6 Reasons for using streaming data analytics

Before I go into the reasons why it is important, what do we mean with streaming data? In the past, and currently still in use with many businesses, there are data warehouses that will get data in batches from operational systems.

However, we need analysis to arrive at the point of action in real-time. That’s the difference between preventing fraud and discovering fraud, a customer making a purchase or abandoning a cart, and proactive/effective and reactive/ineffective customer service.

In this article, I will give you 6 reasons to consider moving from batch to streaming analytics. 

1. The democratisation of insights: Empowering data analysts and business users

As long as there has been data, businesses have tried to use it to better understand their customers, market, and competitors. What’s changed recently is the nature of three core factors that lead to becoming data-driven: a) data availability, b) data access and c) insights access.

As these factors have expanded, or become “democratised,” businesses have enabled themselves to be better managed not just top-down, but also bottom-up, middle-out, and everywhere between an important driver to business success. 

2. Ease of use

A big part of the users of data are data analysts. They understand and have knowledge of the business challenge. In several ways their lives are being made easier, firstly by supplying them with tools they already know and are easy to learn, such as SQL. Secondly, to supply systems with zero maintenance; no need to manage the infrastructure.

On top of this the third is to seamlessly deliver data to the analyst's tools. Delivering streaming data breaks the typical “request and wait” paradigm. Furthermore, empowering business users deepens insight and is making your company more intelligent as a whole.

3. Reduce waste

Although some might think of waste as something in your garbage can, waste in consumer demand is what I am talking about here. How often do marketing and sales create too much or too little demand for products and services. While it does not look as simple as connecting supply chain data to demand data, it wastes energy and effort to overspend time, money and resources on online and offline advertising.

By having data about stock positions as soon as possible in the hand of marketeers, you can still adjust the online advertising. Better automate the pausing, stopping or replacing the online advertising for products and services which are almost or already out of stock.

4. Modelling

According to a 2019 Gartner study¹, the top challenges to adopting AI were: skills of staff (56%), understanding AI benefits and use cases (42%), data scope or data quality. Bringing more fresh data constantly in the analytical environment with no effort, makes the scarce resources more available for lifting the modelling of the fresh data.

5. Improve trust

Another benefit of having fresh or recent data available is creating a better feedback loop. Data analysts have the same date on hand as the operational systems and will report the same outcome for specific actions as the operational reporting system for the specific business function. In this case, it is easier for business people and data analysts to work together with less mapping between reporting outcomes. This increases widely the trust in the outcome of models and furthermore, the adoption will be increased.

6. Regulatory compliance violations

One of the biggest adaptations consumer brands have had to make in recent years is reforming their systems to comply with GDPR. If your data collection architecture still processes events in batch, your downstream systems may not receive consent status updates until hours or even days after opt-out. To support compliance, it’s critical for your systems to be able to process consent status changes in real-time.

How does it work?

So now you are aware of the main benefits, you might be interested in how to get things moving from a technical perspective.

At Google Cloud, the fully managed, real-time streaming platform includes Cloud Pub/Sub for durable message storage and real-time message delivery, Cloud Dataflow, the data processing engine for real-time and batch pipelines, and BigQuery, the serverless data warehouse. Google designs for flexibility and scalability and also supports and integrates with familiar open-source tools, plus other Google Cloud tools like Cloud Storage and databases.

The result is you don’t have to make compromises, as streaming and batch sources are pulled into one place for easy access and powerful analytics.

How to assess if streaming analytics is right for your business?

Not all your problems will benefit from streaming analytics equally, and getting started with real-time data can be overwhelming. There are plenty of ways to capture, ingest, and process data, and plenty of information to be gleaned from analysing your company’s data.

Which data is the right data to gather and analyse? What’s the right way to prioritise the data you want to capture in real-time, and which data can wait? To decide if streaming analytics is right for you, it helps to consider the following: 

1. Assess your current environment: identify which applications generate data in your organisation and rank those data streams based on their importance. For example, in retail, the need for real-time applications would probably rank higher for website clickstreams compared to back office payroll, given direct revenue generation opportunities. 
   
   
2. Map real-time analysis use cases to the data streams: decide which are your critical activities to improve the top and bottom line, whether it be responding to customers, detecting faulty products or enhancing security.
   
   
3. Evaluate buy vs. build: Do you have staff with the right skill sets to capture the maximum value from the technology? Do you have the resources to hire these experts? This will have an implication on time to value as you choose between an open source technology vs. a fully managed service.

Crystalloids is a Google Cloud partner that specialises in data analytics; we have executed many use cases that met our client's business needs, and technical and privacy requirements. 

 ¹Gartner, “Survey Analysis: AI and ML Development Strategies, Motivators and Adoption Challenges.” Jim Hare and Whit Andrews, June 2019.