devopsy.kinsta.cloud

Author: Arawan Gajajiva

  • How Cloud Data Platforms Help Enterprises Drive Business Value

    How Cloud Data Platforms Help Enterprises Drive Business Value

    Cloud data platforms make businesses more scalable and flexible and more cost-efficient

    The need for actionable data is nothing new. Enterprises spend time, money and resources working to get their data into a format that is usable for analytics. But most are still struggling to do just that. More than 90% of enterprises say that making data available for insights is a key challenge in their analytics programs. The journey to modernizing your data infrastructure is challenging, especially if performing data transformation quickly is holding back your business.

    To compete effectively with data, you need “analytics-ready” data. This requires scalable data transformation—the ability to join together siloed data, denormalize, enrich and apply business logic. This process is imperative for getting to the insights that will help you solve business challenges at pace. So how much can data transformation benefit your business? It can rapidly improve your data preparation process, consolidate your data sources, save money, make accurate forecasts and predictions and most importantly, provide you with the insights you need for business growth.

    Cloud-native Solutions and the Cloud Data Warehouse

    A modern data infrastructure works best with cloud technology. Legacy data warehouses hold businesses back from scaling alongside data volumes and bringing data together into a single view. Cloud data warehouses help companies move quickly, and cloud-native solutions use the economics and power of the cloud to handle big and varied data volumes. This is especially helpful with a cloud ELT (extract, load, transform) solution. Traditionally ETL (extract, transform, load) is used to perform data transformations. But the modern ELT approach allows you to move data directly into your cloud data warehouse and then perform the necessary transformation. When enterprises employ a modern data environment and cloud-native tools, it is easier to proactively use data to glean insights—and save on provisioning costs.

    Take, for instance, a large cloud security organization that needed a centralized data repository that was modeled to handle diverse data sources and increasing data storage and reporting needs. This company moved off of its legacy system onto a cloud data platform with a cloud data transformation tool, allowing them to reduce application spends by 84%. Now with a single view of data, they can easily run unified reports to analyze data quickly, opening up greater possibilities to extract insights.

    Preparing and Transforming Data Quickly for Faster Time to Insights

    The act of wrangling data is time-consuming. In a recent survey by IDG, nearly half of data professionals reported that it takes more than a week to prepare data for analytics. ETL, ELT and data preparation workloads are bogging down precious data engineering resources and wasting valuable time that could be better spent innovating with data. Data preparation is a challenge for organizations because the work is code-heavy and manual.  Cloud-native ELT solutions can reduce the burden of data wrangling and provide a much faster, low-code process to prep data.

    Data transformation tools that take advantage of the power of the cloud, have intuitive and automated data preparation processes that eliminate the need for coding.

    This enables businesses to transform data for advanced use cases such as machine learning. For example, a global logistics company that made 100 million deliveries to 37 million unique customers across 72 countries needs to use massive amounts of data, from a variety of sources, to fuel its daily operations. Using data transformation inside their cloud data warehouse helped them to join together disparate data and improve business processes. The company deployed 200 machine learning models in a single year, used to make 500,000 predictions every day. This significantly increased the efficiency of its delivery operations and helped the customer service team reduce inbound call center inquiries by 40%.

    High-quality, Accurate, Timely Data Makes the Difference

    New data sources are proving beneficial—and challenging—for businesses to integrate into their organization. As the pace of data increases, the need for recent and accurate data rises along with it. An analytics platform built in the cloud can provide a much-needed pipeline of high-quality data that can be used in a timely manner, with insights provided quickly.

    As another example, a software company that wants to improve its data-driven culture by relying on fast data integrations using a cloud analytics platform to ingest information. Instead of analyzing data that was days old, the software company used a cloud data transformation tool to load data in real-time from all external sources in the business. By taking advantage of reusable components built in the cloud, the team reduced their data ingestion process from as many as 10 workflows down to just one. This allowed them to reduce their data warehouse and ETL administration, freeing up developer resources to innovate within the business.

    Scaling Resources and Saving Costs With a Cloud Data Platform

    The results of cloud data transformation speak for themselves, as enterprises from different industries see accelerated time to value, scalability and cost savings that validate their investment in modern solutions.

    Data transformation, performed in the cloud, can help you rapidly improve data management and existing processes. Analytics-ready data can help you solve critical business problems and ultimately generate revenue. As external factors contribute to pressure on budgets, resources and road maps, transforming data with an ELT solution will help reduce development time and accelerate innovation with new insights. Cloud ELT and data platforms make businesses more scalable and flexible, cost-efficient and better-prepared to leverage data in advanced use cases involving IoT data, machine learning and artificial intelligence that can predict what comes next and what to do about it.

  • Ten Questions Data Professionals Should Be Asking When Moving to the Cloud

    Ten Questions Data Professionals Should Be Asking When Moving to the Cloud

    You have finally decided to move your data into the cloud. Whether it was the ability to leverage the power of cloud compute, the promise of cost-savings or the scalability and flexibility that a cloud data warehouse can bring, it’s important to have a strategy in place. In order to set yourself up for success, here is a list of questions that will help you organize your data and ensure your data migration efforts provide business value.

    What Is the Outcome I Need to Achieve?

    When you are creating a data migration strategy, you need to have an end goal in mind in order to make the best decisions on technology and approach. Simply getting your data into the cloud won’t be enough to take action on that data. 

    How Many Data Sources Do We Currently Have? 

    Once you have your outcome in mind, take a comprehensive inventory of all your data sources before you begin the migration process. It reduces your risk of discovering halfway through the process that you have no access to a data source–or that you forgot to include it in your migration plan. 

    Who Is the Administrator of Each Data Source? 

    It is likely that you may not have access to every single data source that you are looking to migrate. Make sure to find out who can provide you with access to that data, and fully understand your requests. This person will be able to explain the necessary requirements for that data source and will be your contact to help integrate that source into your migration efforts. 

    What Data Is Most Important to My Key Stakeholders, and What Do They Consider Important in Getting that Data? 

    This question might also be: What are the KPIs that drive the business from the data sources? You’ll need to have a good grasp on the metrics that are used for business-critical KPIs so that you have the right data in the cloud to ultimately be transformed for analysis. For example, a retailer might value click-through-rate on emails as a key indicator of attribution for sales data. In order for them to get that information, they would need to provide data from their email marketing system and their eCommerce platform. Keep stakeholders involved in the success and failure criteria of your project so they can help set you up for success. Identify the system of record for the metrics that each KPI is calculated from. Quite often, more than one system in your organization can have a representation for that key metric, but with different values. Align with the related stakeholders as to which is the system of record in those cases. 

    How Can I Show Business Impact Quickly?

    A proof of concept (PoC) in the cloud is always associated with the desire to show results quickly, as the speed and performance that the cloud brings means that you should be able to move faster, right? Asking yourself what you can do to help the business recognize the value of your project can help you assuage any productivity concerns. To create a quick PoC, start by first identifying a small number of typical use cases to test out. Picking a high-value KPI that is calculated from metrics from well known source systems could be a good candidate for that first iteration.

    What Are My Security Concerns?

    Considering that the cloud can house petabytes of data that enterprise companies collect every day, it is safe to assume that some of that information will be sensitive and confidential. Ask yourself if the data you migrate contains personally identifiable information (PII) and if that information is necessary for the project you are working on. In most cases, PII is not needed for analytics work and therefore, you wouldn’t need to migrate that. You might need to know the brand of a credit card to make a data-driven business decision but perhaps not the actual credit card numbers. 

    Who Has Access to My Data Now that It Is in the Cloud?

    If you are migrating sensitive data into the cloud, these are the security and compliance requirements that come into play. How much data security management you entrust to a cloud provider entirely depends on the needs and requirements of your business. There are several different security models, some of which give you more control, some that hand-off that control to cloud providers—it is important to choose the model that works best for your business. Ensure the vendors you use are SOC II compliant and have all the necessary certifications in place to keep your data safe. Work with your organization’s security group on this. They may have defined an overarching Enterprise Cloud Strategy which can help to provide guidance around the acceptable use of cloud resources for your analytics workloads.

    What Do I Do with My Data Once It’s in My Cloud Data Warehouse? 

    Once your data is in the cloud, what do you plan to do with it? Create a strategy to structure your data in the correct way for your business outcome. Different cloud-built technologies can help structure your data for analytics, prepare your data for machine learning or artificial intelligence projects, or perform data cleansing so you have the most accurate data available. It is important to remember that different cloud data warehouses, data integration tools and data visualization products need to complement one another. Make sure you build your stack for your project to ensure success. 

    How Do I Save Costs in the Cloud? 

    In addition to faster time to value, one of the main reasons companies migrate data to the cloud is to save costs. But, how can you ensure you are saving costs while migrating data? You can take advantage of free trials offered by many purpose-built cloud data tools that allow you to allocate, compute and storage as needed. The beauty of a cloud data warehouse is that you don’t need to provision for future workloads. As your needs grow, you can scale up easily and manage costs effectively. Try to avoid using multiple products that do similar things. This will reduce cost, increase adoption of the products and keep your architecture simple.

    Is My Team Equipped to Handle Cloud Migration?

    Cloud data migration is different than on-prem migration and in such, you and your team will be using technology that you haven’t used in the past. To set your team up for success, inquire about their comfortability using the tools you’ve selected and ensure they have the level of access needed to do their jobs efficiently. If there are gaps in knowledge, you can look to provide the training to help them get up to speed and make onboarding as painless as possible.

    If you answer these ten questions, you will be on the right track to begin your cloud data migration project. Keep your business goals in mind and start small—the flexibility of the cloud allows you to scale your project later if needed.

    — Arawan Gajajiva