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Businesses have been using analytics for years to make confident business decisions, mitigate risks and increase operational efficiencies. Across industries and departments, teams rely on data to drive decision-making and optimize day-to-day operations. But when data is siloed and inaccessible to the people in your organization who need it, they can’t analyze all the data that’s been collected to identify valuable business opportunities. This is one of a handful of areas where cloud analytics surpasses on-premises analytical technologies.
What is Cloud Analytics?
Cloud analytics refers to the process of reviewing, compiling, and preparing data to be learned from and shared—just like on-premises analytics, except the process is conducted using cloud services that house all your data in one place. Cloud analytics saves time and can be accessed anytime, anywhere, without the need to install any hardware.
“Data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to accelerate innovation,” said Gartner analyst Rita Sallem, during her presentation at virtual Gartner IT Symposium/Xpo™ 2020 . A 2020 Gartner report estimated that by 2022 public cloud services would be essential for 90% of data and analytics innovation. If you’re a business performing analytics outside of the cloud, you may have difficulties processing all that data.
What Are the Challenges of Traditional Analytics?
For data to be useful, it must be collected, organized, and analyzed. When data is siloed, it’s often difficult to obtain, is formatted in different ways, and is likely in different file types. In a retail store, for example, one department might categorize in a spreadsheet all pants for men as either “slacks” or “jeans.” A spreadsheet for pants in another department might categorize men’s pants as either “blue jeans” or “trousers.” While each of these spreadsheets may be stored in a file in each department, because “pants” is classified in various ways, the retailer doesn’t have an accurate picture of all the types of men’s pants it has in its inventory.
The retailer, like other businesses, daily collects and grows data, including IP addresses, website content, products and services reviewed or purchased, search queries, and a website visitor’s location. Internally, the IT team may be logging every move that’s made within the network, such as firmware and patch levels, software versions, and event logging from dozens or hundreds of systems. Keeping up with all this data is daunting, and while much of it may not currently be used, it will likely be useful in the future. That means the analytics system you’re using today will need to continue to grow. But there will be challenges doing that with on-premises analytics systems.
Lack of Scalability
The first problem companies are faced with when performing analytics on-premises is the lack of scalability with their traditional analytics software. Even if your data is already housed in the cloud — and it’s likely that at least some or all of it is stored there — many companies are still performing their analytics on-premises. This wasn’t a huge problem twenty years ago when most data was manually entered by humans at a keyboard. But these days, there is so much machine-generated code that almost every organization is a “Big Data” organization. Traditional on-premises data warehouse devices and Hadoop clusters tightly couple compute and storage, so when either the data or the amount of processing require increases, more expensive hardware needs to be purchased.
Lack of Centralized Data Lake
The second problem with on-premises analytics is that you may often find yourself waiting hours, days, or even weeks for reports due to the lack of a centralized place where you can pull data from, such as a data lake. A data lake refers to a central storage area — usually housed in the cloud — where different types of data can be stored. Because there are no filetype limitations in a data lake, companies can bring in unstructured data such media files, documents, IoT data, and others with few limitations as to what kind or how much data can be stored. When doing analytics outside of the cloud, departments may pull from various silos to collect and use their data. Silos are also places where data can be stored, but they’re not accessible to everyone at an organization. For example, a company’s IT, sales, marketing, and operations teams may all be gathering their own data into separate silos. Not only is this inefficient, as the chances for data duplication is high, but manually locating and then moving data in and out of silos across a network is time consuming. The more data you need to move, the more time it takes, delaying the analysis. Since data from different silos are not easily shared, collaboration across departments is discouraged merely by the structure of their data sets.
The third problem that arises when performing traditional analytics is the high cost of both the analytics software and the servers it runs on. These software programs can take days or weeks to get fully up and running and must continually be maintained and paid for regardless of whether analytics are run for just an hour a day or 12 hours a day. Running analytics on huge data sets requires huge computing power, and that could take one machine on premises dozens of hours. Over time, as your data grows, that means more servers are needed for storage and compute.
How Can Cloud Analytics Help?
Organizations can often achieve a lower total cost of ownership for cloud analytics compared to on-premises, given the “pay for what you use” model in the cloud. Cloud analytics allows you create a single organized place for all your data and allows different users/departments to consume that data as they wish. It also always gives you the latest version of data, highly available object storage, and the ability to scale resources up or down as needed.
The major cloud providers all have platforms to store data, but before deciding to work solely with your cloud provider, you may want to consider other cloud databases like Snowflake and Databricks, which work seamlessly with the public cloud providers. Snowflake is a fully managed Software-as-a-Service (SaaS) provider of a single platform for data warehousing, data lakes, and data engineering to handle the demanding needs of growing enterprises. Databricks is another managed cloud platform that provides a unified set of tools for data analysts, data scientists, and engineers to collaborate on data engineering, analytics, and machine learning workloads. Its product Delta Lake is a storage repository that combines the flexibility, scalability, and low cost of a data lake with the structure, data-integrity guarantees, and analytic capabilities of a data warehouse.
Improved Scalability and Built-in Tools
Cloud analytics providers are able to eliminate constraints associated with installing, updating, and maintaining software on-premises by providing businesses with various tools for data integration, analytics, data exploration and data cataloging, which are already built into the cloud ecosystem. You can choose exactly which services you need and have them up and running as soon as you need it. Cloud providers also lift the burden of having to troubleshoot these programs, as they are already being maintained within the cloud. Many cloud services also offer the ability to scale the size and performance of cloud servers, so your analytics capabilities become tailored to your specific computing needs, instead of limited by the operation and management of physical servers.
The power of the cloud is amplified by using a data lake, a repository that allows you to store all your structured and unstructured data at any scale. While you can build your own on-premises data lake, you commonly need to manage both the hardware infrastructure – spinning up servers, orchestrating batch ETL jobs, and dealing with outages and downtime – as well as the software side, which requires data engineers to integrate a wide range of tools used to ingest, organize, pre-process and query the data stored in the lake. Data lakes give you access to all the data the company has acquired. The more data your company can access, the better your ML models will perform to aid management with business decisions. ML is also made easier in the cloud because of the management services provided by cloud vendors, so your IT teams spend less time setting up and training AI models. And with cloud data lakes, companies can gain access to public databases such as Census data or industry data, which cloud analytics vendors make available for their clients to use.
Using cloud analytics also addresses the high cost of maintaining the on-premises infrastructure for your analytics. Vendors offer storage and analytics packages on a pay-per-use plan, meaning you never pay for more than you use. After assessing their needs, businesses can also opt to “rent” software and ML technology on a monthly or even hourly basis. This precision means that companies can have access to the most advanced analytics tools whenever they need them but aren’t tied down by ownership. Additionally, cloud services tend to decouple storage from processing. This means that you can just pay for storage of your data 24/7 but only pay for compute processing for the time it takes to run your analytics workloads.
As an added bonus, in addition to benefitting from the services and tools offered by the cloud, you’re also provided with increased security and safety for both your business and your users. Data that’s stored in the cloud is generally backed up in multiple locations in the local region and optionally around the globe, for a fee, eliminating the danger of a single point of failure. Sensitive information doesn’t have to be transferred through emails or on flash drives, as there is no local information to take advantage of. Everything is protected within the cloud. Cloud analytics services are also able to perform automated scans to identify threats and vulnerabilities, eliminating the need to constantly monitor your data for safety and compliance requirements. The tools you get with your cloud analytics service providers can help you to determine whether there’s anything that violates governance or compliance requirements like GDPR or PCI DSS.
Ready to Make the Switch?
Recognizing these benefits, many companies are already making the switch to cloud analytics. According to a 2021 report by Market Research Future, the global cloud analytics market is projected to grow at a rate of 21% CAGR (Compound Annual Growth Rate) from 2021 to 2027. This means that more and more businesses will rely on cloud vendors for the latest and most advanced analytics software available in the coming years.
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