Why Data Management On Cloud Is The Holy Grail For Enterprises Today
Over half the Fortune 500 companies, listed from the year 2000, no longer exist. All industries around the world are experiencing digital disruption, and a cloud data management strategy is emerging as a go-to option for businesses worldwide.
A Gartner report found that while 87 percent of senior business leaders say digitalization is a company priority, only 40 percent of organizations have brought their digital initiatives to scale.
Among the reasons cited, a lack of flexible, scalable and robust data management capability is frequently explained as one of the top reasons behind why digital transformation initiatives are failing to drive the desired improvements in processes and workflows.
In this blog, we will see how cloud data management methods offer a crucial competitive advantage to enterprises today, impact cutting across industries and economies, value chains and entire value systems.
“George Moore famously quipped that without Big Data, organizations are like a stranded deer, blind and deaf in the middle of the 21st-century market freeway. Let’s delve into the analogy with the automobile: you need to design and fine-tune your engine so that it delivers superior mileage, more power output, efficient thermal design, low fuel consumption as well as feed it with high-quality fuel so that it zooms ahead on the freeway.”
Likewise, when setting up your enterprise data warehouse, you need to ensure that your entire data processing engine is state of the art to remain ahead of the curve.
For this, you need a data management platform that streamlines rules for data storage and access. A sound data management strategy is a prized asset that will help enterprises realize the value from their analytics investments and will be the turbocharge to power the lean, modern, proactive and productive 21st-century enterprise, cutting costs, increasing revenues and helping pre-empt market opportunities.
In this blog, we will delve into the components of what a modern, successful data management system entails.
So, why does the need arise for a cloud data management system?
Today enterprises are deploying many use cases such as text analytics, streaming analytics, real-time social media monitoring, processing technical and functional use cases. Moreover, real-time data ingestion is increasingly being used to support critical business workloads.
Traditional data warehouses, featuring long IT cycles for design and up-gradation, are not built to support these demands since they cannot scale quickly to meet unpredictable spikes in data volume, accept an unprecedented variety of structured and unstructured data from an ever spawning number of source systems, support big data and massively parallel processing systems, and support real-time data analytics use cases on this massive volume ( streaming data).
While the use cases will differ from industry to industry and from company to company, the verdict is unanimous — a traditional data management strategy is not built to support the modern enterprise's needs with their data. Organizations need an open relationship with their data today.
Organizations are automating their data pipelines to optimize enterprise data management, including Master Data Management (MDM). The global enterprise data management market size is expected to grow from USD 77.9 billion in 2020 to USD 122.9 billion by 2025, at a CAGR of 9.5% during the forecast period 2020.
Inconsistent and inadequate data input can affect the quality of decisions. Hence, companies need to break down data silos and ensure data formatting and standardization to remove inconsistencies in the data.
“A modern cloud data management platform needs to be agile enough to use appropriate data transfer mechanisms and deliver lower latency because the users will want their analytics fast. One of Marissa Mayer, Yahoo CEO’s famous epigrams for her IT department was “With data collection, ‘the sooner the better is always the best answer.”
To summarize enterprises need an adaptable data warehouse design that can support the following modern use cases
1) Storing huge volumes of enterprise data- cost-effectively storing data in dumps so that they can be visited later for analysis. This requires an increased scale of ingest and scale of pre-processing
2) Real-time analytics — The solution must support and drive fast data pipelines, ingesting real-time data is becoming crucial and reduce latency for analytics applications
3) Keep unstructured and structured data together — The data management platform must work by blending both structured and unstructured data together.
4) Automated Data Pipeline — to minimize human errors and ensure minimal data duplication while supporting agile use cases
5) Data governance — A governed data governance, access and sharing framework so that IT can ensure that data is being used appropriately.
6) Support advanced analytics use cases — Include ML/ AI capabilities with strong data governance mechanisms for monitoring the performance of models.
7) Support ad hoc requirements — Since different users will require access to the data to solve specific use cases for themselves
The Chief Data Officer needs to ensure greater alignment between the IT folks who control the data and the business team.
Continue: https://www.polestarllp.com/cloud-data-management-data-warehouse
