"A key sign of successful business intelligence is the degree to which it impacts business performance." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)
"Business intelligence tools can only present the facts. Removing biases and other errors in decision making are dynamics of company culture that affect how well business intelligence is used." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)
"Successful business intelligence is influenced by both technical aspects and organizational aspects. In general, companies rate organizational aspects (such as executive level sponsorship) as having a higher impact on success than technical aspects. And yet, even if you do everything right from an organizational perspective, if you don’t have high quality, relevant data, your BI initiative will fail." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)
"The data architecture is the most important technical aspect of your business intelligence initiative. Fail to build an information architecture that is flexible, with consistent, timely, quality data, and your BI initiative will fail. Business users will not trust the information, no matter how powerful and pretty the BI tools. However, sometimes it takes displaying that messy data to get business users to understand the importance of data quality and to take ownership of a problem that extends beyond business intelligence, to the source systems and to the organizational structures that govern a company’s data." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)
"There is one crucial aspect of extending the reach of business intelligence that has nothing to do with technology and that is Relevance. Understanding what information someone needs to do a job or to complete a task is what makes business intelligence relevant to that person. Much of business intelligence thus far has been relevant to power users and senior managers but not to front/line workers, customers, and suppliers." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)
"Data migration is not just about moving data from one place to another; it should be focused on: realizing all the benefits promised by the new system when you entertained the concept of new software in the first place; creating the improved enterprise performance that was the driver for the project; importing the best, the most appropriate and the cleanest data you can so that you enhance business intelligence; maintaining all your regulatory, legal and governance compliance criteria; staying securely in control of the project."
"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)
"Dashboards are collections of several linked visualizations all in one place. The idea is very popular as part of business intelligence: having current data on activity summarized and presented all in one place. One danger of cramming a lot of disparate information into one place is that you will quickly hit information overload. Interactivity and small multiples are definitely worth considering as ways of simplifying the information a reader has to digest in a dashboard. As with so many other visualizations, layering the detail for different readers is valuable." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)
"The way we explore data today, we often aren't constrained by rigid hypothesis testing or statistical rigor that can slow down the process to a crawl. But we need to be careful with this rapid pace of exploration, too. Modern business intelligence and analytics tools allow us to do so much with data so quickly that it can be easy to fall into a pitfall by creating a chart that misleads us in the early stages of the process." (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020)
"Descriptive analytics takes the form of on-demand queries, standard reporting, self-service business intelligence, and data lake exploration. This category of analytics performs analytics on historical data by providing different points of view on the data. The points of view are created by aggregating and filtering the quantitative data (also called the measure), and slicing the data across attributes of functional dimensions, such as sales, customers, and products. We can deliver descriptive analytics in multiple formats such as files, cross tab reports, visual reports, or dashboards." (Pradeep Menon, "Data Lakehouse in Action", 2022)
"Historically, for their analytics needs, enterprises relied upon a set of tightly coupled tools, typically provided by a single vendor. Nowadays, nearly all of the components of a traditional data warehouse are independent and interchangeable. Those independent tools can be flexibly combined to provide a modern data stack. It is common for current enterprises to have separate tools for data ingestion, data pipelines, data storage and querying, data visualization and business intelligence, and data quality. Furthermore, data can flow in the opposite direction out of the data warehouse in what is referred to as reverse extract, transform, and load (ETL)." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)
"Self-service BI initiatives help organizations become more data-driven and democratize access to data. But data can’t be used if it can’t be found. Search and discovery of trustworthy data is a core value of enterprise data catalogs, and the value extends well beyond business users." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)
"Historically, for their analytics needs, enterprises relied upon a set of tightly coupled tools, typically provided by a single vendor. Nowadays, nearly all of the components of a traditional data warehouse are independent and interchangeable. Those independent tools can be flexibly combined to provide a modern data stack. It is common for current enterprises to have separate tools for data ingestion, data pipelines, data storage and querying, data visualization and business intelligence, and data quality. Furthermore, data can flow in the opposite direction out of the data warehouse in what is referred to as reverse extract, transform, and load (ETL)." (Fadi Maali & Jason Lim, "Implementing a Modern Data Catalog to Power Data Intelligence: Make Trustworthy Data Central to Your Organization", 2022)
"Parquet, optimized for Azure Synapse Analytics and Azure Databricks, represents a specialized but increasingly important file format designed specifically for analytical workloads and big data processing. Unlike CSV and JSON, which prioritize readability and interoperability, Parquet optimizes ruthlessly for storage efficiency and query performance in scenarios involving large datasets and analytical processing. The secret to Parquet’s effectiveness lies in its columnar storage approach, which organizes data by columns rather than rows. This organization provides significant advantages for analytical queries that typically operateon subsets of columns across many rows - exactly the pattern common in business intelligence, data warehousing, and analytical reporting scenarios." (Michael J Peña, "Azure Data Fundamentals: A Guide to DP-900 Certification and Beyond", 2026)
See also: the [definitions] and the [index] of similar posts.
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