How Does My Business Benefit from Real-Time Streaming Applications?
Many use cases that exemplify how we think about the future of business revolve around organizations’ ability to make sophisticated decisions with agility and speed. Consider customer 360 initiatives. A company might offer a specific individual a coupon code while they’re browsing the website or at the store. To do so, that company must have real-time visibility into its customer and company data and be able to send that coupon instantaneously.
These types of sophisticated applications require access to various types of real-time data and the ability to process them quickly. Traditional data, which is stored and then processed, can help companies adjust daily with batch processes; however, modern businesses require streaming data as demand for real-time insights and action grows.
In fact, companies that prioritize data innovation in their organizations report an increase in profit of 9.5% and that they are 2.9 times more likely to beat the competition to market. Those companies that use real-time streaming applications to harness the full potential of streaming data will further position themselves at the forefront of the next wave of business innovation.
The benefits of harnessing streaming data with streaming applications
Streaming data is best put to use with streaming data applications. Utilizing streaming data via streaming applications allows companies to better see and understand their business, act as needed, and ultimately make better use of resources like time and money.
How real-time business visibility empowers better decision making
Sometimes, it’s easy to underestimate the power of simply being able to see. This applies to business operations, too: Streaming data applications allow companies to see their operations’ real-time state and react accordingly.
This real-time business visibility is a powerful tool, as it empowers businesses to make the best decisions based on the current state of their business — not based on a past state (such as a week or day before) or a hypothetical future state.
While data from the past week, month, quarter, or even year might suffice for general course correction, it prevents companies from making more agile, in-the-moment decisions on the actual state of their business. This can present a problem when a company faces a novel event (such as a global pandemic) or challenge for which old data doesn’t account.
Consider an apartment leasing company in the city that needs to decide whether or not to adjust rent prices in the spring. While the previous spring’s resident retention rate might give the leasing company a general idea of how far it can raise rent without losing too many customers, it doesn’t consider the current state of its business. This could be a huge oversight if, for example, a pandemic has taken the nation by storm, people are leaving the city in droves for more space and housing in the suburbs, and the leasing company is hurting for cash.
Using tools such as AI and predictive models to hypothesize on events that might happen can also be helpful — but so too might lull companies into not taking action based on the state of their business right now. Often, taking small actions to remediate an issue now is better than taking no action at all and hoping for the best next time.
Ultimately, streaming data’s real-time business visibility empowers companies to make stronger business decisions based on where their business is now — and with the proper context — instead of relying on old data and hoping events follow the same patterns.
How business logic paired with context enhances automation and productivity
The modern business landscape requires organizations that can move with agility and speed — which can be challenging for those reliant on latency-filled legacy data processing methods and quickly outdated data insights. When used to its full potential, streaming data can help companies build and execute business logic that makes more sophisticated use cases and automation a reality.
Streaming applications make these more sophisticated business applications possible, pairing real-time business logic with the appropriate context. Consider, again, the customer 360 use case. To be successful, the company must implement business logic that addresses context (this loyal customer is in this store) and then real-time action (send them a coupon now for a product they are likely to buy).
Successfully implementing real-time business logic with context allows companies to automate the execution of more complex decisions that might need to happen during brief windows of opportunity. This enhanced automation can also free up employee bandwidth for more strategic work, as repetitive work can be automated, and insights can be quickly surfaced.
Without real-time streaming applications, companies will struggle to implement business logic that makes the best use of automation and decision-making and instead be stuck with clunky or latency-riddled solutions.
Reduce latency — and operating expenses
Most traditional streaming data architectures aren’t designed to process and store growing amounts of data in a cost-effective way. In fact, most traditional streaming architectures face a conundrum of latency and cost: as one decreases, the other increases. And when it comes down to it, data storage can also be extremely wasteful.
Streaming applications first solve the tug-of-war between latency and cost. This is because streaming applications follow a process-then-store architecture (rather than store-then-process, which traditional architectures follow), allowing organizations to implement business logic as data arrives and store only necessary data. In this way, streaming applications are selective in what data is collected, what data is stored, and how long data is stored on a case-by-case basis according to the company’s business logic. With this architecture, companies can scale to handle growing amounts of streaming data without breaking the bank on data storage and processing costs.
Streaming applications also help solve expenses around data storage. Most likely, a significant percentage of data companies store is useless (e.g., Clickstream data for each website visitor). Plus, it costs a lot of money to maintain data storage, and latency climbs when data processing queries have to sort through packed databases, data warehouses, or data lakes. However, many companies struggle to manage data storage because they fear that not storing data might limit the range of insights available to them over time.
Streaming data solves the conundrum of latency versus cost in data storage by first pushing data directly where needed as soon as available, thus eliminating time spent requesting the same data repeatedly. Streaming data applications also give companies greater discretion over what data is kept and what is not. You only keep a certain data set for each entity you decide to track.
All in all, streaming applications can be considered the engine oil that powers modern organizations to operate easily. Streaming applications make the best use of streaming data so that organizations can actually see the real-time state of their business operations. This allows them to make better, quicker decisions, implement sophisticated use cases, including automation, improve productivity, cut costs, and ultimately – gain a competitive edge.
The challenges of streaming data
Streaming data has the potential to usher in the next wave of business innovation — for those who can wield it. To date, however, many companies have struggled to harness the full potential of their streaming data by relying on a traditional application structure to process streaming data.
Traditional stream processing architectures are built following a store-then-process approach. This architecture also requires the repeated querying of one or more databases for context, adding latency. Organizations using this store-then-process approach often find that increasing the data rate to decrease latency inevitably drives up the cost of storage required to buffer data until it is processed.
Due to these limitations, the primary challenge of traditional streaming data architectures is that data streaming stops at the application layer. This means organizations cannot analyze and respond to events based on their contextual meaning in real-time.
To take full advantage of streaming data, companies need a streaming data architecture –as offered by streaming applications – that can act on data in motion rather than interrupt the flow.
Unlocking the promise of streaming data
Streaming applications are the key to unlocking the full potential of streaming data. Streaming applications address the challenges posed by traditional stream processing by flipping the approach on its head: Instead of following a store-then-process approach, streaming applications follow a process-then-store approach that surfaces insights as they arrive and stores only the most relevant data.
With streaming applications, companies can reap the full benefit of streaming data to better see, interpret, and act on the real-time state of their business. This increased agility, and speed will position companies to execute more sophisticated use cases that the modern business world is coming to expect and require.
Nstream Cloud delivers the fastest way to build streaming applications, so companies can fully understand what’s happening with their streaming data in true real-time, adapt to the rapidly changing business landscape, and gain a competitive edge.
See how both logistics and retail organizations can employ streaming applications to build a more resilient supply chain and increase customer satisfaction.
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