How Modern Data Platforms Can Help Deliver Better Citizen Experiences

What Is a Legacy Data Platform?

Monte Carlo Data defines a data platform as a “central repository and processing house for all of an organization’s data. A data platform handles the collection, cleansing, transformation and application of data to generate business insights.”

A traditional or legacy data platform, as opposed to a modern platform, isn’t cloud native. Instead, it relies on on-premises infrastructure, in which data is housed and managed on dedicated servers. Traditional data platforms are more structured and rigid, which inevitably produces data silos and challenges related to governance and duplication.

Plus, such environments aren’t always equipped to handle the growing volume of data that most organizations — particularly state governments — must deal with. As a result, traditional data platforms are now considered too inflexible and inefficient compared with modern data platforms.

What Is a Modern Data Platform?

According to Monte Carlo Data, a modern platform is made up of multiple cloud-based solutions and has either a data warehouse or a data lake at the center for storage and processing. A modern platform also often has capabilities for data ingestion, orchestration, transformation and observability.

“Modern data platforms are sustainable, flexible models built on four foundational pillars: versatility, intelligence, security and scalability,” Rowan says. “Their differentiator lies in the fact that they can ingest, process, analyze and present data generated by all systems and infrastructures within an organization, featuring a rare combination of data and detection.”

Modern data platforms are based on cloud-based solutions that give organizations the ability to analyze data quickly and effectively in an elastic, scalable environment.  Such a platform is key to enabling enterprise resilience and innovation and effectively allows you to work with any data set, regardless of what it is or where it’s stored. Centralizing data into a data platform eases management burdens, allowing greater control over critical functions such as security and observability.

DISCOVER: How state and local agencies can modernize to enable data-driven decision making.

How Do Modern Data Platforms Support Storage and Processing?

To be modern, an organization’s data platform needs a few key components. First, it has to be made flexible by leveraging public cloud hyperscalers — also known as large cloud providers — or even Software as a Service platforms. This kind of environment offers nearly unlimited data storage capacity and lets organizations run a number of processes and analyses without additional physical hardware. Modern cloud platforms often have built-in tools for streamlined data processing, analytics and visualization.

A modern data platform must also have a robust data governance strategy to ensure the responsible management of data. With a more simplified data environment, organizations can implement data governance more easily than they might within legacy environments.

Another key component of a modern data platform is data democratization, with information no longer siloed. Instead, data is available for analysis across the organization to facilitate better decision-making, help uncover new efficiencies and identify areas for improvement.

What Are Data Ingestion and Data Pipelines?

According to TechRepublic, data ingestion is “the process of shifting or replicating data from a source and moving it to a new destination … The data moved and/or replicated during data ingestion is then stored at a destination that can be on-premises. More often than not, however, it’s in the cloud.”

Essentially, ingestion involves collecting data from different sources and moving it to a centralized data warehouse or data lake. A data warehouse is a system used to analyze structured data from multiple sources, while a data lake collects and stores unstructured data. Being cloud-based, modern data platforms position organizations to ingest data more easily.

A data pipeline, according to IBM, is “a method in which raw data is ingested from various data sources and then ported to a data store, like a data lake or data warehouse, for analysis. Before data flows into a data repository, it usually undergoes some data processing. This is inclusive of data transformations, such as filtering, masking and aggregations.”

The two main types of pipelines are batch processing pipelines and stream processing pipelines. A batch processing pipeline processes large volumes of data, and as Amazon Web Services points out, is more suitable for tasks such as monthly accounting that happen infrequently and deal with large batches of information. A stream processing pipeline processes a more consistent flow of smaller-volume batches of data, which can be used for real-time analysis and more short-term measurements.

LEARN MORE: How data center optimization helps agencies do more with less.

What Is the Role of Data Discovery in Modern Data Platforms?  

Data discovery involves collecting data from multiple sources, classifying it, then applying advanced analytics to identify patterns and valuable insights to inform better decision-making. Splunk’s Bill Rowan says that data discovery helps organizations better understand their assets; with access to more data than ever, this is absolutely critical.

“Data is key to future business and mission success, which means data discovery is essential to modern data platforms,” Rowan says. “AI- and ML-driven data discovery allows information to be analyzed efficiently, building an analytics strategy around raw data that helps to identify digital transformation opportunities and future-proof businesses.”

What Is Data Observability?

Data observability is the ability to understand the overall health of your data and systems by monitoring events across your environment. Data observability practices involve monitoring and managing data using tools that employ automated monitoring, triage alerting, tracking, comparisons, root cause analysis, logging, data lineage and service-level agreement tracking, IBM notes.

Rowan adds that observability gives IT teams more control over their systems’ functions.

“Organizations with multiple IT environments working on a variety of tasks spread across a network generate large amounts of data, which can be difficult to analyze and troubleshoot. As organizations become more dependent on data to drive efficient business processes, observability allows teams to manage all different kinds of data and helps answer questions about the system’s current performance,” he says.