Data is the fuel of the digital economy, so data-centric organizations have a distinct advantage. To remain competitive, organizations must have a data management strategy in place to effectively ingest, store, organize, and analyze data while ensuring that it is accurate and accessible. However, creating a future-proof data management strategy is complex, given emerging technologies such as cloud and big data, as well as the need for real-time data.
Additionally, organizations are impacted by new regulations on data protection and privacy, and the use of personally identifiable information (PII). More business users and regulators require that the entire “factory” that delivers data becomes more transparent, and this implies more up-to-date data catalogs and metadata.
Here we cover some of the critical challenges that need to be overcome in order to create an effective data management strategy.
Real-Time Data Access
To quickly adapt to market changes and support real-time analytics use cases such monitoring consumer behavior, optimizing ads, and offering relevant product recommendations to consumers, organizations need access to real-time data. However, the data architecture in most organizations is not designed to support this. The most common approach to business intelligence (BI) and analytics involves replicating data from source systems into storage solutions like data warehouses and data lakes, using multiple extract, transform, and load (ETL) processes. While this approach is suitable for regular business reporting, it does not support real-time analytics use cases.
The Capability to Fully Leverage Big Data
In order to carry out advanced analytics, organizations need to be able to store and analyze a growing variety of big data sources. This includes texts (such as contracts and social media messages), voice messages (such as conversations between air controllers and pilots), images (such as accident damage photos), and videos (Such as those taken from security cameras at airports and retail stores). Organizations also like to store data resulting from new business programs; streaming data that needs to be pushed to real-time streaming applications; data from wearable devices, such as game controllers; and telemetry data from connected devices. Regardless of the analytics, the huge volume and variety of big data will directly impact the data architecture.
Cloud Platform Interoperability
Cloud computing technology is growing more quickly than ever, and data integration platforms are streamlining connectivity and crossing platform boundaries, making hybrid and multi-cloud architecture the de-facto standard. New data architecture strategy should support cloud platform interoperability. This would also make it possible to carry out reporting and analysis for business cases that require data to be pulled from multiple cloud platforms.
Data science enables organizations to find hidden patterns in data through analytical models. These analytical models are created using techniques such as statistics, deep learning, machine learning (ML), and artificial intelligence (AI). However, several studies have shown that data scientists often spend 80% of the time on data preparation tasks such as data cleansing and data exploration and only 20% of their time creating predictive models. A modern data architecture plan therefore should contain tools that enable data scientists to focus on their core skills.
The Ideal Data Management Strategy
The analytical technology landscape has shifted over time. Businesses today are not limited to performing simple operational reporting. It is the age of using advanced analytics to solve complicated business challenges. From a technology standpoint, businesses need a flexible data architecture that provides a logically consolidated view of all enterprise data. Such an architecture can provide consuming applications, business users, and advanced analytics teams with data whenever they want, without having to worry about where the data is stored and in what format. A data virtualization platform can be implemented as an enterprise semantic layer, acting as the core of such an architecture. Data virtualization delivers a simplified, integrated view of trusted business data in real time, as required by any consuming application, business user, or data science team. Data virtualization integrates data from disparate sources, locations, and formats, without having to replicate data to a central repository, to create a single, unified data access layer that delivers data to multiple consuming applications. The result is real-time data access to enterprise data for a lower overall cost.