10 components of the Business Intelligence landscape

Data is only meaningful if it serves a purpose. This purpose is to have it transformed into meaningful insight, hindsight, and foresight for an organization to take advantage of. Similarly to how raw materials go through a complex conveyor belt system in a factory before the end product is created, so does the data before it gets transformed into information. I refer to this complex conveyor belt system for data processing as the business intelligence landscape, or the BI landscape for short. Understanding this landscape is the first step in putting together a BI program and determining where initial resources should be invested in. The details of each landscape tend to be unique to the industry, business needs, and the organization which it’s part of, but most of the time, all business intelligence landscapes have the same format at a macro level. It’s usually comprised of 5 pillars and 5 foundation blocks:

business intelligence landscape
business intelligence data sources

Data sources

This pillar outlines the different sources from which your data enters your ecosystem. Typically these are:

  • Operational/ transactional sources – Ex: online purchase/donation forms
  • Reference/ master sources – Ex: HR information system, reference master data (such as fund types)
  • External sources – Ex: Neighborhood wealth ratings database, National Change of Address services
  • Unstructured data – Ex: documentation, emails, phone conversation recordings

You need to be aware of all these sources and how data is captured from each one in order to evaluate the data quality level and ensure the proper transformation and business rules are assigned before it can be stored within the systems you manage.


business intelligence data integration

Data integration

Also know as the “data gate keeper pillar” as it evaluates, standardizes, updates, and transforms the data through:

  • Business rules & procedures – create and optimize them and ensure the data follows them
  • Data profiling – a step often missed, but of great value in understanding the data better and gauging its quality level
  • Data integrity – ensuring data is recorded or captured as intended
  • Data manipulation – the actual process of importing it and integrating the data into your systems


business intelligence data management

Data management

This refers to your typical systems housing the data once under your scope. It consists in the maintenance of:

  • Master data store(s)
  • Operational data store(s)
  • Data warehouse and/or data marts
  • Potentially even shadow DBs consisting of Excel files

 


business intelligence reporting servicesReports and Analytics

This core BI pillar is where the data gets outputted and/or transformed into information through:

  • Ad-hoc queries
  • Operational/ analytical reporting
  • Predictive modeling
  • Forecasting & data mining
  • Online analytical processing
  • & other

 


business intelligence delivery servicesInformation dissemination

The pillar through which the created information reaches your intended audience. This is usually done through:

  • Web portals – Ex: an intranet through which user access is maintained
  • Collaboration platforms – Ex: a SharePoint site
  • Dashboards/ scorecards – Ex: PowerBI or Tableau
  • Extracts – Ex: Excel data sheets
  • Publications – Ex: Executive summaries, board reports, organization or department newsletters
  • Personalization services – Ex: a report automatically delivered through an email, or a dashboard dynamically tailored to its user

You need a report inventory, so here are the elements of a comprehensive one. 


The next 5 components are considered part of the foundation without which each pillar can not stand without significantly more effort. Each of these foundation components go across each pillar and you should ensure they are in place before investing further in a BI program.

business intelligence foundations

Information security

In place to protect data and information from unauthorized access, use, modification, disclosure, and destruction. Proper policies and procedures should be in place.


Data quality

Ensures the data is accurate, complete, consistent, relevant, reliable, and meets the defined business requirements. Read more about the data quality management trifecta.


Metadata management

Ensures information can be integrated, accessed, shared, linked, analyzed and maintained to best effect across your ecosystem.


Data governance

Ensures data is managed as an asset by offering consistent and common definitions, business processes, procedures, roles and accountability. Read more about what data governance is.


People & culture

This last  block is there to serve as a reminder that any business intelligence program you’re initiating is deeply influenced by your own organizational culture and the people sponsoring it, implementing and maintaining it, and the ones benefiting from it.


 

I hope having this landscape overview helps you acknowledge that having a successful business intelligence program is not just about transforming your data into information and servicing it to the right people, at the right time. It is also about managing the data flow across the business intelligence landscape and having the foundation in place to treat data as the asset that it is.

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