Fast Data: The New Age Analytics For Enhanced Customer Experience

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Data is evolving both in terms of quality and quantity in today’s enterprises and in the past few years, changes have occurred at a much faster pace. Not long ago, Big Data was considered the next big thing for digital transformation. Technologies like Hadoop and HBase made sense as batch processing of data was the norm. But things are not the same now. 

By the coming year, 1.7 megabytes of new data will be generated every second by each person online which will be over and above the 44 trillion GBs of data already residing in the digital universe. Not to mention the immediacy with which decisions need to be made around this data and the development of technologies like sensors, IoTs, personalization, geolocation, and more. 

Considering the humongous amount of data and the need for immediacy, big data will be useless if it is not processed in a wink. Hence the concept of ‘Fast Data’ has been born and it’s picking up pace. Let’s understand the concept in greater detail.

Fast Data: The fuel for smart and immediate business decisions

Fast Data or streaming analytics has slipped in subtly but is set to transform the technology & information management industry. It is the ‘New age analytics’ and can be understood as data in motion, streaming in from millions of sources including sensors, mobile devices, financial records, logs, IoTs and much more. Big Data is basically data at rest in the form of stored structured and unstructured data which is analyzed to predict future outcomes from historical patterns. 

The concept of streaming analytics, on the other hand, requires processing in the here and now. Enterprises are moving towards streaming analytics as batch processing is giving way to data analysis in real-time for smart and immediate business decisions. 

A few years back, Macy’s, for instance, used data analysis of past customer behaviours from previous years to populate products in their stores. Today, tech giant Amazon generates personalized recommendations by analyzing your search behaviour in the last few minutes.

Big Data revolved around volume – one of the 3 V’s of data. Fast data is more about velocity and variety, as you can see from the example of Amazon. It involves multiple streams of data to make in-the-moment, business-critical decisions empowering companies to approach their customers in innovative ways.

Multiple industries like financial services, energy, retail, technology, healthcare, telecom can benefit from streaming analytics to optimize their marketing strategies and customer experience. In today’s world, businesses have gone online be it travel & transportation, banking, entertainment, health & fitness and connect customers through an application on a mobile device. Streaming analytics allows these businesses to observe how customers interact with their business and take actions in real-time.

Fast data is imparting numerous business benefits in different industries. Here are a few real-world examples in various industries in which it is delivering value for businesses.


Retail organizations are grappling with an enormous amount of data, varying data sources, and deriving useful insights from disparate data streams. They are also confounded about how the right piece of information is delivered to the right customer at the right time in their buying journey. Streaming analytics is solving both these problems. 

It is applied in the following scenarios in the retail sector. 

  • Proximity marketing

A classic example of this is McDonald’s. The fast-food chain wanted to promote its latest range of coffee-flavoured beverages in Istanbul. They collaborated with another Turkish loyalty app to push mobile coupons to potential customers as and when they came in close proximity of a McD’s cafe. The brand achieved an impressive 20% conversion rate with this strategy. 

This is nothing but fast data analytics and beacons at work. Retail stores can now track the customer’s location in real-time and send them customized offers based on their interests and past purchases as and when they are in the vicinity of a store. 

  • Personalization

Real-time streaming analytics takes personalized recommendations to another level. It combines the customers’ interests & preferences with trending offers, product combinations, inventory statistics, current promotional campaigns to implement personalization. All this relies on data mining and machine learning algorithms to come up with real-time and intelligent recommendations.

The story is not restricted to just online stores. Streaming analytics is leaving a mark on brick-and-mortar stores as well and motivating customers to leave with a bag full of purchases. 

Consider the example of eBay and fashion brand Rebecca Minkoff. They have collaborated to leverage fast data to create one of its kind ‘connected stores’. These physical stores are equipped with data-oriented smart features. 

Once a customer walks into a store, she is given a notification about available fitting rooms. The fitting rooms have mirror-cum-touch screens that enable her to choose sizes and colours which the store attendant will bring for her to try on. These “mirrors” also suggest a pair of trousers or skirts that match the customer’s blouse and then later suggest promotional offers on accessories that will go with her outfit. 

  • Ad optimization

Organizations are battling with static digital marketing efforts as they cannot optimize ad placements in real-time. Real-time streaming analytics is helping companies with dynamic ad placement by tracking user activity in terms of clicks/views, demographics, interests and combining it with marketing budgets to decide on what and what not to bid to the particular user within a matter of milliseconds. 


It is now well-understood that smartphones have revolutionized the telecommunications industry along with the usage of 4G networks, wireless calls, and internet access on phones. The deluge of data that comes from each cellphone in the form of Call Detail Records (CDRs) and Event Detail Records (EDRs) from calling and browsing history, may go waste if not utilized properly for real-time decision making. 

Today, network analytics in real-time is helping telecom companies enhance network performance and customer experience while reporting higher customer retention rates at lower costs than before. Here are a few use cases that address the pain points of telecom companies.

  • Real-time campaign management

Personalized offers and focused campaigns based on customer behaviour is now possible with streaming analytics. It utilizes data points such as call records, network usage, live location, traffic, loyalty points, and more to empower telecom companies to deliver value-added services. 

  • Event-based marketing

Fast data analytics enables service providers to target their customers through location-based advertising and geo-fencing. Once a user enters a particular geographic zone, local telecom companies can push personalized offers in the form of SMS or social media banners based on the user’s past usage. Service providers can also pitch new plans and packages by leveraging the potential service user’s mobile data and usage. 

  • Churn prevention

Streaming analytics helps identify those subscribers who have a higher likelihood of churning. Real-time predictive analytics plays a key role here by listening to all interaction with customers like network usage, location, CDRs, billing information, device & OS used that impact their experience. These data points are utilized for reacting quickly to customer concerns in order to bring down the churn rate. 

  • Preventing mobile spams 

Mobile spams and fraudulent activities prove to be detrimental for service operators by taking a hit on revenue, network bandwidth, and customer experience. Operators have been constantly looking for solutions that will help them detect such spam in real-time and respond quickly. 

Streaming analytics solutions process real-time network feeds to pinpoint such spams while they are being executed. Defining complex spam detection patterns and correlating relevant variables over time and location are the key ingredients in predicting spam instances in advance. The patterns in spam detection and alerts are recognized with real-time dashboards and this helps in automating the whole process. 

Financial Services

Today, streaming analytics has a strategic impact on financial services firms as their customer base do not rely on traditional banking anymore. The industry has become crowded with products and they are differentiated with the experience that they provide. Millions of transactions are being carried out each day and firms have to keep up. Streaming analytics is helping financial services firms to drive growth, manage regulatory changes, and mitigate risks.

Consider the example of a pioneer financial services firm in Europe. The firm had a customer base that they were serving for years together and they wanted to gain real-time, actionable insights from huge historical datasets to so that they can modernize their web & mobile applications. The solution came in the form of a fast data processing platform with the following results. 

Never-before-seen insights – The new solution could integrate datasets that were earlier disconnected to gather insights and design brand new services for their corporate partners & clients.

Tailor-made services – The company was able to unveil interconnections between their corporate customers which gave rise to more personalized services that were not feasible before.

Enterprise-level expansion – The effects trickled down to other parts of the organization as the management decided to implement the technology in other systems as well. 

In another instance, a large health insurance provider hit a wall when it discovered that its average claim processing time had increased by 10% within an astounding duration of one month. Their legacy monitoring tools were making the process even more difficult as it was eating up 90% of the time and effort of the employees involved in reaching a solution.

Thankfully, they turned to a fast data monitoring solution and it quickly diagnosed the problem. As a result, there was a 40% decrease in the time and effort of the employees who were working on the solution and a massive 30% increase in the average claim processing time. 

Enter the world of new-age analytics with Knoldus

If you want to exit the batch mode of data processing and migrate to real-time analysis for immediate insights, turn to our Fast Data analytics solutions. We help you analyze a broad range of streaming data from disparate sources to spot opportunities or risks and make smart decisions in real-time. Leverage our deep experience in scalable, distributed architectures combined with streaming analytics and open the way forward. 

Book a meeting or drop us a message here or at and we’ll help you get started.

Written by 

Ruchika Dubey is a Marketing Manager having experience of more than 6 years. She always wants to flex her creative muscles while solving real-time business challenges. She is engrossed in delivering business value by generating marketing & promotional ideas. On a personal front, she is a shopaholic and likes to travel and explore different cultures.