Implementation of real-time analytics is challenging for most businesses, especially for those dealing with big data. Usage is increasing dramatically but there are still some barriers to widespread integration.
Enterprises who are integrating real-time analytics find it is worth the effort and experience significant advantages. Here are some of the advantages and challenges of using real-time analytics.
Some advantages of using real-time analytics
Real-time analytics offer numerous benefits for organizations and customers.
- Identify errors early
Organizations can identify errors or problems quickly and respond to them immediately. Increasing operational efficiency reduces costs and improves customer service.
- Stay ahead of the competition
Using real-time analytics can help organizations to stay ahead of competitors. They can not only measure what happens in their organizations but what competitors are doing too.
- Deliver customization
Real-time analytics gives organizations the potential to give customers what they want when they want it. Amazon, for example, uses real-time analytics to effectively sell their products because it allows them to tailor the user experience.
- Increase conversion rates
Organizations using real-time analytics can react and respond quickly to prospective and current customers, offering improved service and increasing conversion rates.
- Detect fraud early
Using a real-time data analytics security system enables organizations to detect fraud attempts at an early stage and take preventative measures.
- Save costs
Implementing real-time data analytics may have high costs initially but over time, it saves costs due to increased efficiency, more customer satisfaction and more sales.
Challenges that come with implementing real-time analytics
A number of challenges exist when it comes to implementing real-time analytics, such as a need for more computing power and the ability to collect relevant data.
Data collection
The first step in the process is to collect relevant data. Errors in data collection can affect the integrity of real-time analytics.
Gartner found that 75% of organizations surveyed had experienced a negative impact on company finances from incorrect data and half of them incurred additional costs to reconcile data. It is critical to make sure that systems are in place to ensure data collected is of the highest quality.
Inconsistent ideas about what real-time means can create uncertainties about what data is needed and what sources to use. Ironically, too much up-to-the-minute information can cause paralysis in some companies due to uncertainty about what to do with it.
Data must come from reputable and dependable sources. Companies need to invest time and effort in gathering requirements from all stakeholders and reach a unanimous agreement about what real-time data is needed and what sources to use.
Architecture
The network framework also has to support real-time data collection. The standard version of Hadoop which works for a big data file system is not suitable for streaming data. This is why many enterprises are using Apache solutions which are ideal for collecting and processing datasets. They are also built to accommodate real-time analytics and applications.
Architecture design needs to allow for processing data at high speed. It has to be able to deal with spikes in data volume and should be able to scale up as data grows. What may have been perfectly acceptable to date may not be able to handle data volume when it doubles.
Architecture has to be designed to handle both real-time and offline analytics. For instance, identifying patterns with machine learning is a time-consuming process and it is not suited to real-time processing. This is unlike sending instant alerts which is a good application for real-time analytics. If the architecture can’t handle both, a conflict for computing resources could affect performance.
In-memory computing simplifies architecture. It moves data that used to be stored on hard discs into memory, allowing for much faster computing and the ability to scale up when data increases.
Internal processes
When companies want to use real-time analytics, it means gathering requirements, designing architecture, choosing the right technology stack and solving software and hardware issues. These technical tasks can distract enterprises from what to do when it comes to internal processes.
Perhaps you’re a manufacturer who wants to improve your equipment repair time. Your maintenance team spends too much time trying to identify what is wrong with the equipment and is often unable to fix a machine because they don’t have a replacement part. This causes downtime which is costing you money.
Implementing real-time analytics can solve the problems but it also means having to revise the way your maintenance team operates, change job descriptions and key performance indicators.
Implementing real-time analytics requires a different way of working. Getting insights every second requires a different approach. If real-time analytics are viewed as a way to improve internal processes, employees will be more accepting of change.
Employee mindset
Upgrading your systems and implementing real-time analytics can be frightening for employees. If they are prepared properly, it doesn’t have to be.
Some employees, including management, may not see the benefits of real-time analytics. To change their minds, company owners and IT officials need to convince upper management of the advantages. This is critical to gaining acceptance of the whole workforce.
To avoid disruption, top management should be able to make clear the reasons for the shift to real-time analytics and the opportunities it creates. If there are technical barriers, employees may need training to feel confident that they’re able to adjust.
Companies need to have data strategies in place if they want to realize the benefits of real-time analytics. They need to begin by defining strategies for cutting operation costs, targeting specific markets and improving customer satisfaction. Knowing their goals helps them to focus on the kind of data they need to collect and analyze.
Final Thoughts
Organizations that implement real-time analytics are likely to face a number of challenges. It’s worth overcoming these because of the many benefits using real-time analytics offers. There are numerous ways to apply it for the benefits of staff and customers. Breakthroughs in other areas of modern technology are helping to dismantle some of the most significant challenges and the future looks bright for more widespread adoption of real-time data.
