The Truth about Business Intelligence

The Dresner Advisory report suggests organizations around the world are attempting to maximize their daily operations by examining, displaying, and querying their past data in a variety of new and interesting ways.

This is new ground for businesses as they break away from traditional static spreadsheet views and move towards flexible and adaptable interactive dashboards. However, business intelligence software is missing a crucial component that businesses need in order to adapt to long-term market changes.

What Business Intelligence is Missing

To truly be an intelligent business, BI software would need to include a component that analyzes future challenges and opportunities, not just analyze historical data.

Current BI software should be labeled as Data Intelligence (DI), as it is incredibly sophisticated at organizing and outputting digestible reports on past business processes. Yet, it is missing the ability to create a malleable and queryable view of the future via agile financial models.

There is No Such Thing as a Unicorn

Similar to budgeting applications, there is no existing software that is best-in-class for both historical data manipulation and prospective modeling.

The root of the unicorn issue is that every software is purpose-built to solve a specific underlying challenge. The foundation of BI software is data; it was created to analyze historical data, identify relationships, and output highly customizable reports and charts.

Software Efficiency Curve: A graphical representation of the unicorn problem. There is no software that is best in class for both long-range planning and operational management. Spreadsheets are capable of providing rudimentary insights for both axes, but cannot compete with purpose-built software.

Modeling intelligence (MI) software like Synario was purpose-built to look at future possibilities. With Synario, business leaders can view changes in their tied-out financial statements as they explore an unlimited number of future scenarios. To accomplish these types of projections quickly, the foundation of financial modeling software must be formula-based.

Software that attempts to create data-based projections struggles when attempting to answer complex scenario-based questions using an entire dataset. For example, let’s say a business is looking to model their financial health as they change the price of their products over a range of five values and is looking to add between one and five machines to help increase manufacturing output.

Modeling only two key drivers results in 25 different financial scenarios. Altering a sizable dataset 25 times consumes valuable time and energy, and after painfully modeling all 25 scenarios, data-based software cannot quickly isolate the differences between scenarios because each scenario is a disconnected copy of the original dataset.

When the software is formula driven and purpose-built for modeling, altering financial statements based on any number of scenarios is fast and efficient. Because projected datapoints are all formula-based, updates to assumptions are quickly pushed through the model, only affecting related values rather than updating and copying an entire dataset.

Minute differences are isolated and highlighted, making the right trajectory clearly identifiable. A foundation in formulae means that only applicable components of projections are updated when a new assumption is entered into the model, rather than updating the entire database.

Business Intelligence was not Built for Long-Term Strategy

Most BI software includes rudimentary forecasting capabilities adept at identifying data relationships and projecting those relationships into the future. For example, if sales of a certain product declined every January for the last 3 years, then the BI software would continue projecting that decrease into the future.

BI forecasting techniques have a variety of technical names such as demand forecasting, predictive modeling, modeling with machine learning, and descriptive modeling. Each has a different use case and can help when making decisions in the short-term. Where they cannot help is when a business needs answers to long-term strategy questions.

Those types of questions need to be answered using in-depth scenario analysis. A multitude of trajectories need to be explored to determine which set of projects, initiatives, and key business drivers need to be adapted to maintain long-term financial health.

In the previous example, demand forecasting predicted that sales will dip every January by extrapolating on previous sales data. However, if the company in question was looking at selling their products in new countries and locations, that continued forecast may not apply. If a decline in January sales was a result of holiday shopping in December, then sales in another country, where most of the population does not celebrate the same holidays, may not see any decline in sales. That stabilization of sales during the month of January may influence the decision to begin selling in one country versus another.

Modeling Intelligence (MI) is crucial to answer these types of scenario-based questions. Extrapolating historical data does not support strategic decision-making if the data does not exist for a new project or initiative. New buildings, product launches, and service offerings are all examples of high-level decisions that many companies do not have any data to support.

Without existing data to orient their decision, business leaders need to analyze all future possibilities before locking-in a long-term strategic direction. Even analyzing best, worst, and expected cases can often lead to disastrous consequences due to a limited view of the future.

True Business Intelligence

Combining data intelligence and modeling intelligence yields true business intelligence. Data intelligence provides the insights into short term business operations while modeling intelligence informs long-term decision-making through queryable financial models.

Many businesses and their internal leadership teams are solely focused on increasing revenues in the short-term using existing BI (or data intelligence) software. Only a select few organizations are fully realizing the benefits of implementing modeling intelligence software in conjunction with traditional BI. Forward thinking organizations often utilize both traditional BI and MI to achieve business agility and resilience in the short- and long-term.

See our available case studies to discover how other organizations are utilizing modeling intelligence.