What is a Data Platform? And How to Build An Awesome One

Unlocking the power of data isn’t just a tech trend – it’s a game-changer for your business. Or do you disagree?

With relevant data at your fingertips, you can provide new products and services as well as create more profit streams. But getting there necessitates developing a comprehensive BI solution – a data platform. Data platforms support decision-making by providing timely and accurate information and also enable analytics that can foresee trends and opportunities. Through big data analytics, progressive leaders turn the decision-making process into a competitive advantage. And here is why – a transparent and open information culture drives operational process enhancement, opens up a 360-degree view of the customer, and helps determine methods that add value for business. 

Savvy leaders know this is their competitive edge: for organizations looking to capture the full potential of their information and AI, implementing a robust data platform is not an option – it’s a necessity. But what are big data platforms, and why are they so vital to contemporary business operations?

What is a Data Platform?

The companies that deal with data collection, processing, storage, and analysis require robust technology and a modern approach to help them complete all these stages seamlessly. The absence of a Business Intelligence strategy often leads to a lack of thorough data analysis, structuring and retrieval problems, inefficient sales, poor customer experience, etc. And this is where a data platform comes to the rescue.

The data platform definition is simple: this software acts as a central repository for all information, distills it into one source of truth, and enables scaling up sophisticated advanced analytics programs that interpret raw data into commercial value. This integrated solution collects, manages, and covers the entire data life-cycle, visualizes it conveniently, and processes outputs from various sources. Creating a data analysis platform offers many advantages, including efficient data management, scalability, actionable insights, better representation, and increased customer loyalty.

A good data platform as a service will:

• Inform. Customers pay attention when they’re offered helpful and useful insights.
• Connect. Data can build connections, especially when it meets customers at the right time. Data-driven apps and personalized experiences can connect users to brands.

This isn’t optional – it’s essential. Agree?

For the customer, it’s all about getting the information they want, when they want it, and how they want it. With the rich mobile access available to users, this is increasingly a multi-channel, multi-device challenge that requires instantaneous responsiveness. Also, with the vast datasets collected by devices, from wearable wristbands to the cars we drive, there is a greater need for a secure and scalable platform to access, manage, and deliver personalized content.

So, how do you build an awesome one? Let’s break it down.

Building a Data Platform

The success of an enterprise data platform heavily depends on its functionality. The tech stack will look substantially different for a 1,000-person e-commerce firm than it will for a 100-person Fintech startup and the precise details can be defined only by a data platform architect. 

The more thorough the approach to determining the feature set for the analytics platform, the easier it will be for potential users to fulfill their objectives. As a result, the first stage in developing analytics software is to do a thorough analysis of the target audience that will be using the product. You need to focus on the following parameters while designing your software:

• Type and size of the companies that will use the solution;
• User requirements for analytics tools;
• The volume of data within the organizations;
• User types (administrators, managers, occasional analytics users, etc.);
• The importance of collaborative tools, etc.

So, let’s consider the modern data platform development process in detail. It includes five vital steps listed below.

Start with Cloud Storage

The data platform engineering starts with selecting a cloud storage solution that meets your needs for scalability, reliability, and performance. For example, you can choose AWS application development. Here we outlined the most important considerations at this stage:

• AWS and Azure are popular choices for storing large volumes of unstructured data.
• Both platforms provide high availability, durability, and security features to ensure your information is safely stored and easily accessible.
• Both AWS and Azure offer pay-as-you-go pricing models, so you can manage costs based on your storage needs.

Implement Data Integration Tools

It’s best to use integration tools to integrate information from multiple sources into a unified system. They assist in extracting, transforming, and loading (ETL) data efficiently. An example is Apache Nifi – a robust open-source tool that supports various integration scenarios. So, you need to ensure that your chosen tool can map data from diverse sources accurately.

Choose a Processing Engine

When building a data platform, choose an engine based on your specific performance needs, whether for batch or stream processing. Also, we advise that you check the processing engine integrates well with your storage and integration tools.

A processing engine handles the computation and analysis of data. One that can process large volumes quickly and efficiently is Apache Spark, a leading open-source engine known for its speed and ability to handle complex processing tasks. It supports in-memory processing, which enhances analytics.

Add an Analytics Layer

To achieve the best user experience, we recommend choosing tools with user-friendly interfaces to ensure accessibility for non-techies. Also, it is highly important to make the analytics tool support customization and integration with your data sources and processing engine.

At this step, you need to incorporate an analytics layer like Tableau or Power BI that enables users to visualize and interpret data effectively. It should provide intuitive interfaces for querying and reporting. Any BI solutions usually have the most fascinating part, i.e. Data visualization is the most fascinating part of any BI solutions. You can create customized dashboards using our business intelligence services and look transparently at business performance.

Modernize Your Data Governance

Last but not least, you should take care of effective data governance as it is responsible for its quality, security, and compliance.

Current governance approaches often need to be more relaxed and sensitive to the business environment. By 2027, for example, 60% of enterprises will fail to realize the expected value of their AI use cases due to ineffective governance frameworks. To avoid this outcome, integrate best practices into your data governance strategy:

• Create an efficient governance structure;
• Develop and implement effective policies and standards;
• Assess and enhance data governance performance;
• Create a process for iterating and learning;
• Adopt a new attitude to data.

Follow these methods to turn your raw data into actionable insights.

Case Studies and Examples

Making data a strategic asset means that employees can generate rich analytical insights, which in turn can enhance each aspect of the business. So, let’s consider data platform examples where implementing them is a game-changer for an organization.

1. Maintenance

A manufacturing company uses data platform capabilities to collect indicators from sensors on machinery and production lines. Predictive analytics models forecast equipment failures and schedule maintenance before issues arise.

Results: Reduced downtime, extended equipment life, and lower maintenance costs.

2. Inventory optimization

A large retail chain monitors sales, supplier deliveries, and inventory levels in real-time with an analytics platform. This assists in optimizing stock levels, reducing overstock and stockouts, and improving supply chain efficiency.

Results: Reduced inventory costs, improved product availability, and enhanced customer satisfaction.

3. Omnichannel customer experience

A global retail chain integrates data from physical stores, e-commerce platforms, mobile apps, and customer loyalty programs into a unified platform. This allows them to track customer interactions across all channels and create a smooth shopping experience.

Results: Consistent customer experience, higher customer retention, and increased sales through personalized marketing.

4. Real-time portfolio management

A cryptocurrency investment firm uses a platform to monitor and manage client portfolios in real-time. It integrates information from various exchanges, wallets, and blockchain networks to provide a comprehensive view of asset performance.

Results: Improved decision-making, timely portfolio adjustments, and enhanced client satisfaction.

5. Automated SaaS financial reporting

An accounting firm uses an analytics platform to automate the collection and consolidation of financial details from various sources (e.g., ERP systems, bank statements, and invoices). The platform generates accurate financial reports in real-time.

Results: Increased reporting efficiency, reduced manual errors, and timely financial insights.

6. Blockchain analytics and insights

A blockchain analytics company gets insights into blockchain activity, such as transaction volumes, network health, and miner behavior. This data is sold to institutional investors, researchers, and regulators.

Results: Enhanced transparency, better decision-making for stakeholders, and increased market trust. 

Practical Tips for Creating an Analytics Platform

Building a robust platform is not limited to choosing the right stack and modern data platform architecture. You should also follow a practical data platform strategy for the sake of its effectiveness and sustainability. In this section, we will share some key practical tips.

Start with a Minimum Viable Product (MVP) and Iteratively Improve

Involve End-Users Early in the Design Process to Ensure Usability

Implement Automated Monitoring and Alerting for Proactive Issue Resolution

For example, you can take a look at a custom monitoring platform developed by Broscorp. The platform we built features real-time monitoring – so, issues are not missed and clients’ business operates 24/7. We added metrics that help diagnose clients’ environments at earlier stages and prevent even more outages.

Future Trends in Enterprise Data Platforms

The big data sector is rapidly expanding. According to Statista, around 328.77 million terabytes, or 0.33 zettabytes, of information are created every day. This corresponds to approximately 120 zettabytes each year. The massive scale reshapes the data platform arena.

Let’s consider some core big data and data analytics trends for the coming years.

Increased Investment in AI and Machine Learning Models

An investment in AI and MLMs is on the rise. It provides advanced data processing and insights, supporting decision-making across industries. The growing availability of AI tools and services makes these technologies accessible even to smaller businesses. This broadens their impact and drives innovations in various sectors. According to Gartner, this sector will grow even more significantly:

 61% percent of the midsize enterprise CIOs surveyed plan to adopt AI-related technologies, and 18% plan to adopt data and analytics in 2024. However, without a data-driven culture, MSEs may achieve some tactical success in providing information to business users, but they will miss many opportunities.

Streaming Data

This refers to processing massive data volumes from various sources, so the infrastructure and software architecture needed to support real-time data streaming can be complicated and challenging to manage. Currently, not so many businesses have the internal resources, skills, and know-how to create and run such systems. However, this direction is gaining momentum and according to CB Insights, the stream processing market is estimated to reach $52 billion by 2027. There’s a solid reason for this: modern data platforms make it much easier to manage.

Data Lake Market Growth

Data lakes provide a centralized repository for storing large amounts of structured and unstructured data; they allow businesses to extract relevant insights using AI and machine learning. Mordor Intelligence forecasts a nearly 30% compound annual growth rate for the data lake market through 2026. This substantial development is being driven by the massive amounts of information generated by organizations, particularly SMEs. With the growth of IoT devices, social media usage, and digital transactions, organizations are increasingly producing petabytes of data. This is where arises the need for a cheap and scalable storage solution, which data lakes supply.

Conclusion

Raw real-time data can be turned into strategic assets that your company can use to drive business operations and gain a competitive advantage.  What if your data platform could not only meet today’s needs but also prepare you for the future? At Broscorp, we don’t just deliver solutions – we create tailored platforms that are in tandem with the business goals.

Efficiency, innovation, and growth are not mere talks, but the results that we bring about through every project. We understand that when your customers are happy, it leads to higher profits. That’s why we design data platforms focused on your success today and tomorrow.

Are you ready to unlock the power of data? Contact Broscorp and we’ll bring your idea to life.