Data Analytics & Consulting

Business Analytics & Intelligence


Business Analytics and Intelligence (BI) refers to the numerous technologies and processes for the integration, management, and presentation of data. The objective of Business Intelligence is to support the business decision making process with better information.

Business Analytics and Intelligence are not one and done projects, but more of an overall program, increasing the capability of the business as the program matures. Good business intelligence starts with a competent data quality process that helps improve your organization’s overall data efficiency in addition to driving actionable intelligence.

Business intelligence success requires a few essential steps.

  • Building trust between the business and IT teams.
  • Develop a standard Business Intelligence Architecture, effectively reducing the total cost of ownership and increasing the scalability of enterprise business analytics solutions.
  • Implementing a business intelligence and analytics governance model that support best practices throughout the organization.
  • Aligning strategic business intelligence initiatives to a proven methodology like Data Ideology’s SWIFT methodology.
  • Develop a center of excellence approach to manage the process formally.

"Good business intelligence starts with a competent data quality process"


Business intelligence tools have evolved tremendously over the past few years, and now bi applications are everywhere as the applications have moved closer to the business. End users are increasingly accustomed to rich graphical and visual interfaces and expect to have the same from their business intelligence and business analytics applications

Business Intelligence and Business Analytics tools are the primary technology components, which business users leverage to interact with data. Many business users and leaders are consumers of reports and dashboards that distributed throughout the organization.   

The following represents an evaluation of these different platforms and their high-level capabilities. The primary tools we are seeing at our customers are categorized into the following two categories: Enterprise Business Analytics tools vs. Enterprise Business Intelligence tools. 

Enterprise Business Intelligence (Traditional):

  • Strengths
    • Hundreds of users
    • Multiple LOB’s
    • Analyzing current trends
  • Focus
    • A large number of users across the enterprise
    • Operational Reporting, Dashboards, Alerts, and limited self-service
    • Report Consumers
  • Use Cases
    • Executive Dashboards with guided navigation
    • Operational Reporting (Highly Formatted)
    • Report Distribution to the Masses (Bursting)
  • Platforms
  • IBM Cognos
  • Microsoft SSRS
  • Oracle OBIEE
  • SAP Business Objects 

Enterprise Business Analytics (Data Discovery):

  • Strengths
    • Hundreds of users
    • Multiple LOB’s
    • Trends, Root cause, and predictive analysis.
  • Focus
    • A large number of users across the enterprise
    • Analysis, Discovery, and Predictive.
    • Interactive Users
  • Use Cases
    • Data Discovery
    • Data Mashup
    • Advanced Analysis
  • Platforms
    • Tableau
    • Microsoft PowerBI
    • QlikView 


Do you have any questions regarding Business Analytics and Intelligence?

Data Visualization Tools

Data Visualization tools (QlikView, Tableau, Microsoft PowerBI) have become such an integral part quickly and efficiently delivering business intelligence capabilities throughout organizations. They’ve become easy to use for end-users when working with a conformed data model that is in business-friendly terms. When coupled with the proper data set, they can enable self-service business intelligence capabilities for the end-users within the organization. Many large organizations own several licenses in several formats from each of these vendors.

These data visualization tools can provide the missing dashboard and self-service functionality, as described above, with the current business intelligence platform gaps. As the usage and adoption of data visualization tools continue to increase, organizations should look to rationalize the platforms and move toward an enterprise standard.

When working with customers, we leverage top industry resources from Gartner, Forrester, TDWI, and others assist when narrowing down the list of BI Platforms that should be considered as viable options if consolidating to work toward an enterprise standard. We look at vendor performance, and key uses cases for each BI platform were reviewed to understand the strengths, weaknesses, and capabilities of each vendor reviewed.


  • Operational Reporting
  • Interactive Dashboards
  • Alerts and Notifications
  • Collaboration
  • Office Integration
  • Self Service
  • Big Data Connectivity
  • Data Visualization
  • Data Mashup
  • Meta-Data Layer
  • Forecasting and Predictive Modeling 

We often help our customers evaluate an enterprise standard for efficiencies and simplifying the business intelligence landscape while leading to more integration and less business intelligence/analytics silo’s. 

It’s essential to consider the Enterprise BI Platforms in which your organization has invested a lot of time and money to deploy throughout the enterprise. Sometimes the cost to migrate outweighs the benefits of a wholesale change.

Two key benefits from a consolidation effort would be:

  • lowering total cost of ownership (TCO)
  • Improving consistency of information and analytics


An organization should leverage this centralized approach to establishing a partnership between the business and IT. The following considerations should be a part of this partnership with each party sharing ownership and responsibilities for the following areas:

  • Vision: Business and IT strategy aligned 
  • Prioritization: BI Projects priority aligned to business strategy
  • Funding: Costs, prioritization, and BI project alignment matrix
  • Change Management: Communicating and controlling the scope of projects
  • Best Practices: Solution is in-line with business strategy and BI architecture
  • Requirements / Rules: Agreement, clarity, and priority 

A centralized approach enables a new enterprise business intelligence and analytics deployment model, making the IT teams more of an enabler for the business users. Data Ideology’s view is that there are nine success criteria for enterprise data and analytics success:  

  1. Data/ BI as a Program
  2. Leadership Support
  3. Change / Project Management
  4. BI and DW Platforms
  5. Data Governance and Quality
  6. Competency Center or Center of Excellence
  7. Balanced Business and IT teams
  8. End-User Adoption
  9. Business Ownership 

Over time, this centralized approach enables a shift of IT resources that are currently drowning in report requests of developing or reformatting multiple variations of the same data sets to more strategic and higher value analytics challenges. When the business and IT operate as more of a partnership, it enables the IT teams to deliver better business outcomes with actionable intelligence.

As the amount of programming and report requests diminish over time, and the IT team’s capabilities mature, they shift their focus to architecting scalable analytics models as well. This work becomes much more valuable as the analytic content (Visualizations, Reports, and Dashboards) can now be pushed out into the business users as these data models are crucial to unlocking self-service analytic capabilities.

Many business users get enamored by dashboards and visualization; however, the value is in the data models exposed to the business users. 

SWIFT Framework

"Doing the right things" is accelerated and enabled by leveraging our proven SWIFT Framework.


When looking at the top reasons BI projects fail, the following are typical reasons:

  • Lack of Executive Leadership commitment  
    • Changing priorities 
    • Failure to closeout projects
    • New shiny object
  • Changing Project Scope 
    • Continually adding new features and functionality 

How Data Ideology helps customers address these top reasons BI projects fail?

  • We leverage our proven SWIFT methodology to guide the overall process
  • Project Portfolio Management 
    • The continuous process of identifying, selecting and managing a portfolio of projects in alignment with key performance metrics and strategic business objectives
  • Project Portfolio Prioritization
  • Build consensus on projects through an evaluation process 
  • Improve the alignment with business goals and objectives 
  • Add transparency to the prioritization of IT projects 
  • Improve collaboration across the organization
  • Improve the balance of work for IT staff 

Contact Us


What is a Data Strategy & Why is it Important?

In 2023, companies need a data strategy more than ever as the landscape of data management and analysis continues to evolve and become increasingly more complex.
Banking & Financial Services

The Benefits of Data Warehousing in Finance

A data warehouse is a storage system that enables you to track crucial data points over time and analyze them to run your financial operations smoothly and make sound decisions. 
Banking & Financial Services

The Benefits of Data Lakes for Financial Services

Data lakes are centralized repositories of data that are helpful for compliance purposes, performing forecasts, risk assessments, and understanding customer behavior.