Bigdata and HadoopData Warehousing: A Critical Path to Customer Success in the Cloud

Data Warehousing: A Critical Path to Customer Success in the Cloud

The COVID-19 pandemic accelerated every aspect of digital transformation, and the move to the cloud is no exception. One study found that 81% of organizations accelerated their shift to the cloud because of the pandemic, and the number of enterprises planning to move 75% or more of their apps and workloads to the cloud jumped by approximately 200%. 

It’s generally agreed that cloud data management and analytics drives numerous business benefits, including reducing costs and strengthening business continuity. Enterprises that move to the cloud expect to benefit from faster decision making, higher quality data, and broader, more secure access to insights for employees working remotely. 

But like so many business buzzwords, the cloud is no magic bullet. It’s estimated that companies waste around 35% or more of their cloud spend, with the result that optimizing existing cloud solutions is high on the list of priorities for 2022 and should direct businesses towards data warehouses. 

There are a number of obstacles in the way of cloud data management nirvana, but data warehousing can help you overcome them. 

Ensure data integration 

There’s often an assumption that once data reaches the cloud, it’s instantly fully accessible to every application. Unfortunately, that’s often not the case. First of all, it’s worth remembering that as much as 96% of enterprise data still resides on-premises. Simply adding cloud data capacity doesn’t help you over this hurdle. Your data ends up fragmented rather than integrated, damaging data integrity and affecting your path to insights. 

Additionally, there’s a risk that when you shift to the cloud, you simply transfer the existing individual islands of data from databases, applications, and spreadsheets into an equally siloed cloud version. Legacy data formats can persist and isolate data, even when that data is stored in the cloud. And while cloud data storage should break down silos, new micro-silos can arise unless it’s managed properly. 

This is where data warehouses like Redshift vs Snowflake can help. A data warehouse helps break down the silos that can travel with data, to unify all your datasets, no matter where they’re coming from, and transform legacy datasets into a single, accessible data pool.

Access a wider range of analytics tools

Data fragmentation tends to be a self-repeating crime. When data is fragmented, it has a domino effect across the organization, holding companies back from connecting data with powerful analytics tools and preventing them from achieving the promised agility and speed of the cloud. 

Lack of integration doesn’t just mean that some datasets are cut off from others, but also that you aren’t always able to deploy the tool of your choice, so you can’t always reach the insights you are seeking even if all the data is at your fingertips. That in turn can restrict your understanding of crucial issues, right when you most need clarity. 

Cloud data warehouses help you out by supporting a vast range of third party business intelligence (BI) tools, including more powerful next-gen analytics using advanced machine learning (ML) and deep learning (DL). With a cloud data warehouse, you can run ad hoc, high performance, and semi-structured data analytics at scale, plus numerous data visualizations and interactive dashboards, opening up access to all insights. 

Get more out of the same data

Simply moving data to the cloud doesn’t mean you’ll change the way you use it. If your data remains the gated preserve of data scientists and skilled analysts, you’re not going to see the value that you expect. Your non-techie employees are going to remain divorced from data insights, but your data scientists don’t know which challenges could be addressed with the data that’s under their control.  

But cloud data warehouses enable flexible data working, which is why you moved to the cloud in the first place. They can integrate with intuitive, self-serve data portals that allow every department to run their own queries on the same data, without diluting the integrity of the original data. 

For example, the same monthly sales data can be used by the finance department to forecast profit and loss, marketing to track RoI on campaigns, sales teams to identify the most valuable accounts to target for retention, and executives to decide whether to accept an M&A offer. 

Speed up time to insights

Data is valuable, but much of that value is intrinsic. Until you convert it into meaningful insights, it’s not going to help you boost revenue, cut costs, or spot risks and opportunities. At a time when everyone is racing to be the first to detect a new niche in the market or anticipate changing customer demands, cutting time to insights can be crucial. 

By decoupling data sourcing from compute architecture, data warehousing can have a significant impact in just this area, enabling BI tools to respond significantly faster. The more stable and scalable system means that you can permit all your stakeholders to run queries at the same time, reducing delays while teams wait their turn to explore data. 

Running analytics in the cloud data warehouse also removes the need to physically migrate data each time. This saves time to insights while increasing security, since the fewer places your data resides, the better. Additionally, cloud-based data warehousing makes it faster and easier to swap out datasets, ensuring that everyone is working off the most up to date data.

Data warehouses are a key component for cloud data success

There’s no denying that cloud data management is  the way forward, and the trend can’t (and shouldn’t) be halted. But it’s increasingly clear that cloud data warehouses need to be part of any cloud data strategy. By helping reduce time to insights, unify data, open up access to insights, and free you to connect any BI tools, data warehouses can have a significant impact on cloud data success. 

Also Read: The Role Of Cloud Computing In Digital Transformation


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