Why should companies adopt Modern Enterprise Data Architecture?

Published on
12/12/2019 01:51 PM

In the current technological landscape, every enterprise now perceives data as a valuable asset in the digital economy. Nevertheless, a whitepaper from SnapLogic insists that companies must implement Modern Enterprise Data Architecture in order to fully realise the value of their data assets.

Implementing Modern Enterprise Data Architecture

As the report observes, enterprises need to use data assets strategically, operationally, consistently, and accurately. In effect, organisations must consider a data-as-a-service (DaaS) offering as part of their own data strategy.

As a result, this ultimately ensures high levels of SLA, governance, accuracy, and availability. As enterprises make the journey to the cloud, it is thus integral that they adopt Modern Enterprise Data Architecture (MEDA).

The value of data lakes

Unlike a legacy data warehouse, a data lake entails a collection of all data types: structured, semi-structured, and unstructured. In fact, this means that data structure does not require a definition when captured, only when read.

Data lakes are also highly scalable, which means that enterprises can support larger volumes of data at a cheaper price than a legacy data warehouse. Data can also be stored from relational sources and from non-relational sources without ETL, meaning that data is more easily available for analysis.

Key drivers for building MEDA

As MEDA becomes more widespread, it is crucial that companies have the ability to measure the success of their implementation. According to the report, there are seven key business drivers to consider when building MEDA.

First of all, MEDA should support the democratisation of data, which requires data sharing, quality, security, and governance. This architecture must also enable a “hyper-connected” enterprise within and beyond an organisation, while supporting the transition to self-service and the Citizen X (integrator, data scientist, etc.).

MEDA should also allow enterprises to move from historical reporting to predictive and prescriptive analytics and enable a greater responsiveness to the line of business users (LOB). Finally, this architecture must provide future-proofing for new data and downstream applications, while achieving the "elusive enterprise digital transformation."

Check out our latest podcast, in which technology influencer Dr Sarah-Jayne Gratton discusses how to remove the confusion surrounding AI

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