For today’s real-time business, seconds are more important than ever. If you still deal in seconds, it’s a losing hand.
The new battlefield is at the edge of the enterprise, and time there can be measured in nanoseconds. Continuous streams of data are constantly being added to the system. The systems must be able to make real-time decisions with data that is vastly different from what was possible just a few decades ago. It is important to make the best decisions possible within the service-level agreement terms (SLA) that may be a ridiculously short window of 50 to 300 milliseconds.
“Decision making” is not a new concept. It’s been around for as long as computers have existed. This is because of the speed with which decisions must be made.
Businesses that operate in real-time are turning to models that use AI and ML to automate their decision making and behavior prediction are increasing. These models are the engine that drives real-time decisioning. The ability to combine massive amounts of data from multiple sources can greatly improve real-time decisions and could well decide whether businesses win. It might be simpler to ask, “What doesn’t require real-time decisioning?” Here is a partial list:
The “tsunami” of data that is sweeping over our enterprises today is something we all know. The old expression, “more data equals better decisions” is a common one. These data waves, which will only increase with 5G and other advancements, must be a positive thing. But not necessarily.
Although most companies claim that they can handle all the data flowing through their company, the truth is that very few are able to keep up with it.
The problem is that it’s becoming more difficult to ingest data. The water from the fire hose can seem overwhelming in a way. While companies may capture some of the data and make use of some of it, a lot of it ends up on the virtual floors. Data waste can have serious consequences. In decision-making situations, a company that can ingest just a few terabytes might be able to achieve a 65% accuracy rate. However, a company that can ingest between 30 and 40 terabytes will likely have a higher accuracy rate than 90%.
Let’s take anti-fraud software as an example. A decision engine takes milliseconds for a transaction to be legitimate. False positives or false negatives should not be accepted. Customers will leave if they believe that fraud is being committed. The company can lose money if the fraud is not detected. There will be problems if any one system does not inform the other about a customer’s past, habits, and preferences.
Thanks to advances in AI, ML, and database architectures, it is possible for real-time and back end systems to co-exist. This allows real-time decisioning engines for businesses to deliver verdicts that are both pleasing to the consumer and business.
These are the three areas that every data-driven company should be aware of.
1. Create a realistic edge strategy. Different requirements are different for companies in terms of customer access, latency, scale, and access. Every company must determine what data should be pushed to the edge. How much of the stored data should be accessed and at what speed? It is not easy to design for real-time. You should spend your money in a way that best serves and attracts customers.
2. Scalability is not to be sacrificed. Organizations can lose sight of the importance and speed of scaling. Mobile devices, for example, have seen a dramatic increase in data generation. Every day, millions of mobile device users transact. This growth is expected to continue with the introduction of 5G networks. Data architects are under pressure to find new ways of scaling up and out.
3. Modernize your data architecture. Legacy architectures do not lend themselves to real-time decision making. The data architecture that was in place a few years back may be obsolete. Today’s organizations need an architecture that can infuse and process data from multiple sources, and make it available at the edge of their eyes in a flash. To create or maintain a competitive advantage and monetary value, your IT team must have the ability to update the organization’s architecture.
Real-time businesses can increase revenue by providing support, protection, and products at the right time. This will help reduce customer churn and stimulate growth. To achieve these goals, your company must properly ingest and use all data available. When data is properly used, the value it holds is amazing. If data is wasted, it can be a loss of money and you will lose out in the digital economy.
Visionaries make use of real-time data to create new products and improve customer experiences. Recent examples of how data can transform whole markets include DoorDash, Venmo and Uber, as well as Venmo and Uber. It’s hard to predict who will be the market disruptors in the future, but it is safe to assume they’ll be those who harness data in ways that break down barriers and create new norms.