLP) querying based on keywords has been with us for years but some vendors are now using more advanced NLP capabilities that can distinguish nuances and intent in complete sentences. On the cutting edge, these systems are starting to retain the context of the queries, so instead of asking one isolated question, users can have a responsive dialogue with the data, drilling down and exploring from an initial query. 

Self-Service Analytics Evolves to ‘Smart’ Analytics


Source: Constellation Research 

BI > Data Scientists?

A new report by Gartner, produced by surveying more than 3,000 CIOs across the world, reveals that self-service analytics and business intelligence tools could possible produce more reliable outputs and data analysis than data scientists.

Gartner recommends high focus on 4 areas to make self-service analytics and BI reliable:

1)       Alight self-service initiatives with organizational goals and capture measurable and successful use cases

2)       Involve business users with designing, developing and supporting self-service

3)       Take a flexible and light approach to data governance

4)       Equip business users for self-service analytics success by developing an on-boarding plan

BI for CW Management

A Vendor Management System (VMS) is often the most appropriate system of record for contingent workforce (CW) management programs, but in some cases a lot of data is scattered across multiple corporate systems. Some VMS solutions enable seamless data-sharing between systems through one- and two-way data integrations, but many times these are difficult or expensive to implement or manage.

Thus, many program managers are looking for solutions to provide more comprehensive visibility into CW programs. When looking at evaluating business intelligence and analytics platforms, there are several points to consider:

  • BI applications have several growing capabilities with strengths and weaknesses. I.e. some are strong in data analysis and automations while others have dynamic dashboards and graphical displays.
  • Ease of use is becoming a larger factor, since often CW program managers need to access and operate the applications, as opposed to data scientists or IT experts. So it becomes important to consider ease of use capabilities that drive deeper adoption