How to Overcome 8 Cloud Data Strategy Challenges
An Enterprise Architect’s Guide on How to Overcome 8 Cloud Data Strategy Challenges
Enterprise architects face a range of challenges related to cloud data strategy. They’re looking for speed and safety, efficacy and economy, and appropriate tooling and technology. To get beyond the hurdles that are common across many organizations, take the following steps.
1. Move beyond data silos.
When organizations rely on disparate systems, disparate teams and varied infrastructure environments, disconnected—siloed—data often results. When data silos exist it means that some teams and/or groups of individuals in the company are not able to find and access the proper data which leads to ambiguity, potentially wrong decisions, and incorrect conclusions which undermines the performance across business units. The State of Data Science and Analytics report from IDC reports that workers spend 90% of their working week (around 36 hours) on data-related activities such as searching and preparation.
To combat data silos the first thing that should be considered is to define your Data Lake or Data Warehouse as the single source of the truth and ensure that your data is certified and timely and representative of all of the company’s domains. This allows all of the applications, users and systems across the organization to extract the same data at the same time, it preserves the authenticity of the data and delivers insights that are drawn from certified combined datasets fit for consumption.
2. Gain Operational Efficiency
If the statistic from IDC is correct that 90% of a worker’s time is spent searching for and preparing data, then it is clear that having a single source of truth for all the company data which is easy to find and access, clean, timely (with time variants), and secure, will equate to a 36 hour a week of productivity gain to the workers to do other value-add activities such as AI/ML, analytics, improving efficiencies elsewhere such as Analytics, AI/ML models.
3. Improve data portability
Organizations spend big money for every byte of data they move out of a public cloud. Cloud service providers charge egress fees to transfer your data out of their cloud storage, which means that moving data is cumbersome and expensive. In response, many enterprises are increasingly moving towards a multi-cloud strategy, requiring replication of data across distinct hybrid cloud environments, often proving to be a very cumbersome task. Enterprises may run into complexities that drive up operational costs and can lead to access and management issues in hybrid cloud environments.
To improve data portability, use a solution that doesn’t charge egress fees for taking data out. By avoiding these fees, you can more easily and more affordably repatriate your data or migrate data to a different cloud provider.
4. Avoid vendor lock-in
As your business needs evolve, you’ll look for the flexibility to use the best-in-breed tools from your cloud provider of choice. But suppose you’re locked into a single cloud provider. In that case, you may be held back from innovating—making it difficult to sell your solution portfolio to new markets or preventing you from moving into new areas, like the retail sector or the consumer products and goods space.
What is Vendor Lock-in? Cloud vendor lock-in occurs when transitioning data, products, or services to another vendor’s platform is difficult and costly, making customers more dependent on (locked-in to) a single cloud’s data storage solution. Some public cloud vendors create lock-in for customers by charging egress fees to move data out of their cloud. Other cloud companies lock customers in by limiting their products’ integration potential with other vendors’ products.
To get beyond this kind of restriction, select a data services solution that abstracts your data from cloud provider lock-in and also provides a high-speed, low latency connection to your data.
5. Accelerate your data ingestion
Reducing your time to insight is an essential capability in today’s digital world. Waiting hours (or days) to ingest data sources into a cloud-accessible repository isn’t a viable option when you need to run a business-critical analysis. This is particularly troublesome when using tools or services from cloud providers that may not have access to your data, requiring data ingestion into those cloud providers before any analysis can be completed.
By using a solution that allows you to ingest data centrally, you allow simultaneous access by all cloud providers. The result? You don’t need to replicate or ingest data into separate clouds at the time of analysis, radically improving the speed of analysis and decision making.
6. Lower data storage costs
Accessing tools and services across hyperscale providers (like AWS, Azure, and Google) can maximize the innovation potential of your teams. But it’s reliant on making your company’s data accessible by these cloud services simultaneously and in the same location.
If you currently store data in a single public cloud provider, you’ll have two options.
- Duplicate data into another public cloud to access that same dataset with the discrete services in the secondary public cloud, or
- Copy it on demand for ad hoc analysis.
Both can create complexity and costs can skyrocket due to unnecessary duplication of data across cloud providers.
Instead, use a single copy of data that’s accessible from all clouds at the same time. Because this eliminates the need to duplicate data, a single copy of data can cut your overall storage costs by as much as 75%, depending on the number of cloud providers you leverage.
7. Avoid the risks of data impermanence
A key advantage of the public cloud is scalability meaning you can quickly scale out (adding more nodes or adding more compute or storage) or scale down as needed. But cloud resources can be ephemeral, meaning that they come with some risk. When cloud resources are decommissioned or deleted, data may be inadvertently deleted. This would certainly be problematic if you’ve created a complex and time-consuming AI/ML model or are storing metadata in the cloud, for example.
To prevent the associated risk, select a solution that leverages a centralized and persistent data source. If a cloud resource or service is decommissioned, your data remains safe.
8. Rely on real near-time reporting
Companies and decision-makers demand access to real-time or near-real-time reporting on disparate data sources. Data silos created by storing unique data sets in various public cloud providers, created by applications and services in those clouds, can hamper this.
For more reliable reporting functionality, look for a solution that centralizes data from distributed data streams, IoT sources, third-party data sources, or company data sources. Combining these sources into a cloud-agnostic data service breaks down data silos, providing a single data repository that is ready for on-demand access and analysis.
For a customized evaluation of how to move beyond your organization’s particular cloud data strategy challenges, contact us!