We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways organizations tackle the challenges of this new world to help their companies and their customers thrive.



In a world of proliferating data,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/big-data-big-benefits-what-leaders-say/" target="_blank" rel="noreferrer noopener"> every 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’ll use to make smarter decisions. Doing this will require rethinking how you handle data, learn from it, and how data fits in your<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/winning-the-future-digital-transformation-the-cloud-and-aws/" target="_blank" rel="noreferrer noopener"> digital transformation</a>.



<figure class="wp-block-image size-full"><a href="https://www.sisense.com/product/business-teams/" target="_blank" rel="noreferrer noopener"><img decoding="async" src="https://cdn.sisense.com/wp-content/uploads/packages-CTA-banners_BI-and-Analytics21.jpg" alt="Sisense for BI &amp; Analytics Teams" class="wp-image-98069"/></a></figure>



<h2 class="form-marker wp-block-heading">Simplifying digital transformation</h2>



The growing amount and increasingly varied sources of data that every organization generates make digital transformation a daunting prospect. But it doesn’t need to be. At Sisense, we’re dedicated to making this complex task simple, putting power in the hands of the builders of business data and strategy, and providing<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/whitepapers/analytics-and-bi-for-business-users/" target="_blank" rel="noreferrer noopener"> insights for everyone</a>. The launch of the<a aria-label=" (opens in a new tab)" href="https://docs.google.com/spreadsheets/u/0/?ftv=1" target="_blank" rel="noreferrer noopener"> Google Sheets analytics template</a> illustrates this.



<h2 class="wp-block-heading">Understanding how data becomes insights</h2>



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’s more important than ever that everyone can appreciate what happens to data as it goes through the BI and analytics process.&nbsp;



<h2 class="wp-block-heading">Your definitive guide to data and analytics processes</h2>



<figure class="wp-block-image size-full fancybox"><img decoding="async" src="https://cdn.sisense.com/wp-content/uploads/the-data-journey-infographic-770x1883-1-scaled.jpg" alt="The Data Journey: From Raw Data to Insights" class="wp-image-101945"/></figure>



This flowchart infographic shows how raw data becomes actionable insights. It will navigate you through every consideration you might need to make about what BI and analytics capabilities you need, and every step of the way that leads to potentially game-changing decisions for you and your company.



1. &nbsp; &nbsp; Generating and storing data in its raw state



Every organization generates and gathers data, both internally and from external sources. The data takes many formats and covers all areas of the organization’s business (sales, marketing, payroll, production, logistics, etc.) External data sources include partners, customers, potential leads, etc.&nbsp;



Traditionally all this data was stored on-premises, in servers, using databases that many of us will be familiar with, such as SAP,<a aria-label=" (opens in a new tab)" href="https://documentation.sisense.com/latest/managing-data/connectors/excel-online.htm#gsc.tab=0" target="_blank" rel="noreferrer noopener"> Microsoft Excel</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/oracle/" target="_blank" rel="noreferrer noopener"> Oracle</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/sql-server/" target="_blank" rel="noreferrer noopener"> Microsoft SQL Server</a>,<a aria-label=" (opens in a new tab)" href="https://documentation.sisense.com/latest/managing-data/connectors/ibm-db2.htm#gsc.tab=0" target="_blank" rel="noreferrer noopener"> IBM DB2</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/postgresql/" target="_blank" rel="noreferrer noopener"> PostgreSQL</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/mysql/" target="_blank" rel="noreferrer noopener"> MySQL</a>,<a aria-label=" (opens in a new tab)" href="https://documentation.sisense.com/7-1/managing-data/connectors/teradata.htm#gsc.tab=0" target="_blank" rel="noreferrer noopener"> Teradata</a>.



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<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/salesforce/" target="_blank" rel="noreferrer noopener"> Salesforce</a> for sales,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/marketo/" target="_blank" rel="noreferrer noopener"> Marketo</a> for marketing automation, and large-scale data storage like<a aria-label=" (opens in a new tab)" href="https://aws.amazon.com/marketplace/seller-profile?id=b9a3a7ee-d8b2-4322-a03a-eec7853e8610" target="_blank" rel="noreferrer noopener"> AWS</a> or data lakes such as <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/amazon-s3/" target="_blank" rel="noreferrer noopener">Amazon S3</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/glossary/big-data-hadoop/" target="_blank" rel="noreferrer noopener"> Hadoop</a> and <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/get/business-intelligence-on-microsoft-azure/" target="_blank" rel="noreferrer noopener">Microsoft Azure</a>.



An effective, modern BI and analytics platform must be capable of working with all of these means of storing and generating data.



2.&nbsp; <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/modernize-your-etl-processes-discover-better-insights/" target="_blank" rel="noreferrer noopener">Extract, Transform, and Load</a>: Prepare data, create staging environment and transform data, ready for analytics



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 a package such as<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/product/data-teams/" target="_blank" rel="noreferrer noopener"> Sisense for Cloud Data Teams</a>. Using<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/solutions/data-engineer/" target="_blank" rel="noreferrer noopener"> data pipelines</a> and data integration between data storage tools, engineers perform ETL (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.



In the age of the Cloud, the most effective repositories are cloud-based storage solutions like<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/redshift/" target="_blank" rel="noreferrer noopener"> Amazon RedShift</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/google-bigquery/" target="_blank" rel="noreferrer noopener"> Google BigQuery</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/sisense-and-snowflake/" target="_blank" rel="noreferrer noopener"> Snowflake</a>, <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-connectors/amazon-s3/" target="_blank" rel="noreferrer noopener">Amazon S3</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/glossary/big-data-hadoop/" target="_blank" rel="noreferrer noopener"> Hadoop</a>,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/get/business-intelligence-on-microsoft-azure/" target="_blank" rel="noreferrer noopener"> Microsoft 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<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/glossary/relational-database/" target="_blank" rel="noreferrer noopener"> relational databases</a> (rows and columns),<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/understanding-structured-and-unstructured-data/" target="_blank" rel="noreferrer noopener"> semi-structured</a> data (<a aria-label=" (opens in a new tab)" href="https://en.wikipedia.org/wiki/Comma-separated_values" target="_blank" rel="noreferrer noopener">CSV</a>, logs,<a aria-label=" (opens in a new tab)" href="https://documentation.sisense.com/latest/managing-data/connectors/xml.htm#gsc.tab=0" target="_blank" rel="noreferrer noopener"> XML</a>,<a aria-label=" (opens in a new tab)" href="https://documentation.sisense.com/latest/managing-data/connectors/json.htm" target="_blank" rel="noreferrer noopener"> JSON</a>),<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/understanding-structured-and-unstructured-data/" target="_blank" rel="noreferrer noopener"> unstructured</a> data (emails, documents, PDFs), and<a aria-label=" (opens in a new tab)" href="https://en.wikipedia.org/wiki/Binary_data" target="_blank" rel="noreferrer noopener"> binary data</a> (images,<a aria-label=" (opens in a new tab)" href="https://en.wikipedia.org/wiki/Audio_data" target="_blank" rel="noreferrer noopener"> audio</a>, video).&nbsp; Sisense provides instant access to your cloud data warehouses.



3.&nbsp; Data modeling: Create relationships between data. Connect tables



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,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/solutions/business-analyst/" target="_blank" rel="noreferrer noopener"> BI and business analysts</a>, can examine it, create relationships between data, connect and compare different tables and develop analytics from the data.



The combination of a powerful storage repository and a powerful BI and analytics platform enables such analysts to<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/solutions/business-analyst/" target="_blank" rel="noreferrer noopener"> transform live Big Data</a> from cloud data warehouses into interactive dashboards in minutes. They use an array of tools to help achieve this.<a aria-label=" (opens in a new tab)" href="https://support.sisense.com/hc/en-us/articles/115015336108-Table-Types" target="_blank" rel="noreferrer noopener"> Dimension tables</a> include information that can be sliced and diced as required for customer analysis ( date, location, name, etc.).<a aria-label=" (opens in a new tab)" href="https://support.sisense.com/hc/en-us/articles/115015336108-Table-Types" target="_blank" rel="noreferrer noopener"> Fact tables</a> include transactional information, which we aggregate. The<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/product/mash-up/elasticube/" target="_blank" rel="noreferrer noopener"> Sisense ElastiCube</a> enables analysts to mashup any data from anywhere. The result: highly effective<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/glossary/data-modeling/" target="_blank" rel="noreferrer noopener"> data modeling</a> that <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/data-mapping-tools/" target="_blank" rel="noreferrer noopener">maps out</a> all 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.



After this, the process follows one of two paths:



4. &nbsp; &nbsp; Building dashboards and widgets



Now,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/solutions/developer/" target="_blank" rel="noreferrer noopener"> developers</a> pick up the baton and they create dashboards so that <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/solutions/business-user/" target="_blank" rel="noreferrer noopener">business users</a> can easily visualize data and discover insights specific to their needs. They also build<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/three-steps-to-actionable-analytics-data-insights-outcomes/" target="_blank" rel="noreferrer noopener"> actionable analytics apps</a>, thereby integrating data insights into workflows by<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/whitepapers/from-dashboards-to-actionable-apps-the-future-of-embedded-analytics/" target="_blank" rel="noreferrer noopener"> taking data-driven actions through analytic apps</a>. And they define<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/whitepapers/deep-data-exploration-advanced-analytics-and-insights-using-python-and-r/" target="_blank" rel="noreferrer noopener"> exploration layers</a>, using an enhanced gallery of relationships between widgets. A package such as<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/product/business-teams/" target="_blank" rel="noreferrer noopener"> Sisense for BI and Analytics Teams</a> is designed to achieve easy to visualize analytics and form game-changing insights.



Advanced tools that help deliver insights include<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/knowledge-graphs-why-every-company-needs-them/" target="_blank" rel="noreferrer noopener"> universal knowledge graphs</a> and<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/whitepapers/augmented-analytics-the-future-of-business-intelligence/" target="_blank" rel="noreferrer noopener"> augmented analytics</a> that use<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/blog/data-for-all-empowering-users-with-ai-ml-and-analytics/" target="_blank" rel="noreferrer noopener"> machine 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.<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/press-release/sisense-releases-new-ai-natural-language-capabilities-to-improve-data-driven-decision-making/" target="_blank" rel="noreferrer noopener"> Natural language querying</a> puts 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.



5.&nbsp; &nbsp; Embed analytics into customers’ products and services



Extending analytics capabilities even further,<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/solutions/developer/" target="_blank" rel="noreferrer noopener"> developers</a> can create applications that they <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/product/embedded-analytics/embedding-analytics-modern-applications/" target="_blank" rel="noreferrer noopener">embed</a> directly into customers’ 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’ve 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 <a aria-label=" (opens in a new tab)" href="https://www.sisense.com/product/analyze/" target="_blank" rel="noreferrer noopener">self-service analysis</a>.&nbsp;



With a package like<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/product/embedded-analytics-form/" target="_blank" rel="noreferrer noopener"> Sisense for Product Teams</a>, product teams can build and scale custom actionable analytic apps and seamlessly integrate them into other applications, opening up new revenue streams and providing a powerful competitive advantage.



<figure class="wp-block-image size-full"><a href="https://www.sisense.com/product/embedded-analytics/" target="_blank" rel="noreferrer noopener"><img decoding="async" src="https://cdn.sisense.com/wp-content/uploads/packages-CTA-banners_Product-Teams.jpg" alt="Sisense for Product Teams" class="wp-image-92007"/></a></figure>



<a aria-label=" (opens in a new tab)" href="https://www.sisense.com/product/" target="_blank" rel="noreferrer noopener">Find out more</a> about how you can benefit from AI-powered self-service BI and build actionable analytic apps with all your data, simplify complexity and access insights that can drive your business forward. 



<a class="action-btn " href="https://cdn.sisense.com/wp-content/uploads/the-data-journey-infographic-770x1883-FINAL.pdf">View Full-Size Version</a>



Adam Murray began his career in corporate communications and PR in London and New York before moving to Tel Aviv. He’s spent the last ten years working with tech companies like Amdocs, Gilat Satellite Systems, and Allot Communications. He holds a Ph.D. in English Literature. When he’s not spending time with his wife and son, he’s preoccupied with his beloved football team, Tottenham Hotspur.

The Data Journey: From Raw Data to Insights

LinkedIn

Twitter

GitHub

curve-image-unique-image-unique

curve

3-dark-2-image-unique-image-unique

3 DARK 2

Get the latest in analytics right in your inbox.

Article