Why is data aggregation a crucial step in building successful online marketplace solutions?


What does data aggregation mean and what is a data aggregator platform?

Did you know?

There are 2.5 quintillion bytes of data created each day in the world. What’s your company’s share in this?

And did you know how much this data means for your company in the digital world? Well, it comes as no surprise that data science ranks #1 in LinkedIn top jobs with a 4.7 job score. That IBM, Amazon, and Microsoft have the largest data science workforce is no surprise either. The secret: They’ve mastered the core concept of data; Data aggregation!

Could data aggregation also be your pathway to greater values? It definitely must be.


What is data aggregation?

Data aggregation refers to a process that involves gathering information and expressing it in summary form for statistical analysis or other purposes. In many cases, data aggregation is exclusively done to get more information about particular data sets based on explicit variables such as profession, age, or sex.

Raw data can also be collected and gathered over a specific time period and aggregated to provide summaries such as maximum, minimum, average, sum, and count. This information can then be applied for many benefits including website personalization for targeted content and advertising to individuals belonging to these groups.

A good use case of data aggregation can be how it drives the finance industry. Financial account aggregation compiles information from different accounts including credit card accounts, bank accounts, or investment accounts to present it in a single, easy to access place.

Aggregator platform

Data aggregator platforms are used to fetch and compile data from multiple sources and that makes deriving of insights and patterns-tracking easier and more accurate without losing source data lineage. These platforms are particularly helpful in big data analytics. Data aggregation is particularly useful to make all collected data sensible and of utmost importance to a business that wants to utilize this information for strategic decision making. The best platforms are ones that give a wide breadth and in-depth data collected from myriad sources, clean the data, and analyze it before sharing the data as a single source. Clarity, simplicity, and speed are also characteristics of a good data aggregator platform.

When do you need to aggregate data in real-time?

Real-time data aggregation taps into streams of multiple sources such as GPS data and continuously compiles that data to provide real-time customer information. This, in turn, allows the management, tracking, and analytics of live flowing data. Aggregating data in real-time requires adequate awareness and insight into choosing an appropriate timing for weighing business readiness. To make the most out of data, real-time aggregation of data is important; and even more so when:

You want to give deeper insights into the business using data visualization:

Visualization of business information is a vital component in managing the key performance indicators (KPIs). When viewed in real-time, KPI data gives a single source of truth and provides updated data that offers a bird’s eye view of business performance at any given time.

You want to monitor customer behaviors to derive useful insights:

You need to aggregate data in real-time when there is a need to get further insights into customer behavior. Real-time data allows a business gain visibility into products bought by customers, what they are not buying, what they prefer and what they dislike that helps in timely responses and actions.

You are seeking a competitive advantage:

With real-time data, a company can identify trends, and craft forecasts of the industry. Real-time data is a great asset for innovation, competitiveness, and a great brand reputation.

How do you identify the need for data aggregation?

Increase in data and data sources comes with increased data management challenges. With these challenges, it is fair to say that every company that appreciates the potential of data in increasing sales and revenue needs to aggregate data.

But what about real-time aggregation of data? How do you identify the need to aggregate data in real-time? The following lines outline common indicators that your business needs real-time data aggregation.

You need instant information to evaluate an initiative:

Do you need to understand the impact and performance of a project, product, or an initiative? Real-time aggregation of data gives a visual representation of data that helps evaluate progress and also understand whether there is a marked improvement or not.

Your stakeholders need clear and up-to-date reports:

You may have found yourself in a certain situation where everything you try to explain to stakeholders doesn’t make sense to any of them. This is an indicator you need to consider real-time data aggregation that helps you explain the situation as-is using simple and self-explanatory dashboards.

You are looking to expand your business and modify current services:

Real-time data aggregation will help you reach out to more customers and improve customer satisfaction by instantly replying to their queries.

The primary purpose of real-time data aggregation

Confusion and overlapping of data is a common challenge for every organization sharing information across departments and through multiple communication channels. A real-time information system is vital in this case to increase the productivity and efficiency of the business.

The primary purpose of real-time data aggregation is the timely utilization of crucial and insightful data by employees. This is further enhanced to keep the operations smooth by creating a single reference point such as a dashboard to allow instant access of information by different people at different individual levels.

Real-time data aggregation helps evade risks by keeping the business away from “inevitable uncertainties” as they are called. This, in turn, reduces costs and saves on time that can be used in product improvement and customer services.

We will cover the following topics:

  • Tools, Industries, and Processes for Data Aggregation
  • Online Marketplace Business Model
  • Algorithmic Trading and Smart Trading Tools
  • Microservices Architecture and Containerized Applications
Our process

Tools, Industries, and Processes for Data Aggregation

Tools/architecture required for data aggregation

For a data aggregation process to be effective, efficient, and fast, there exist tools to help in every step of the process. These tools are used to combine data collected from multiple sources to one reference point.

The tools are well configured to capture features beyond rows and columns that are characteristic features of common tools such as Excel. They present a great platform for several calculations and provide high level summarized statistical results.

Like many other software tools, data aggregation tools require careful evaluation before picking since not all tools will have best-fit features for your business needs. The tools must employ specific architectural models as the building blocks to the best products.

The data aggregation architecture provides for integration APIs. These sets of protocols are used by aggregation services providers and allow entities access to new markets and customers from their collections of insight global data from banks and other sources. Several integration API protocols exist and are included in the architecture of most providers in different combinations. Common protocols include:

  • SOAP (Simple Object Access Protocol)

    operates with the two basic functions – GET and POST. GET is used to retrieve data from the server, while POST is used to add or modify data.

  • REST (Representational State Transfer)

    in its turn, changes the state of the corresponding source by making a request to the URI (Uniform Resource Identifier).

  • GraphQL

    leverages requests of two types – queries retrieving data from the server, and mutations changing the data.

Data aggregation as a service

Data aggregation as a service is a cloud computing model in which a provider delivers data aggregation hardware and software tools to users over the internet. This, however, does not mean that the third-party will take all the business data activities.

Instead, data aggregation as a service offers companies access to volumes of rich data available in the most innovative form. In this case, a business buys access to a large data set and integrate it with their on system.

A subscription to data aggregation as a service will provide access to cloud-based database of insightful and summarized data. This data can be customized to fit individual organizations. This information is accessible from anywhere in the world to any employee provided they are connected to the Internet.

How data aggregation can be applied to various industries that are ripe for it and how they will benefit

It is without any doubt that data is the future for every effective company. Be it healthcare, finance or e-commerce, top business executives and strategists have embarked on data doctors for strategic plans and business growth.

The reason is simple. Making sense from tons of raw data is a tough and tiresome endeavor for business strategists. But this is now changing thanks to data aggregation. Industries can now tap into this impactful technology and unleash a better version of themselves. Data aggregation can be applied in several industries such as:

  • Fintech

    Fintech industry has the most benefits to gain from data aggregation. It is mostly applicable in maintaining transactions and bank accounts in one place. Automated wealth management and personalized financial advice are other application benefits.

    Additionally, in this century, best finance companies are ones which are able to tap on accurate, up-to-date news and financial trends. These financial firms can use data aggregation to gather headlines and use these data for market intelligence insights through predictive analytics.

  • Healthcare

    Data aggregation can be applied in the health sector in summarizing patients’ habits through drugs, doctors’ data and finance. It can also be applied in maintaining patients’ records and reports.

    Doctors can use aggregate data to identify common symptoms that might predict the course of an infection and to get insight on the best treatment for a disease. This data can be very useful for crafting prevention measures.

  • E-commerce and retail

    Data aggregation can be useful in understanding customer shopping behavior, targeted shopping recommendations and product information aggregation. It is especially an effective tool for competitive price monitoring.

  • Retailers

    Retailers use data aggregation in their research of what they’re up against by gathering new and up to date information about their competitors’ prices, product offerings and promotions. This information is pulled from websites where competitor products are listed.

  • Transport and travel (Shipping and automobiles)

    Data aggregation will help in improved mapping of demand and seats, automotive predictive maintenance and traffic regulations.

    Travel industry can benefit by using data aggregation to gather data from customers' conversations. This data can then be used for customer sentiment analysis to determine voices infections, language and expressions used to calculate emotions that helps in product improvement decisions.

  • Supply Chain management

    In supply chain, data aggregation applies in customized suppliers management, value chain management and autonomous transport vehicles.

    Aggregate data such as customer demographics can be used by company financial officers in crafting budget allocation plan across the supply chain, be it product strategies of marketing.

    With quality and rich data, these industries are able to make strategic business decisions that results in reduced cost and increase in revenues.

Data aggregation process

A data aggregation process involves the following steps:

  • Data requirements specifications

    This step involves identifying necessary data as inputs to the aggregation process based on a hypothesis or an experiment e.g. population and population variables such as age, gender, and income.

  • Data collection

    This involves the process of gathering information on identified variables. This information is pulled from a wide range of sources to give a wide dataset. Accuracy and honesty involved in this process plays a major part in ensuring the final deliverable is meaningful and effective.

  • Data processing

    This process involves organizing the collected data for aggregation. Structuring of the data is done as required for the aggregation tools.

  • Data cleaning

    Data cleaning is the process of correcting errors such as incompleteness and data redundancy. Different methods of data cleaning exists for different types of data.

  • Data analysis

    This involves application of various analysis techniques to understand, decipher, and derive relations among variables to draw a conclusion based on defined requirements.

  • Communication

    The results of the data aggregation process are dispatched to the users in the clearest and simplest format. Visualization of data using graphs and charts with color code highlighting will help in effective communication.

Online Marketplace Business Model

Online marketplace business model

An online marketplace refers to an ecommerce site or application that organizes services and products of different companies and sells them on its own platform. In this case, the firm serves as the mediator between the owner of the products and the consumers. The firm makes a profit from commissions charged for every successful transaction.

How to build a unified platform with data aggregation techniques.

A unified platform is one in which data is merged from multiple sources into one single central place. The best way to tier this data together is through a unified application. This platform can be built using data aggregation techniques. An effective technique is one that strikes a balance of high scalability and usability. Some of the features to consider when building these platforms include:

In the case of Bitcoin and other cryptocurrencies, these ledgers consist of a series of financial transactions that track how the coin has changed hands over the years. However, the same idea can be applied to all kinds of information, including how resources, information, or data are transacted among users.

  • Integration

    The data aggregation technique should integrate with multiple data sources.

  • Reliability

    This guarantees timely and secured access to the platform.

  • Latency

    Ability of the platform to allow real-time access of data.

Architech challenge

What are the architectural challenges in building a marketplace model?

  • Efficient searching and matching

    A marketplace encompasses diverse sellers, products and services information. The diversity in individual seller products, buyers.\, and currencies is a common characteristic of a marketplace model.

    The main challenge in building a marketplace platform is developing an architectural model that is designed to help buyers and sellers find each other without much effort and in a timely manner.

  • Security

    The security of a marketplace model cannot be negotiated. It is the most vital organ of every online platform more so when it means brand reputation and trust. Developing a marketplace that makes sellers and buyers feel secure is a dream for every company. A great challenge in building a marketplace model lies incorporating security measures in the design. These are measures that will secure every transaction and assure minimum disruption to business in the case of a disaster or any mishap.

  • Adequate pricing

    How do you ensure constant income for your business in different situations? In every business, there will be times when demand and supply patterns change given that the external environments are dynamic and will change from time to time. You don’t want your business to suffocate during these seasons. A real design headache. How do you ensure the architecture captures these seasons?

Difference between an aggregator model and a marketplace model

The aggregator and marketplace models share a lot of common features that one may think they are used interchangeably. There however exists many differences that tell an aggregator model from a marketplace model. In other words, these are the differences between Uber (aggregator) and Amazon (marketplace).

  • The brand name difference

    In a marketplace, the brand is a collection of different brand names. Well put, the brand is the bigger marketplace picture, which contains different vendors who sell services and products under their own brand names.

    In aggregator model products and services are provided under a common brand name. In Uber for instance, taxi-operators provide services under Uber brand name.

  • Product quality

    In a marketplace, different products provided by different vendors come in different qualities. For aggregator model however, standardized quality is offered across the brand. They work in making sure product provided by partners are of similar quality.

  • Product pricing

    Different vendors under marketplace set their own product prices. It is therefore common to have the same products with different prices. On the other hand, aggregator model has standardized product prices. An order, in this case, is given to the provider who is nearest to the buyer.

Algorithmic Trading and Smart Trading Tools

Algorithmic trading or algo trading refers to a trading system which use advanced mathematical tools to facilitate financial transactions decision making. Algorithmic trading aims at helping investors execute financial strategies as fast as possible for higher profits.Smart trading tools involves expert advisor tools that help investors improve trading execution and management while optimizing trading strategies and managing risks.

 algorithmic trading

Process of algorithmic trading development

Developing a trading algorithm involves a process that starts from goals to testing and actual trading through the following phases:

  • Define goals and objectives

    Goals include things like the market you want to trade in, and your desired returns and drawdowns. Drawing solid goals and objectives will keep you alert on your path to satisfaction while helping avoid risks.

  • Find an idea for building the strategy

    An idea forms the starting point of coding an algorithm. The best ideas are ones with a clear explanation behind them. It is a good move to test every idea you get before settling on specific one.

  • Developing an algorithm

    With clear goals, objectives and roadmap, it is now the time to put hands on the real thing. The trading algorithm. This step involves following best practices and doing random tests to ensure the final system is based on the drawn strategy and is built for effectiveness.

  • Testing

    Testing is done to ensure the deliverable is valid and verifiable. Several tests are carried out before the system is deployed.

  • Turning the strategy on

    After passing the tests, the strategy is now ready. At this step, you turn the strategy on and start trading with real money. The strategy can be automated on your machine or a virtual private server.

Industries where algorithmic trading can be used

  • Stock market

    Algorithmic trading can be applied in stock markets to among many things automated buy/sell orders, generate trading signals or indicators, market movement forecast and risk management.

  • Retail Trade

    TRetail traders can use algorithmic trading to automate the retail process which in turn increases the number of trades for more profits. Also with predetermined entry-exit rules, a retailer can use algorithmic trading to avoid losses that arise from poor decisions.

  • Finance

    Algorithms are proven faster, smarter and more efficient than humans in finance newsfeed analysis, earning statement analysis and financial performance evaluation.

Benefits of smart trading tools

  • Smart trading decisions

    Smart trading tools allow both automated and simulation trading within its program. With simulation trading, investors can practice in the market without real money. Automation provides fast and consistent way of trading. This combination helps traders make quicker and streamlined decisions.

  • Fundamental analysis of data

    Smart tools are able to filter quickly through tons of data and give a shortlist of best match companies that match your criteria.

  • Streamed procedures

    Smart trading tools simplify trading by automating the procedures that are otherwise manually done by the trader.

  • Competitive advantage

    Smart trading tools allow real-time access of information from anywhere that positions the user at a competitive advantage.

Microservices Architecture and Containerized Applications

Containerized applications

Application containerization is the process of bundling an app and its related configuration files, dependencies and libraries together for efficient and safe running across multiple computing environments. Multiple isolated components and services have access to the same OS kernel and run on a single host.

Containerized applications for automated trading

Containerized applications are mainly used to simplify developing process, testing and production flows for cloud-based services. This is no different with developing automated trading systems. In this case, containerized applications help in:

Instant operating system start-up

Replication of containers help easy deployment of automated trading systems

Containerized applications help achieve enhanced performance with higher security.

Scalability is another important benefit of containerization as it makes it possible for more app instances to fit in a machine unlike if each was on their own.


Difference between monolithic and microservices Architecture

A monolithic architecture is one in which the parts that makes the application such as code, business logic and database are built in a single, unified unit. In monolithic architecture, client-side and server-side application logics are defined in a single massive codebase. This means that any change to the application needs to be built and deployed for the entire stack all at the same time.

The microservice architecture on the other hand involves developing an application as a collection of small services with each service running independently. These services communicate with each other using HTTP resource API. The services are written purely in business terms and deployed independently. The business APIs are standardized in a way that consumers are not affected by changes in application services.

Benefits of building your system on a microservices architecture

Most of the benefits of microservice architecture lie in the business-oriented values to the company including:

  • Great components organization

    Microservices architecture provides a well-organized structure with every service performing its job independently without minding other services in the application.

  • Service decoupling

    Decoupling of services in microservice architecture makes it easier to reconfigure and recompose the service in order to serve different apps. This also enables fast delivery of parts for larger integrated systems.

  • Application resilience

    Microservices architecture increases the run-time of the system for greater performance. Resiliency is achieved through dispersing system functionalities across various services.

  • Increased revenue

    Revenue increases with reduced downtime and an increase in iterations achieved through microservices architecture model.

Legacy migration and cloud-hosted systems

Legacy migration is the process of importing traditional systems, and data in them, into a new platform, media or format. The term is mostly used to describe the process of moving organization’s legacy systems and data to the clouds from the traditional data centers. This process is orchestrated by various drawbacks of legacy systems including:

  • Non flexibility

    Building new features to meet new business requirements requires massive efforts in legacy system. Most times this process will mean re-engineering the whole system.

  • Lack of IT staff

    Legacy systems are built on bygone technologies which young and current IT professionals may not want to put their hands on it.

  • Expensive

    Old hardware used in legacy systems use excessive power and resource time making it expensive for the company.

Cloud-hosted system

A cloud-hosted system is one whose hardware and software resources exist in the internet and are managed by high-end server computers to allow access of data from any corner of the world as long as the user is connected to the internet. Some of the features of cloud-hosted systems include:

  • Resource pooling

    Cloud-hosted systems have access to multiple computing resources pulled together through a multi-tenant model. These resources can be assigned and deassigned depending on client’s demand.

  • On-demand self service

    cloud providers offer clients the ability to continuously monitor the computing capabilities including server uptime, network traffic and storage.

  • Network access

    Users of the system can access data from anywhere, through any devices as long as they have an internet connection.

    While it is a great move to migrate from legacy systems to modern, the process is not as easy. You will face challenges along the way that may seem to halt your plans. Knowing these challenges beforehand may prepare you for the process. Some of these challenges include:

  • Change management

    The task of managing the change from a traditional system to a modern one requires both technical and managerial expertise to ensure minimal interruption to programs and effective training of users.

  • Co-existence of two systems

    Due to phased approach of data migration, time will come when the two systems will be running together before full transition. In this case challenges in handling data duplication, overlapping become inevitable.

  • Expensive

    Modernization process involves large investments of money and time. For complex systems, this process may go for years hence requires a lot of capital.

    By now, you must be convinced about how automating data aggregation capabilities can add a significant value to your business, especially in Fintech and banking operations. For more on our data aggregation solutions, please check our portfolio.

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