Eugene Golovan Tech Lead. 15 years of experience in IT. Deep expertise in Big Data, Spark, Scala, Java, system architecture and project planning. Solid web programming & development experience.

Top 5 Big Data Challenges Every AdTech Startup May Meet

4 min read

Big Data top 5 challenges - Blog S-PRO

In recent years, the advertising technology market has made a big leap. Digital marketing has evolved into data-driven marketing. Using big data, you can create effective campaigns, conduct communication with customers, and come to them precisely once the desire for a product or service breaks out.

The advertising tools ecosystem along with the approaches and processes used with it form a technology called adtech. Therefore, big data projects have clearly become a usual thing in adtech, although it doesn’t make big data simple.

Let’s try to highlight some adtech technical problems that a startup has to solve to gain success:

  • develop a product that can be easily scaled in line with the increasing workloads and data volumes that should be stored
  • integrate with third-party services to attract new users and segments
  • identify users for targeted advertising without violating the regulations
  • aggregate analytics in a timely manner, often in real time
  • ensure the safety of any personal user data that may be available to the system

Under these points, we can see challenges that most likely each adtech startup will face in a way.

#1. Data Growth

big data growth - Blog S-PRO

The very first challenge associated with big data is likely to be the storage of all incoming information.

At the very beginning of its existence, an adtech startup hurries to launch MVP and test its ideas on the market. However, development speed is often accompanied by compromises in software quality. And when a startup takes off, it turns out that their platform is not ready for scalability. Refactoring, maintaining and scaling a monolithic code base slows down further market entry.

Once the size of the code base increases and changes must be made quickly, many companies turn to microservice architecture. Microservice architecture helps to:

  • simplify the introduction of new technologies
  • perform refactoring more efficiently
  • simplify integration using the API
  • scale the development team

In addition to this, a startup begins to use distributed computing technologies, which, moreover, often do not require additional investments. Such software as Hadoop cluster, Apache Spark, Cassandra database, Apache Flume, Apache Kafka and many others are open source software with the right of free commercial use. At the same time, many of these technologies are products that have long established themselves in the market and in some way are already the de facto standard for such tasks.

#2. Integration

Anyone who has experienced adtech knows that this is a complex ecosystem. DMP, DSP, SSP, and many other abbreviations are the things that you will have to face in the near future.

Whatever role you play in this ecosystem, one way or another you will have to face a lot of integrations.

In technical terms, a microservice architecture will be much help here. As each individual microservice will perform its specific integration task. Whether sending segments to a DSP platform or collecting reports on a Facebook campaign.

The data exchanged between the platforms have a completely different format. Therefore, a preliminary thorough business analysis, planning, and research of 3rd-party APIs capabilities will greatly simplify the task and reduce the risks of performing the refactoring.

#3. Privacy

big data Privacy - Blog S-PRO

You may have heard about the use of cookies and fingerprints regarding online tracking. Let us briefly explain each of these phenomena.

A cookie is a small text file that is stored on your computer or mobile device for a certain period of time when you visit a website. Cookies are the most common method of tracking users across multiple websites. Third-party cookies store data about the websites visited to record the user’s browsing history over a long period of time.

As for fingerprints, they are used to accurately identify unique (returning) visitors and compile reports on them. Advertisers and adtech vendors use this technology to identify and track users on the Internet, which allows them to create user profiles and show targeted ads.

Here we go! According to the latest legislative acts, including the GDPR, certain restraints in the tracking of users has emerged.

Safari became one of the first browsers to ban the installation of third-party cookies. Apple was followed by the Mozilla developers who added blocking such cookies to their Firefox browser. Most recently, Google Chrome has caught up with them. Surely, the situation with the other browsers is to some extent similar.

Thus, the tracking of users became much more complicated. And every day the situation changes. Users’ privacy comes to the forefront, and you, as a successful adtech startup will have to invent new user targeting algorithms to deliver the relevant content to them.

#4. Timely Analytics and Targeting

Of course, adtech startup is not going to just store its big data. Obviously, they will want to use this data to achieve certain business goals. Perhaps the most important tasks that will arise during data processing will be the aggregation of analytical reports, as well as the construction of targeted segments of user profiles.

To achieve this speed, the latest ETL designs and analytical frameworks are often used. As they significantly reduce the time required to create reports. Here we should have in mind that many of them, while not all, are free open source technologies. Startups tend to invest in software with real-time analytic capabilities, which allows them to immediately respond to market events.

For example, Apache Kafka is an open-source stream-processing software platform written in Scala and Java by LinkedIn and donated to the Apache Software Foundation.

Another example is Apache Cassandra. It is designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Developed by Facebook and released as an open-source project on Google code further becoming an Apache Incubator project ending as a top-level project.

It is hardly a complete list of software for big data.

#5. PII

pii - Blog S-PRO

Security and PII (Personally identifiable information) data protection is also a serious task for startups in adtech. You will need to undergo regular safety audits and certifications according to the required standards.

The data come from multiple sources and, therefore, have potential security problems. You may never know which data channel has been compromised, which compromises the security of the data you have in use. As a business that uses big data, make sure that you maintain your users’ confidentiality and security. As it happens, some data may be very attractive targets for hackers.

Implementing the Data Security best practices for securely collecting, storing and retrieving data is essential.

Conclusions

Creating a successful adtech startup with big data is not an easy task. Although if you’re already acquainted and prepared for the most challenging tasks then you have all the power to play a major role in the adtech industry, therefore, resulting in impressive growth.

A reliable helping hand of the professional technical partner can do magic and easily take you through the hardest calls on the road.

Let’s overcome the challenges together!

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Eugene Golovan Tech Lead. 15 years of experience in IT. Deep expertise in Big Data, Spark, Scala, Java, system architecture and project planning. Solid web programming & development experience.

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