Philosophers have struggled for centuries to articulate a definition for the word “intelligence.” Aristotle considered it the highest of human virtues, and the root of rational thought, and indeed, the meaning of its original Latin word, intellectus, translates to understanding.
Another definition, often attributed to the late astronomer Stephen Hawking defines intelligence as “the ability to adapt to change.” It’s one that adheres closely to that of Greg McStravick, president of Database and Data Management at SAP, when he gets to talking about the intelligent enterprise.
“In the same way that humans adapt and learn from experience and information, businesses can learn and adapt in order to grow,” he says.
But whether you’re talking about a person or a business there’s a catch: The only way to understand something is if you have the right information about it, the right data. And in business, getting the right data isn’t easy.
“The only way to change and grow is through data coupled with emerging technologies such as artificial intelligence and machine learning,” McStravick says. “A solution that helps companies manage large volumes of data from disparate sources to gain valuable insights is the foundational requirement for the intelligent enterprise.”
Businesses gather a massive amount of data about every aspect of their operations. Study the data close enough, the thinking goes, and eventually patterns emerge that lead to insights, that in turn lead to more informed business decisions. And those decisions usually aim for one of two outcomes: increase revenues or reduce costs.
So it’s no coincidence that data has been called the oil of the 21st century. The comparison runs deep. Before it’s useful, oil must first be refined into valuable items like gasoline or jet fuel. For it to have any value, McStravick says, data must also be refined and analyzed: “You can’t fuel an intelligent enterprise without data. It’s like a car with no gas.”
The comparisons end there. Access to valuable, actionable data is difficult for many reasons. For one thing it’s usually spread all over the place. Most enterprises store their data in a fragmented manner in six to eight different cloud systems, each running different applications. This “data sprawl” leaves companies without the meaningful insights and understanding of their customers, suppliers, and even their own products. And that leads to uninformed business decisions.
This sprawling data landscape is also hard to govern, and to protect. Both problems add a new layer of risk and liability: New regulatory schemes such as GDPR in the E.U. impose stiff financial penalties for data misuse.
The end result of all this? Most enterprises are not yet fueling up to learn and grow. What they need is trusted, connected, and intelligent data and more flexible cloud architecture.
Introducing SAP HANA Data Management Suite
SAP’s vision with SAP HANA Data Management Suite is to provide an enterprise with a common data model that brings together all data types from many sources without moving anything.
“Once the data has been captured and processed, it becomes trustworthy, and only then can it be used for analytical and computing purposes,” McStravick explains. “The elegance of SAP HANA Data Management Suite is in combining large volumes of data outside of core systems. It’s designed to allow computation on data at the point where it resides without having to replicate it.”
That makes it easier to perform analytics or to feed the data into a machine learning system.
An Example of Enterprise Intelligence
Companies in a range of industries are on the journey to intelligence.
“Some energy companies are already leveraging the geospatial capabilities of the SAP HANA platform to identify all impacts on their grid and to manage the subset of the grid infrastructure on a graph basis,” says McStravick.
He goes on to explain that to achieve the first layer of intelligence, a company would load all data on customers, plants, and homes using their utility services by longitude and latitude into the in-memory system to pinpoint exactly where their assets are. Events like service outages can be immediately identified by location and managed.
“That would normally take hours; with SAP HANA it takes sub-seconds,” McStravick says. “In the second layer, the company could start to leverage other sources of data to predict the likelihood of outages, based on earth observation and weather data.
It is this capability that would enable them to intelligently and proactively predict outages, structure outage teams, or schedule predictive maintenance.
A Phased Approach
One big issue facing enterprises on the journey to intelligence is the fact that their enterprise resource planning (ERP) systems are aging and their master data is not harmonized, making it difficult to access and understand information coming from disparate sources.
For example, any company that has dozens of manufacturing sites worldwide generates huge data volumes in a complex landscape. Achieving standardized reports is a challenge because often tables are stored in different systems and a lot of manual tools are needed for mapping them.
“The SAP HANA Data Management Suite in the cloud helps address this challenge,” McStravick explains, “because it provides a central data repository and a unified stream of clean data enterprises can analyze and process to make decisions.”
Intelligent enterprises are on a long-term journey that usually involves a phased approach. First, they need an operational platform to automate processes and allow access to all data, which may have been physically and logically dispersed at one time. Next, they can start making better and quicker tactical decisions using trustworthy information. And finally, they can proceed to advanced business decision-making for strategical impact.
“The beauty of SAP HANA Data Management Suite is that everything an enterprise needs to learn and grow is built in,” says the president of SAP’s Data Management Business. “It provides the data framework, data insight, and data governance to help the company really understand itself.”