Data latency: What is truly important to know about it?

Nowadays, every company generates mad loads of data from operations, appliances, machines, communication, transactions, etc. In fact, recent estimates show that the worldwide data generated on a daily basis comes up to 7.5 septillions gigabytes. And all these data have the potential to be used as valuable sources of Business Intelligence, improving the planning and risk assessment processes to quickly respond to changing market conditions, innovative technologies, trends, customer behaviors, and competition.

But in order to be able to keep with all those important factors and achieve high value and use of data, it is crucial to look into one important term, called “Data latency.” 

In short, data latency is the time it takes for data to travel from one place to another. Companies usually measure the latency in terms of two types of delay: 

1. The time-lapse between the occurrence of a data-generating event and the arrival of these data at the company database or system.
2. The time-lapse between the arrival of data at the company database or system, and its availability to be retrieved for business use. 

And yet, business entities recognize three different types of latency, which are used in distinct circumstances. 

Types of Data Latency

1. Zero-data latency (or so-called real-time data)

This is the case when data arrives at the database immediately after an action occurs and can be simultaneously collected and retrieved from the storage to be used for the achievement of business objectives. Of course, this means that the businesses that want to benefit from real-time data have to possess an appropriate database to allow these concurrent activities. 

Often, such storages require solid financial investment and expertise to be set-up and handled in the right way. Besides, constant updates are a “must” in order to continue functioning effectively and in an errorless way. 

2. Near-time data

Data becomes available for business use at set intervals, according to the business needs. This may be hourly, daily, weekly, monthly, or in relation to another interval set by the organization. 

The databases and storage used should be updated regularly in order to keep relevant data sets and diminish the risk of mixing data from different time-intervals. This is crucial for ensuring the trustworthiness and transparency of the data resources within the business entity. 

Besides, such near-time data requires set and aligned management and technical procedures for effective dealing and handling of data, which to guarantee high-quality insights regarding organization, industry, customers, and competitors. 

3. Some-time data

In this case, data is only accessed and updated when needed, which is usually no more than a couple of times a year. 

In theory, there is not much to say about this type of latency, as the best way to give an example of it would be when customers change their addresses or phone numbers. Those data have to be updated in the company’s system when change appears, in order for the entity to have access to truthful information. 

Do all firms require Zero data latency?

Not at all. In fact, every company experiences data latency, depending on the industry of operation, activities, company objectives, etc. Yet, in most cases, organizations benefit from all three types, taking into account their different purposes. For Example:

• Some-time data is useful to update customer contact lists;
• Near-time data is great for issuing annual or quarterly financial and sales reports for progress assessment;
• Real-time data is key in industries where time is crucial for maintaining competitive advantage, minimizing loss, optimizing output and profit and being able to cope up with the constantly changing working environment in real-time. Such industries are Industry 4.0, Warehousing, Logistics, Data integration, etc. 

The zero-latency data gives those organizations plenty of benefits for planning, decision-making, achieving employee satisfaction, boosting CX, and acquiring strong competitive advantage to stand out from the competition. Nonetheless, achieving an impeccable real-time data architecture is a costly, complex, and time-consuming process, which requires constant long-term involvement of management, data engineers, and strategists. 

Exactly for those reasons, companies find it extremely difficult to set up and maintain such data latency, but with the right objectives in mind, it is possible to succeed and thrive. 

Tips on diminishing latency

1. Capture events and collect data as close to the source as possible: In other words, change the IT architecture from centralized to decentralized. 
2. Adopt multi-cloud connectivity: Integrate applications, devices, and data in cross-connected clouds.
3. Use HTTP/2: Improves the dynamics of the information updates, optimizes the flow of data between users and servers, and boosts the pace of data transactions. 
4. Less external HTTP requests: Including pictures, fonts, JS, and CSS files to third-party servers: Fewer of those means better data velocity and faster transaction pace. 
5. Browser caching: This diminishes the number of requests to the server and optimizes the flow of data, as certain resources are saves within the browser itself. 

Conclusion

After all, data latency is not something that we should fear. Its appropriation depends on micro and macro environmental factors, which differ for every organization and industry. And when companies know about the types of latency, their uses, and purposes, they can start working towards achieving zero-latency data. Of course, according to what is best for their particular case.