One of the most valuable aspects of self-serve business intelligence is the opportunity it provides for data and analytical sharing among business users within the organization. When business users adopt true self-serve BI tools like Plug n’ Play Predictive Analysis, Smart Data Visualization, and Self-Serve Data Preparation, they can apply the domain knowledge and skill they have developed in their role to create reports, analyze data and make recommendations and decisions with confidence.
It is not uncommon for data shared or created by a particular business user to become popular among other business users because of a particular analytical approach, the clarity of the data and conclusions presented or other unique aspects of the user’s approach to business intelligence and reporting.
In fact, in some organizations, a business user can get a reputation as being ‘popular’ or dependable and her or his business intelligence analysis and reports might be actively sought to shape opinion and make decisions. That’s right, today there is a social networking aspect even in Business Intelligence.
Think of it as Social Business Intelligence or Collaborative Business Intelligence. It is a new concept that we can certainly understand, given the modern propensity for socializing and sharing information that people want to share, discuss, and rate and they want to understand the context, and views and opinions of their peers and teammates.
By allowing your team members to easily gather, analyze and present data using sophisticated tools and algorithms (without the assistance of a programmer, data scientist or analyst), you can encourage and adopt a data sharing environment that will help everyone do a better job and empower them with tools they need to make the right decisions.
When considering the advantages of data popularity and sharing, one must also consider that not all popular data will be high-quality data (and vice versa).
So, there is definitely a need to provide both approaches in data analysis.
Create a balance between data quality and data popularity to provide your organization and business users with the best of both worlds.
You may also wish to improve the context and understanding of data among business users by leveraging the IT curation approach to data and ‘watermarking’ (labeling/tagging) selected data to indicate that this data has been certified and is dependable.
Business users can then achieve a better understanding of the credibility and integrity of the integrated data they view and analyze in the business intelligence dashboard and reports.
As the organization builds a portfolio of reports and shared data it can better assess the types of data, formats, analysis and reports that are popular among its users and will provide more value to the team and the enterprise.
Encourage your team members to share their views and ratings, with self-serve data preparation and BI tools and create an environment that will support power business users. While self-serve data prep may not always produce 100% quality data, it can provide valuable insight and food for thought that may prompt further exploration and analysis by an analyst or a full-blown Extract, Transform and Load (ETL) or Data Warehouse (DWH) inquiry and report.
There are many times when the data extracted and analyzed through self-serve data preparation is all you will need; times when the organization or the user or team needs solid information without a guarantee of 100% accuracy. In these times, the agility of self-serve data prep provides real value to the business because it allows your team to move forward, ask questions, make decisions, share information and remain competitive without waiting for valuable skilled resources to get around to the creating a report or performing a unique inquiry or search for data.
If you build a team of power business users, and transform your business user organization into Citizen Data Scientists, your ‘social network’ of data sharing and rating will evolve and provide a real benefit to the organization. Those ‘popular’, creative business users will emerge and other users will benefit from their unique approach to data analysis and gain additional insight This collaborative environment turns dry data analysis and tedious reporting into a dynamic tool that can be used to find the real ‘nuggets’ of information that will change your business.
When you need 100% accuracy – by all means seek out your IT staff, your data scientists and your analysts and leverage the skilled resources to get the crucial data you need. For much of your organization, your data analysis needs and your important tasks, the data and analysis gleaned from a self-serve data preparation and business intelligence solution will serve you very well, and your business users will become more valuable, knowledgeable assets to your organization.
By balancing agility and data ‘popularity’ and democratization with high quality, skilled data analysis, you can better leverage all of your resources and create an impressive, world-class business ‘social network’ to conquer the market and improve your business. To achieve a balance between data quality and data popularity, your organization may wish to create a unique index within the business intelligence analytics portal, to illustrate and balance data popularity and data quality, and thereby expand user understanding and improve and optimize analytics at every level within the enterprise.
Original Post : Data Agility and ‘Popularity’ vs. Data Quality in Self-Serve BI and Analytics!
