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In 2021, you don\u2019t need someone to tell you that digital transformation has already become a mainstay for modern businesses. While companies were already leveraging different types of digital technology, the COVID-19 pandemic has further accelerated the process.<\/p>\n
One of the most significant outcomes of the digital transformation of businesses is the generation of a vast assortment of data. When judiciously used, this data can be instrumental in optimizing business processes and skyrocketing revenue.<\/p>\n
Most organizations lack the right infrastructure to store, process, and analyze data. Even if they invest in the right data analytics tools, the insights often don\u2019t make their way to other teams, including business development and planning.<\/p>\n
It creates a significant gap between strategic planning and business intelligence. While you might have a plethora of data in your hands, it\u2019ll be of no use unless you can turn it into action-driven insights and facilitate better decision-making.<\/p>\n
That\u2019s where cloud data warehouses (CDWs) step into the picture. They help break the organizational silos that exist between business science and data intelligence.<\/p>\n
In this blog, we\u2019ll take a closer look at the concept of cloud data warehousing, and understand how it empowers business intelligence. Also, we\u2019ll discuss tips to harness the potential of CDWs for data-driven business planning and decision-making. Let\u2019s get started.<\/p>\n
Simply put, a cloud data warehouse is a centralized cloud-based repository that stores, analyzes, and transforms an organization\u2019s data. It\u2019s equipped to collect incoming data streams from various systems. Also, it structures and formats the collected data to make it useful for different departments.<\/p>\n
CDWs eliminate the need for organizations to procure and maintain dedicated hardware for on-premise data warehouses. Also, they help overcome the drawbacks of legacy data warehouses.<\/p>\n
CDWs are known for their improved performance, scalability, elasticity, and reliability. Also, team members can access the data using any device from any location, as long as they have reliable internet access. That makes cloud data warehouses particularly useful for today\u2019s hybrid working environments.<\/p>\n
Considering these benefits, it isn\u2019t surprising that the global cloud data warehouse market is expected to be worth $39.1 billion by 2026<\/a>.<\/p>\n But one of the lesser-known benefits of CDWs is the integration of data intelligence into business science and strategy.<\/p>\n Business science involves the use of data to drive business decisions and processes. On the other hand, data intelligence refers to the application of artificial intelligence and predictive analytics to convert the collected data into actionable insights.<\/p>\n It\u2019s easy to see how useful data intelligence can be for business science. It can help organizations improve operations using result-oriented data, instead of relying on guesswork and intuition.<\/p>\n But the complexity of storing and interpreting data from various sources often creates a disconnect between these domains.<\/p>\n While data analytics tools might generate plenty of insights, senior-level decision-makers often don\u2019t have the resources to interpret them. That\u2019s because most business intelligence tools require the data to be presented in a proper structure and format.<\/p>\n The role of cloud data warehouses is to eliminate this friction. They facilitate seamless interaction between data analytics and business intelligence tools. They store data and insights in a proper format that can be accessed and comprehended by different departments.<\/p>\n With nearly 53% of enterprises<\/a> migrating their data to the cloud, it\u2019s futile to wonder whether CDWs collate business science and data intelligence. Instead, you need to identify the best strategies to achieve the integration of business science and data analytics for a robust business intelligence infrastructure.<\/p>\n If you want to maximize the utilization of data intelligence for driving business decisions, the first step is to choose the right cloud data warehouse. From Amazon Redshift and Google BigQuery to Snowflake and Azure Synapse – the CDW market is flooded with options.<\/p>\n If you’re unsure about which service provider to select, going through a detailed Snowflake vs Redshift<\/a> comparison could help. You can learn about the performance of each CDW and find out whether it offers decoupled storage and compute. Also, make sure you check the pricing plans and storage formats before making the final selection.<\/p>\n It\u2019s just as important to outline a seamless data migration strategy. Copying all your data to the cloud at once could prove to be counterproductive. Instead, create a gradual migration plan that focuses on individual departments.<\/p>\n Lastly, you need to invest in the right set of business intelligence with in-built analytics solutions. Make sure the tools easily integrate with your cloud data warehouse.<\/p>\n Business science and data intelligence share the common goal of driving better business decisions. Combining the power of cloud computing with data warehouses will bridge the gap between these domains and help you make the most of business intelligence tools.<\/p>\n","protected":false},"excerpt":{"rendered":" In 2021, you don\u2019t need someone to tell you that digital transformation has already become a mainstay for modern businesses. While companies were already leveraging different types of digital technology, the COVID-19 pandemic has further accelerated the process. One of the most significant outcomes of the digital transformation of businesses is the generation of a… Can You Bridge Business Science and Data Intelligence in Cloud Data Warehouses?<\/span><\/a><\/p>\n","protected":false},"author":17,"featured_media":9996,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-9995","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general"],"_links":{"self":[{"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/posts\/9995","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/comments?post=9995"}],"version-history":[{"count":2,"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/posts\/9995\/revisions"}],"predecessor-version":[{"id":9998,"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/posts\/9995\/revisions\/9998"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/media\/9996"}],"wp:attachment":[{"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/media?parent=9995"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/categories?post=9995"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/flevy.com\/blog\/wp-json\/wp\/v2\/tags?post=9995"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Merging Business Science with Data Intelligence: The Role of Cloud Data Warehousing<\/h2>\n
Enhancing Business Intelligence with Cloud Data Warehouses<\/h2>\n
Improving Business Decision-Making with Cloud Data Warehousing<\/h2>\n