Business intelligence modernization, or BI modernization, is all about data.

By making BI modernization a priority, companies can leverage their structured and unstructured data to produce intelligent insights and improve data-driven decision-making.

A comprehensive BI architecture consolidates data, analyzes it, provide actionable insights, and improves human decision-making. BI platforms also support the customer experience (CX), particularly by driving operational efficiency and by facilitating a responsible, intuitive, and governed use of customer data.

BI modernization brings tried-and-true data analysis practices into the 2020s. Legacy BI tools typically required a team of skilled information technology (IT) specialists; BI modernization, on the other hand, employs artificial intelligence and machine learning (AI/ML) technologies, automated data pipelines, and other automation tools. The best BI modernization strategy is ultimately one that leverages the power of a company’s data.

Artificial intelligence and machine learning easily complement business intelligence modernization initiatives. Both AI and BI focus on automation, the efficient and insight-driven use of data, and a lessening of manual labor performed by employees. AI and BI tools allow employees to hand over control of tedious and manual work, then pivot their focus to tasks that require human brainpower.

As artificial intelligence tools become even more relevant to business operations, business intelligence modernization will become more critical. In their larger quest to leverage their data, companies should not wait to update their business intelligence tools and processes.

During a cloud migration, BI modernization strategies assist by minimizing migration and maintenance costs; providing a 360-degree view of data stored on a public, private, or hybrid cloud; and improving data security during data transfers.

Present-day BI strategy leverages BI modernization tools for a wide number of purposes. These tools can facilitate interactive data visualization, the consolidation of data from disparate data sources, and trend prediction. Examples of interactive data visualization include animated infographics, navigable 3D maps, and eye-catching and interactive annual reports. By enhancing data presentation, insights, and more, BI modernization improves upon legacy BI.

The phases of a business intelligence modernization life cycle include data collection; an analysis of business and user requirements; the designing of a data model; the building of a data warehouse; data visualizations; testing and development; deployment; and performance measuring and tracking.

Per Corporate Finance Institute, an online training and education platform, these steps can be summarized more succinctly as: requirements gathering; data collection; analysis; sharing insights; taking action; and iteration.

If followed carefully, these steps help businesses leverage their data by using it to make informed, data-driven decisions. The business intelligence modernization life cycle is continuous and iterative.

The first step of the BI modernization life cycle (data collection) is integral to determining the business objective. Data collection might be both external and internal; it might involve the use of customer surveys, feedback forms, social media analytics, market research, and internal systems, spreadsheets, and company reports.

During requirements gathering, businesses should identify their priorities and concerns; their unanswered questions; their existing knowledge and insights; the intelligence they hope to glean from their data; the expected duration of the life cycle, prior to adjustments and iterations; and the amount of ongoing support their BI modernization will need.

Extract, Transform, and Load (ETL) is another significant part of the business intelligence modernization life cycle. ETL best practices include metadata management, the validation and testing of data, data quality maximization, indexing, and thorough monitoring and documentation.

Per Kumar Goswami (CEO of Komprise, a software company), in an interview with Transforming Data With Intelligence, “Unstructured data has traditionally been left out of the business intelligence landscape, but that’s changing. AI and machine learning (ML) rely upon these massive troves of file and object data to feed ML models to drive innovation and research, and ultimately improve outcomes.”

Business intelligence of the past was context-unaware, siloed, and unautomated. While employees had access to useful data, this data lacked context. Valuable insights proved difficult to extract.

As modernization becomes more common, and as legacy software and systems become scarcer, businesses will likely run into even more difficulties with their legacy BI tools.

While companies might be reluctant to completely abandon their legacy BI tools and systems, however, the greater risk lies in depending on outdated systems.

Their hesitation is understandable: After all, such a venture is expensive, time-consuming, and risky. Existing systems are deeply hardwired not only into the company but also into employees’ ways of working and day-to-day life. For this reason, outside consultants can help a company’s workforce adapt to a shift as impactful and transformative as business intelligence modernization. Proper BI modernization training increases employees’ faith in their new processes and tools and their ease in using them. Additionally, consultants make sure that business intelligence modernization remains modern, and that companies are aware of updates and new features to incorporate into their BI system.

Finally, business intelligence modernization is not complete without the collection of employee and user feedback. It is important to see how (or how not) BI modernization is improving operations and the customer experience.

Google Cloud’s business intelligence modernization solutions, for example, aim to “help you develop a strategy to modernize BI and put data at the center of your business transformation.” This strategy, per Google, helps businesses innovative and differentiate with data; strategically utilize [the company’s] BI budget; and future-proof [the company’s] BI strategy. Google’s approach includes: architecture with unmatched flexibility and security; an efficient and reusable semantic layer; self-service business intelligence with built-in connectors; a developer platform designed for new data experiences; and multicloud and database access to support any environment.

BI solutions offer a number of business benefits, and they are not limited to the following:

Competitive advantages
Sophisticated reports and dashboards
Real-time, actionable insights that improve decision-making
Data architecture maturity
Identification of patterns and trends within an organization’s business operations
Improved validation processes
Facilitation of a data-driven mindset among employees
Reduction of data silos across an organization, particularly during this time of exponential data growth
Streamlined operations and modernized ways of working

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