Data growth has been explosive, to say the least. Mechanical devices were once limited to calculations, until we discovered that we could store information on them, too. Having a gigabyte of storage on a computer once seemed unfathomable, but now we’ve got up to terabytes at our fingertips. With more and more data, we need to be more and more creative and systematic about how we store and handle it.

We see data enabling various decisions, from mapping the world to fighting forest loss. As our technology evolves to greater and greater capacities, we’re also able to store more and more data–but for what?

Keeping up with data growth

Not all companies have managed to unlock the business value of the data available to them. Some executives still ask for printed reports and some employees still manually encode transactions on spreadsheets. Companies who have successfully tapped into the power of data have asked themselves this question:

How can we maximize the derived business value out of our growing data?

Data is useful for companies in various aspects, including marketing, costing, customer experience improvement, and strategic decision-making.

Changing information systems

Information systems have evolved alongside computers throughout history, taking your mom-and-pop store to a mid-size enterprise to a global retail brand. Back in the 1960s, computers recorded and encoded transactions, inventory, and other documents. This was especially convenient for businesses with large volumes of products for sale.

Business boomed going into the 1970s, and so did customer data. Data got so large that cashiers and store clerks could no longer manually do inventory or verify transaction data. Online transaction processing followed shortly to automate accuracy verification and sharing of data across multiple stores. Later in that decade, databases came to the rescue and Structured Query Language (SQL) became the standard for database management systems. This allowed mid-sized companies with two or three branches across a particular town to share data with each other.

These innovations gave rise to management information systems that access, organize, and summarize information for visibility and guide decision-making on a low level. Businesses didn’t want outsiders accessing their data, so virtual private networks kept company secrets under lock and key. Eventually, they evolved into on-premise data platforms that allow their systems to do the same things, but faster and without duplication—enabling enterprises to expand at rapid rates, especially in e-commerce.

The standard now is the data platform, but what is it? It’s easier to understand data platforms with what they do: end-to-end data handling. From ingesting, processing, storage, transformation, preparation, querying, governance, and more, data platforms do it all and enable companies to handle petabytes worth of data.

Transforming the platform

As we continued to collect and store more data, on-premise storage solutions like hard drives and file servers no longer sufficed. We’ve turned to the cloud and automation for ease and scalability to meet rapidly-changing business needs in our modern data platforms.

In the past, data infrastructure required having servers you need to manually maintain and allocate. Today, we’ve shifted to serverless platforms. Services like Snowflake and Google BigQuery allow us to store, process, and manage data. This prevents data engineers from having to act as middlemen for data analysts and project owners for manual resource allocation. Instead, they offer fully-managed solutions with minimal constant data engineering involvement. This streamlines the relationship between data analysts and product owners.

More demanding computing requirements also meant having architectures that can handle more data. This prompted data platforms to transition from the extract-transform-load (ETL) pipeline to the extract-load-transform (ELT) pipeline. This enables data pipelines to be more scalable (thanks to the cloud) and robust against rapidly changing business requirements. Extract-and-load steps are highly repeatable across organizations, enabling cheaper, more efficient, specialized ELT products. In turn, data teams can spend more time on data and insighting instead of infrastructure and maintenance.

Optimizing data retrieval and security

With data growth comes more nuanced analytics. The modern data platform enables just that —by supporting self-serve and democratized data access for query support and minimal maintenance. This allows for better access across the organization and enables useful queries at every level, providing insights whenever appropriate. Since multiple people can access data at the same time, there’s no bottleneck for getting information. The quicker turnaround time means two things: get information quickly when you need it, and generate more and better insights.

More complex systems also require more robust security, and modern data platforms find the balance between robustness and ease of monitoring through centralized data governance. This makes systems secure and easy to maintain.

How modern enterprises are maximizing data growth

Data platforms have been transformative for company operations. We used data platforms to help East West Banking Corporation automate over 500 ATMs nationwide, impacting 2 million transactions a month, and overall increasing transactions captured with half the manpower. Similarly, we’ve used data platforms to help LBC Express gain better insights with a single customer view using our customer intelligence engine. This resulted in faster processing of large volumes of historical data. We have also worked with UNDP and international nonprofit iMMAP to identify informal settlements with the goal of expanding access to humanitarian aid using geospatial data.

Modern data platforms are a necessity in present-day businesses. Google services like Drive, YouTube, and the staple search engine run on the same cloud platform they offer as a service, and Google Cloud Platform is used by big names like Verizon to deliver 5G Edge. Companies like KraftHeinz and Western Union use Snowflake for cloud computing and storage solutions. Amazon has also turned its enterprise data platform into the most widely-adopted web service, Amazon Web Services, that now enables the operations of Adobe, Unilever, and Pfizer. With the wealth of data available now and fast-evolving technological capabilities for storing, analyzing, and querying them, the way to stay ahead of the curve is to keep up.

Companies that have managed to maximize business value use data, manage it well, and invest in great talents to refine their systems. The right services and talents accelerate and optimize business operations to make better decisions and maximize resources.

If you enjoyed this post, check out our case studies on data cloud and data platforms. If you’d like to start your data transformation journey, get in touch!