Real-time analytics capture data as it is collected, providing timely insights and immediately usable guidance for decision-makers at all levels.
Data is everywhere. Every day people create over 2.5 quintillion bytes of data, and that number keeps rising as the Internet of Things expands.
More importantly, data scientists are learning more and better ways to ethically collect data.
There’s enormous transformative potential hidden in that data – if businesses can find a way to analyze it in time.
Enter real-time analytics, a way to interpret data at its freshest point.
What are Real-Time Analytics?
Real-time analytics, also known as streaming analytics, involves analyzing data as it enters a system to provide a dynamic overview of data, its current state, and emerging trends.
It puts data to work as soon as it’s available.
Real-time analytics is done through the use of continuous queries.
The system connects to external data sources, pulling fresh data and enabling applications to integrate specific types of data into its operations or to update an external database with newly processed information.
The practice stands apart from descriptive, predictive, and prescriptive analytics.
All of those require a batch of historical data to be exported and analyzed. In real-time analytics, software intercepts and visualizes data as it’s collected.
Of course, data isn’t a single-use item. It can be funneled into other analytics methods as well.
The advantage is that by using real-time analytics owners can start putting their data to use while more in-depth processes run.
There’s an Expiration Date on Data
Batch analysis provides a host of useful insights, but it takes time. Waiting on results delays the availability of information. In some cases, the potential value of the insights gained is worth the wait.
After all, Artificial Intelligence exponentially reduces the amount of time needed for deep analysis.
Sometimes that short window matters, though. Data ages fast, and much of it is most useful within a short window after collection. Its value degrades as it ages.
- Demand is surging for a specific service.
- There’s too much inventory of a perishable item building up.
- A customer is in a brick and mortar store.
- A customer has been searching for a type of product in the app.
- A marketing campaign is flagging unexpectedly.
All of these insights need to be acted on quickly.
If data owners wait for more thorough analysis, any actions taken have a weaker effect.
The client leaves the store, or sales don’t quite meet their potential.
Real-time analytics is the tool that provides timely insights to aid executives in ongoing management and rapid response.
It isn’t a replacement for other analytics. In fact, more through forms of analytics are usually where analysts find the best performance indicators to track using real-time analytics.
There’s a synergistic effect: predictive analytics suggests that a specific situation will lead to a major issue if left unchecked, then real-time analytics identifies the beginnings of that situation in time to act.
Where Real-time Analytics Shines
The most lucrative uses of real-time analytics fall under one of two categories: solving problems before they become major issues and spotting opportunities in time to take action.
As mentioned earlier, descriptive and predictive analytics are incredibly useful for highlighting the best key performance indicators (KPIs) to track.
They aren’t always responsive enough to detect the changes that signal the earliest stages of a problem, when small corrections can have a large impact.
That’s where real-time comes into play. Streaming analytics tracks KPI as they’re recorded, flagging anything that might be a concern.
- Inventory and supply chain management
- Production line oversight
- Fraud Detection
- Credit monitoring
The sooner a company can move on an opportunity, the greater their potential for profit.
Real-time analytics helps narrow the gap between receiving indicators of a time-sensitive opportunity and being able to act on that information.
Streaming analytics are usually displayed through dynamic visualizations which are easily understood by busy executives.
They’re a low-complexity tool for integrating integrate analytics usage into daily operations.
- Contextual marketing campaigns
- Social media management
- Suggestive selling
- Mobile asset deployment
Changing the Game for Enterprise
Integrating real-time analytics into the decision-making process is a huge advantage.
Companies who use it are more responsive to actual conditions instead of playing catch-up using outdated data.
When potential windfall conditions form, they have the forewarning to maximize their profit. If there’s a problem brewing, they can take action to minimize the disruptions.
It’s also easier to judge the impact of new programs with a constant stream of data.
This helps to level the playing field between small to medium businesses (SMBs) and large companies.
SMBs can exploit their data to achieve higher efficiency while large companies gain the fine control and fast responsiveness of SMBs.
Real-time analytics don’t impose a perfect balance; multinational corporations tend to have better analytics programs while small businesses can be more flexible in response to changing customer needs.
They are, however, becoming necessary for companies that want to stay competitive.
Those who fully utilize their data consistently outperform their peers, enjoying:
- More revenue
- Less wastage
- Higher efficiency
- Improved customer and employee satisfaction
- Greater ROI from marketing campaigns
In short, companies who aren’t maximizing their data usage are handing their rivals the competitive edge.
Real-Time in Action
The biggest companies around the world are already using real-time analytics to drive profit. Take a look at how it’s being used today:
The digital media giant collects streaming data on when their content is viewed, where it’s shared, and how it’s being consumed by more than 400 million visitors a month.
Employees can analyze, track and display these metrics to writers and editors in real-time to guide targeted content creation.
Royal Dutch Shell, better known simply as “Shell”, uses real-time analytics in their preventative maintenance process.
The system collects and monitors data from running machines to spot issues before they break.
This saves a huge amount of money from lost productivity and secondary equipment failures caused when something breaks.
Package delivery depends on a seemingly endless number of factors, and customers expect their packages within the delivery window regardless of outside circumstances.
The UPS system tracks scores of data points to provide real-time “best route” guidance to drivers.
It also updates depending on office hours (for commercial deliveries) and customer change requests.
Putting real-time analytics to work comes with its own set of challenges.
Bad data leads to flawed insights. Companies need to have a system in place to monitor data quality to ensure it comes through the pipeline ready for analysis.
A business intelligence tool can’t work if no one wants to use it.
There’s no getting around the fact that pushing real-time analytics will cause workflow disruptions in the beginning.
The trick is to sell the team on its value using actual success stories from other projects.
When they understand what they have to gain, they’ll be more willing to work through the early disruptions.
Data security is a serious concern with every business intelligence project.
A major security leak puts both the company and its customers at risk.
Know where data comes from, set up strong security protocols, and be sure it’s being collected legally and ethically.
Making the Most of Real-time analytics
Getting the most from real-time analytics requires planning and executive support. Here are some ways leaders can help ensure success:
Focus on relevant KPI
The point of real-time analytics is to gather time-sensitive insights for immediate use.
Flooding the dashboard with irrelevant data or things unlikely to make an impact in the short term can hide those valuable insights.
Identify KPI that have an immediate potential impact and prioritize them for streaming analytics. Always have a specific business reason for adding KPI to the tracked list.
Promote data-driven decision making on an institutional level
Encourage management (and decision makers at all levels) to refer to data early and often.
If a new course of action is suggested, ask what the data says. Provide resources for learning how to access company data intelligence products.
Lay out company guidelines for collecting, vetting, and using data. This kind of cultural shift starts at the top, so be sure data is king in the C-suite as well.
Modify rules and decisions based on data- but allow time for changes to affect metrics first
There’s a fine line between watching a problem grow without stopping it and abandoning a good plan before it’s had a chance to work.
For example, a restaurant location accidentally orders more fruit than they’re likely to need.
A regional manager spots the problem and launches a digital ad campaign along with tableside upsells to use as much as possible.
It takes time for customers to find and respond to ads, so the manager should wait to see if the promotions work before searching for another solution.
Make real-time analytics part of a larger analytics program
Data intelligence has the greatest impact when several techniques are used in combination with each other.
Small changes noticed during real-time analytics might not seem relevant on their own, but they could take on new weight when measured against historical data.
Sell key internal users on real time analytics
Internal adoption can make or break a project.
Choose stakeholders wisely during discovery, and make an effort to win support from the entire team before launching a new analytics program.
Invest in quality tools
Many real-time analytics tools are built into enterprise software.
When a company moves beyond those entry-level options, it’s critical to make quality as important as cost. Substandard tools are often worse than nothing.
They cause frustration among the team and lower the project’s chances of success.
Stay within budget, but be sure it’s a practical budget that puts core requirements in realistic reach.
That’s easier than it sounds. Modern real-time analytics is surprisingly affordable between off the shelf software and modular custom software.
Consider consulting a developer before making a purchase to be sure it’s worth the investment.
Don’t forget about upkeep
Real-time analytics is a tool that needs to be maintained. Stay on top of software updates and maintenance.
Enforce good data management policies, and use common sense. If results seem strange, find out why instead of acting anyway.
Have realistic expectations about real-time analytics. They’re a tool, and a powerful one, but they’re only as good as the data that feeds them and the people that use them. Keep practical considerations in mind and the benefits of real-time analytics can be transformative.
Where should you start with real-time analytics? Our experienced developers can help you put together the right analytics program for your company. Set up a free consultation today!