Logical data fabric: knitting together enterprise data
As the digital economy grows and organisations accelerate their plans for digital transformation, the capture and use of data becomes ever more critical. COVID-19 has accelerated the need for change as businesses pivot to hybrid working models and optimise their customer-facing assets, such as applications and other go-to-market tools.
However, with valuable data now being ingested from a vast array of points across the enterprise, the rise of data sprawl has posed a real challenge for many organisations.
PricewaterhouseCoopers' 22nd CEO Survey found that 95% of local CEOs see their company success ‘resting upon better use of customer and client data.' A recent IDC report found that more than 80% of IT leaders reported data sprawl as one of the most critical problems they face today.
With such a deluge of potentially useful data, many struggle to make full use of it. The data is often stuck in disparate silos, making it difficult to find and govern, and time-consuming to include in traditional reports. Therefore, turning data into intelligible insights is often problematic at best.
Data management has evolved significantly since the 1970s when we first stored and accessed it via different spindles and punch cards. Through systems like Hadoop, data storage and retrieval have greatly enhanced – but the modern enterprise is still faced with an influx of data that makes even these modern systems obsolete. Logical data fabric can be the contemporary solution to data sprawl – a technique that enables data to be efficiently converted and used for business purposes.
Logical data fabric: knitting a virtual view of data
For decades, data has been captured and stored in central repositories, mainly so organisations can achieve a unified view of information across applications and databases.
Rather than consolidating data into a central physical repository, a new data management paradigm called logical data fabric has emerged. A logical data fabric knits a virtual view of data across applications by leaving it in its original sources while enabling a unified view of all enterprise data.
Data virtualisation forms its core technology, and many of its capabilities are automated through artificial intelligence (AI) and machine learning (ML).
Logical data fabric has increased in importance as organisations increasingly store data in multiple cloud-based platforms, adding to existing on-premises data-silo problems.
Stop collecting and start connecting: why logical data fabric is a better solution
Logical data fabric enables organisations to stop ‘collecting' their data and placing it into a central repository but rather to start remotely ‘connecting' to their data at its various sources through data virtualisation. Regardless of the data's location, logical data fabric brings the data together in a virtual fashion for the benefits of data discovery, management, and governance.
In this way, logical data fabric greatly improves the efficacy of business data users. Without needing to move data from its source into a temporary repository, IT teams no longer need to program ETL scripts to convert data before loading it into target systems. Instead, data virtualisation can perform transformations on the fly, which saves on storage costs.
Also, data virtualisation's low- to no-code approach significantly reduces the resources required to develop unified data views and intelligence.
More importantly, data virtualisation serves as a virtual catalogue for all data assets within the enterprise, including the lineage of all data sets from their points of origin to their final published state, the format of specific data, and the relationship between different data assets. Through this catalogue, users do not need to access different systems to perform actions such as data discovery or documenting business definitions for the purposes of data governance. The process is streamlined.
Logical data fabric also possesses powerful data preparation capabilities, normalising data formats and simplifying them for business consumption. Business users can then easily access the data within their favourite analytical, operational, web, or mobile applications.
The future of logical data fabric? embracing AI/ML
Logical data fabric already embraces AI and ML to automate routine tasks, which is set to intensify. AI and ML continuously analyse changing data patterns and automatically integrate new data for unified views, delivering them in the most appropriate formats to business users.
The incorporation of AI and ML technologies enable organisations to better understand the data consumption behaviour of certain users.
Logical data fabric for today and tomorrow
Logical data fabric is no longer just a concept. Due largely to the benefits outlined above, the adoption of logical data fabric has been gaining speed. By providing business-friendly views of enterprise data in its entirety and automating key processes using AI and ML, logical data fabric will be one of the hottest data management trends in coming years.