Where is your organization on the cloud data journey? Most businesses are steadily moving toward the cloud, with most businesses planning to migrate to the cloud or expand their cloud presence in the next few years. There are four stages of the end-to-end journey: cloud discovery, cloud data migration, cloud data maturity, and cloud data leader. It’s possible that for larger enterprises with diverse data needs, some departments and functions could be at different stages. Each stage has business drivers pushing IT and data professionals forward to create, refine, and exploit cloud data management to enable better business decisions. The following descriptions are a general guide to each stage and considerations for moving forward along the path to a scalable cloud data infrastructure.
Stage 1: Cloud discovery
If you find yourself at this stage, you most likely have data stored inside on-premises data warehouses and are evaluating a move to the cloud. If you made the move, you’d be in good company – in a recent survey, 90 percent of data professionals reported that they have already placed some data into cloud data warehouses.
At this stage, you need to determine how to speed up analytics and reporting to help your company compete with data insights. You’ll want to ensure that the data you have is accurate and up to date when you create reports and pull metrics. The best place to build your data analytics platform is in the cloud – with cloud-native solutions. The scale and performance power of the cloud is the right choice for enterprises that want to reduce costs and save time in their data management strategy.
When it comes to your data architecture, you should identify gaps, in both products and skills, to determine how to expand your cloud presence. Do you need a hybrid cloud architecture to start with? What is the best cloud data warehouse for your business? Ask questions of every vendor or find a consulting partner to help you strategize.
Stage 2: Cloud data migration
Enterprises at this stage are invested in the cloud, having already chosen a cloud service provider and a cloud data warehouse to manage their data. Now, you will need to make decisions on what data to load into the cloud and how to do so efficiently.
As we mentioned in the first stage, companies move to the cloud to take advantage of speed. In this stage, two big business drivers – centralizing data and creating a scalable data infrastructure – inform how businesses design their cloud data management platforms. Data silos inevitably spring up in large organizations. But in order to get a clear view of business-critical insights, having data in a central place is imperative. A cloud data warehouse and cloud-native ETL solution can serve as a single source of truth.
Loading data into the cloud should be a measured project – start with a small use case where the KPIs and metrics are well-known and the data sources are accessible. Showing the value and speed of cloud data analytics in a relatively short time can help you get buy-in to set up a scalable infrastructure in the cloud.
Stage 3: Cloud data maturity
Cloud technology is now second nature to data professionals at this stage of the cloud data journey. Right now, you have established solid cloud data analytics use cases, improved analytics and reporting for a part of the business, and are likely even running data orchestration and transformation jobs as a regular part of your day-to-day.
At this point in the data journey, you are ready to make the cloud work harder for you. This is the time to refine and hone your cloud data management strategy. When it comes time to prepare and load data, you want to ensure that you do not use up resources on tasks that can be automated. Three out of four organizations (74%) say that it’s either “extremely” or “very” important to reduce the amount of time and resources spent on data preparation and pipeline development. Explore where you can automate parts of the ETL process to truly take advantage of the speed and power of the cloud.
In life, and in cloud data management, increased maturity means increased responsibility. As you move more data into the cloud, you will also need a tech stack that can complement your cloud strategy and your business objectives. It’s important to pick software that can help you eliminate time-consuming tasks like hand-coding and also incorporate safeguards like disaster recovery or versioning.
Stage 4: Cloud data leader
Companies at this stage of their data journey are well-positioned to make data-driven decisions. Likely they are already doing so, using data science and advanced analytics to inform decisions across the business. They may even be using transformed data to create machine learning models and artificial intelligence. With data as their biggest asset to drive business and product development, they will be looking to further their investments to innovate faster with insights.
In order to scale data efforts further, enterprises can’t wait on IT to provide insight. At this stage, they need to provide other parts of the business with access to data sources for analytics – in a thoughtful, considered manner. Providing analytics-ready data to everyone in the business helps promote data-informed decision making. Now more people and teams across the business can take the guesswork out of their processes and spread data literacy inside the organization.
To achieve this data democratization, organizations need to ensure that proper security and governance for data sharing is in place. IT professionals and business users should work together to understand, implement, and collaborate on all aspects of data governance and cloud solutions to ensure data self-service efforts are responsible and compliant.
Understand your stage in the cloud data journey
As your business expands your cloud capabilities, there will be different considerations, business drivers, and barriers for every part of the journey. Understanding your business objectives, managing expectations of projects, and ensuring your data architecture can scale, will help you efficiently move through each stage.
Credit: Source link