The number one obstacle to a business’ growth is its ability to scale business intelligence, have visibility into its data and the ability to make informed business decisions. It may be time to look at how you are leveraging your most significant asset—your data.
By Evan Schwartz

I have spent more than 20 years working with customers from various industries, and there appears to be no consensus or wide adoption of what Business Intelligence is. This confusion stems from misunderstanding the fundamental differences between Data, a Report and Business Intelligence (BI).

In today’s world BI is commonly considered to be a vital business resource. It is considered valuable by any business decision-maker across every industry. I have seen many business professionals inaccurately group data, reporting and BI into a single Business Intelligence Strategy. So, it might be helpful to establish what is Data, a Report and Business Intelligence, as well as their key differences.

The Essential Components of Data
If BI is digital gold, then Data is the equivalent of unprocessed raw ore mined from the world around us. This analogy is so commonly accepted that the term data mining is ubiquitous within our business lexicon. Data mining uses databases, statistics and machine learning to uncover trends in large datasets. At this point, Data is only an accumulation of facts and dimensions stored somewhere for review and requires interpretation. In the Waste and Recycling industry, as an example, if you were collecting charges against a casual customer weighing in at a landfill, then the Data are the facts of that transaction. It is interesting only within the context of that single transaction, that single event in time. Beyond that single event, it has little to no value.

The essential attributes of Data are that it contains a time-based component and some description attributes: In addition to measurement, you know what it is and when it was collected. It takes, at minimum, these three attributes to “say” something about the data. The measurement by itself is not interesting—$105.67. However, knowing that Evan Schwartz paid $105.67 to dump some household waste on September 29th at 10:37 AM ET and paid with cash—well, that is a data point. However, Data alone will not help you make good business decisions.

The Purpose of a Report
The next component to BI is “The Report.” A report is not BI. We overuse this term, I think, to represent any information we extract from a system—”We’ll write a report!” A report has a brand recognition problem, and we have watered down the term to the point that it has lost all meaning. I have seen companies with thousands of reports. Unfortunately, what they have are thousands of queries or data extracts. A report should tell a story, answer a business question and target a business audience. A proper report has some essential components or attributes like the description of Data. A
report has a title, a purpose, aggregation, or calculation of data in some way (SUM, AVG, GROUPING, etc.) and is answering one or more specific questions about a collection of data used to make a business decision. For instance, what were the total tons of material my landfill received today? The data from the previous example would be part of this report if we executed the report when I wrote this article.

Now, to complicate things, a query or data extract could have some of these elements. The key point here is to ask if it just describes a particular business context, or do the results drive a business decision? If it is not driving a business decision, it is a data extract. For instance, what business decision would a daily tonnage report for a landfill help to facilitate? At a glance, a facilities manager could tell if everything was running well simply by knowing the total tons on a given day of the week and month of the year. A good facilities manager knows their business. If the total did not look right, they would want to see the transaction details to see what happened. Noticing a gap in the data mid-day, they could correlate that data gap with recalibration of the scale. For them, all is right in the world—and the report confirms this.

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and mape to provide users with detailed intelligence about the state of the business.
Image courtesy of AMCS.

So, what is Business Intelligence, if Not a Report? Great Question!
According to CIO Magazine: “Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions. BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business. The term business intelligence often also refers to a range of tools that provide quick, easy-to-digest access to insights about an organization’s current state, based on available data.”1

So, reports, for example, are important if they contain the right data and are used to deliver the right insights to the business to generate better decision-making. Continuing with our scenario, how does the Facilities Manager know what a typical Wednesday in September’s tonnage should be? How do they know if the facility is healthy or not by examining a single aggregated data point? The answer is simple; they need Business Intelligence. Metrics are the key attribute required for Business Intelligence. Without metrics, you do not have BI. While raw data are facts, and reports calculate or aggregate facts into data points, a metric is an assertion or data point over a comparable. Having analyzed this data, over time, they can create a KPI (key performance indicator). This is descriptive analytics, backward looking and where most companies still live. In this case, the KPI for a Wednesday in September is 1,000 TNs of material. KPIs have tolerances built in. If they see that the facility processes approximately 1,000 TNs (+/- 100 TNS), they are happy. However, this is just a simple example of how data can be interpreted into something meaningful for the business.

Predictive and Insightful
Business intelligence has two sides: predictive and insightful (prescriptive). A predictive Business Intelligence metric has a KPI to measure against, with tolerances and limits of acceptability. Insightful metrics, however, are more of an art form. You are examining data over time, looking for trends, variations, and outliers, or developing new KPIs to predict better or anticipate changes in your industry. Through insights, you want to support decisions that produce desired outcomes. You are prescriptive. Predictive BI is automatable and used as a watchdog over critical areas of the business. Overweight vehicles blocked before outbound shipping, extended service times for a drop and return container indicate a driver problem, uncharacteristically high contamination rates from specific regions of your service area suggesting a call to action by your ZeroWaste initiative are all examples of Predictive BI in action.

Insightful BI is the untapped frontier of BI and begins to cross the boundary of predictive analytics to prescriptive analytics supporting business decisions that drive outcomes. Here is where AI has taken a dominant role looking over transactional data, live, as we generate it, and looking for patterns, behaviors, anomalies over time, and generating intuition about massive amounts of data. The next generation of AI is helping us see things in our data that would take years of experience to understand. Following the previous example, the Facilities Manager that established a healthy KPI of 1,000 TNs for Wednesday in September deduced that KPI over years of experience. When extreme conditions exist, like the recent pandemic, humans often take too long to adjust the KPIs they have become dependent upon to drive business decisions. Adjustment, then, comes at a high cost of
friction and potential losses. Having AI as a vanguard centurion lording over our data and looking for shifts in patterns, we are presented with a near-perfect analysis of our vast oceans of data. We then use this analysis to distill them into KPIs so that automated agents can stand ready as watchdogs over our increasingly more complex world to extract the best positive business outcomes possible, or directly drive a desired outcome.

Leverage Your Most Significant Asset
So, when looking at the reports your organization has available, ask yourself if you are looking at raw Data, data extracts, an actual Report, or a Business Intelligent dashboard helping you make business decisions. The number one obstacle to a business’ growth is its ability to scale business intelligence, have visibility into its data, and the ability to make informed business decisions. Ask yourself, are you reacting to the industry, or driving it? It may be time to look at how you are leveraging your most significant asset—your data. | WA

Evan Schwartz is Chief Enterprise Architect at AMCS Group. He has 25 years of experience in the waste, recycling, and commodities industry and has been with AMCS for five years. For more information, e-mail [email protected] or visit