{"id":11078,"date":"2021-01-20T23:33:24","date_gmt":"2021-01-20T16:33:24","guid":{"rendered":"http:\/\/54.151.235.32\/?p=11078"},"modified":"2021-03-03T17:55:46","modified_gmt":"2021-03-03T10:55:46","slug":"the-data-journey-from-raw-data-to-insights","status":"publish","type":"post","link":"https:\/\/renovacloud.com\/en\/the-data-journey-from-raw-data-to-insights\/","title":{"rendered":"The Data Journey: From Raw Data to Insights"},"content":{"rendered":"

In a world of proliferating data,\u00a0every company is becoming a data company<\/a>. The route to future success is increasingly dependent on effectively gathering, managing, and analyzing your data to reveal insights that you\u2019ll use to make smarter decisions. Doing this will require rethinking how you handle data, learn from it, and how data fits in your\u00a0digital transformation<\/a>.<\/p>\n

Simplifying digital transformation<\/strong><\/h3>\n

The growing amount and increasingly varied sources of data that every organization generates make digital transformation a daunting prospect. But it doesn\u2019t need to be. we\u2019re dedicated to making this complex task simple, putting power in the hands of the builders of business data and strategy, and providing\u00a0insights for everyone<\/a>. The launch of the\u00a0Google Sheets analytics template<\/a>\u00a0illustrates this.<\/p>\n

Understanding how data becomes insights<\/strong><\/h3>\n

A big barrier to analytics success has been that typically only experts in the data field (data engineers, scientists, analysts and developers) understood this complex topic. As access to and use of data has now expanded to business team members and others, it\u2019s more important than ever that everyone can appreciate what happens to data as it goes through the BI and analytics process.<\/p>\n

Your definitive guide to data and analytics processes<\/strong><\/h3>\n

1.Generating and storing data in its raw state<\/h4>\n

Every organization generates and gathers data, both internally and from external sources. The data takes many formats and covers all areas of the organization\u2019s business (sales, marketing, payroll, production, logistics, etc.) External data sources include partners, customers, potential leads, etc.<\/p>\n

Traditionally all this data was stored on-premises, in servers, using databases that many of us will be familiar with, such as SAP,\u00a0Microsoft Excel<\/a>,\u00a0Oracle<\/a>,\u00a0Microsoft SQL Server<\/a>,\u00a0IBM DB2<\/a>,\u00a0PostgreSQL<\/a>,\u00a0MySQL<\/a>,\u00a0Teradata<\/a>.<\/p>\n

However, cloud computing has grown rapidly because it offers more flexible, agile, and cost-effective storage solutions. The trend has been towards using cloud-based applications and tools for different functions, such as\u00a0Salesforce<\/a>\u00a0for sales,\u00a0Marketo<\/a>\u00a0for marketing automation, and large-scale data storage like\u00a0AWS<\/a>\u00a0or data lakes such as\u00a0Amazon S3<\/a>,\u00a0Hadoop<\/a>\u00a0and\u00a0Microsoft Azure<\/a>.<\/p>\n

An effective, modern BI and analytics platform must be capable of working with all of these means of storing and generating data.<\/p>\n

2.Extract, Transform, and Load<\/a>: Prepare data, create staging environment and transform data, ready for analytics<\/h4>\n

For data to be properly accessed and analyzed, it must be taken from raw storage databases and in some cases transformed. In all cases the data will eventually be loaded into a different place, so it can be managed, and organized. Using\u00a0data pipelines<\/a>\u00a0and data integration between data storage tools, engineers perform\u00a0ETL<\/strong>\u00a0(Extract, transform and load). They extract the data from its sources, transform it into a uniform format that enables it all to be integrated. Then they load it into the repository they have prepared for their databases.<\/p>\n

In the age of the Cloud, the most effective repositories are cloud-based storage solutions like\u00a0Amazon RedShift<\/a>,\u00a0Google BigQuery<\/a>,\u00a0Snowflake<\/a>,\u00a0Amazon S3<\/a>,\u00a0Hadoop<\/a>,\u00a0Microsoft Azure<\/a>. These huge, powerful repositories have the flexibility to scale storage capabilities on demand with no need for extra hardware, making them more agile and cost-effective, as well as less labor-intensive than on-premises solutions. They hold structured data from\u00a0relational databases<\/a>\u00a0(rows and columns),\u00a0semi-structured<\/a>\u00a0data (CSV<\/a>, logs,\u00a0XML<\/a>,\u00a0JSON<\/a>),\u00a0unstructured<\/a>\u00a0data (emails, documents, PDFs), and\u00a0binary data<\/a>\u00a0(images,\u00a0audio<\/a>, video).\u00a0 Sisense provides instant access to your cloud data warehouses.<\/p>\n

3.Data modeling: Create relationships between data. Connect tables<\/h4>\n

Once the data is stored, data engineers can pull from the data warehouse or data lake to create tables and objects that are organized in more easily accessible and usable ways. They create relationships between data and connect tables, modeling data in a way that sets relationships, which will later be translated into query paths for joins, when a dashboard designer initiates a query in the front end. Then, users, in this case,\u00a0BI and business analysts<\/a>, can examine it, create relationships between data, connect and compare different tables and develop analytics from the data.<\/p>\n

The combination of a powerful storage repository and a powerful BI and analytics platform enables such analysts to\u00a0transform live Big Data<\/a>\u00a0from cloud data warehouses into interactive dashboards in minutes. They use an array of tools to help achieve this.\u00a0Dimension tables<\/a>\u00a0include information that can be sliced and diced as required for customer analysis ( date, location, name, etc.).\u00a0Fact tables<\/a>\u00a0include transactional information, which we aggregate. The result: highly effective\u00a0data modeling<\/a>\u00a0that\u00a0maps out<\/a>\u00a0all the different places that a software or application stores information, and works out how these sources of data will fit together, flow into one another and interact.<\/p>\n

After this, the process follows one of two paths:<\/p>\n

4. Building dashboards and widgets<\/h4>\n

Now,\u00a0developers<\/a>\u00a0pick up the baton and they create dashboards so that\u00a0business users<\/a>\u00a0<\/strong>can easily visualize data and discover insights specific to their needs. They also build\u00a0actionable analytics apps<\/a>, thereby integrating data insights into workflows by\u00a0taking data-driven actions through analytic apps<\/a>. And they define\u00a0exploration layers<\/a>, using an enhanced gallery of relationships between widgets.<\/p>\n

Advanced tools that help deliver insights include\u00a0universal knowledge graphs<\/a>\u00a0and\u00a0augmented analytics<\/a>\u00a0that use\u00a0machine learning (ML)<\/a>\/artificial intelligence (AI) techniques to automate data preparation, insight discovery, and sharing. These drive automatic recommendations arising from data analysis and predictive analytics respectively.\u00a0Natural language querying<\/a>\u00a0puts the power of analytics in the hands of even untechnical users by enabling them to ask questions of their datasets without needing code, and to tailor visualizations to their own needs.<\/p>\n

5. Embed analytics into customers\u2019 products and services<\/h4>\n

Extending analytics capabilities even further,\u00a0developers<\/a>\u00a0can create applications that they\u00a0embed<\/a>\u00a0directly into customers\u2019 products and services, so that they become instantly actionable. This means that at the end of the BI and analytics process, when you have extracted insights, you can immediately apply what you\u2019ve learned in real time at the point of insight, without needing to leave your analytics platform and use alternative tools. As a result, you can create value for your clients by enabling data-driven decision-making and\u00a0self-service analysis<\/a>.<\/p>\n

Article written<\/a> in collaboration with Sisense, Author Adam Murray<\/em>
\n<\/em><\/p>\n

Tags:<\/strong>\u00a0Data Modeling<\/a>\u00a0|\u00a0Digital Transformation<\/a>\u00a0|\u00a0Extract Transform Load<\/a><\/p>\n

Source: Sisense<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

In a world of proliferating data,\u00a0every company is becoming a data company. The route to future success is increasingly dependent on effectively gathering, managing, and analyzing your data to reveal insights that you\u2019ll use to make smarter decisions. Doing this will require rethinking how you handle data, learn from it, and how data fits in […]\n","protected":false},"author":13,"featured_media":11083,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[666,44,667],"class_list":["post-11078","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-and-analytics","tag-data-modeling","tag-digital-transformation","tag-extract-transform-load"],"_links":{"self":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/posts\/11078","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/comments?post=11078"}],"version-history":[{"count":0,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/posts\/11078\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/media\/11083"}],"wp:attachment":[{"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/media?parent=11078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/categories?post=11078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/renovacloud.com\/en\/wp-json\/wp\/v2\/tags?post=11078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}