Data is the new oil; It is increasingly becoming more valuable than ever before.
Not only is it being generated significantly faster than ever before, we are also increasingly awash with a wider array of data as the world sees explosion in data volume that has expanded in both variety, and velocity.
This is evident in the mind-blowing fact that 90% of all the data in the world now has been generated over the last two years. As such, the success of any business is tied more and more to its ability to manage and leverage these torrents of data.
However, less than half of an organization’s structured data is actively used in making decisions — and less than 1% of its unstructured data is analyzed or used at all.
Several challenges stand in the way of businesses from leveraging their data asset for a data-driven decision making.
Three of the major data challenges are exponential increase in the volume of data; ever increasing diversity of data, and the speed at which businesses have to deliver insights for a timely decision-making.
The exponential growth of data volume and proliferation of SaaS (Software-as-a-Service) apps that businesses are now increasingly making use of particularly result in myriads of data silos that further complicates business’s effort of addressing their data challenges.
Data, such as customer churn rates, sales data, costs of goods sold, is important but is of limited value unless it is integrated with other data, analyzed, and transformed into insights that can guide decision making. Churn rates, for example, put into a historical context give better and meaningful perspective. So do sales data when put into a market context. Integrating such core input data with data from multiple internal as well as external sources requires first and foremost a central consolidated data storage.
Data storage and management is therefore a crucial first step and basic building block towards making use of the available data and implementing a data-driven decision-making.
When it comes to meeting today’s analytics needs and addressing the intelligence challenges of businesses, the Cloud is a game changer and it’s a game changer for lots of reasons ranging from scalability, agility to efficiency.
Cloud-based solutions are increasingly geared towards addressing these challenges much more so than the traditional on-premise solutions. Cloud computing helps businesses to meet these challenges by making possible data management at scale and speed without having to worry about the infrastructure.
Cloud data warehousing specifically enables businesses to maintain a single source of truth for their data across the scattered silos, as well as distinct views through which different business functions can access the data in ways that meet their unique data needs — at much lower cost and with much greater flexibility and speed.
Fully managed cloud data warehouses like Snowflake Computing, Amazon Redshift, and Google BigQuery, help business meet today’s data challenges by easily integrating with cloud-based ETL tools that can easily prepare, transform and structure raw data on demand and by making data immediately available for analysis.
The fully managed nature of cloud-based data warehouses also helps streamline this entire process and enables seamless implementation of real-time analytics so analysts and IT teams can focus on what matters to the business.
In light of this, building a cloud BI and analytics for a SaaS company I work for is a recent big project I have recently worked on as a lead. In this project, I worked on things ranging from identifying current data needs and challenges to extensively researching and evaluating multiple cloud solutions such as cloud data warehouses, cloud ETL and BI tools — meticulously weighing in the strengths and weaknesses of competing cloud solutions and cloud data warehousing solutions and corresponding ETL vendors.
I have documented my progress and researches throughout all the phases of this project. In a series of article I will be publishing here, I will be sharing with you my experience and how the project transformed my company’s BI and analytics.
Written by Abel Z. Shiferaw . Abel is Manager of Business Reporting and Data Analysis at mHelpDesk. He was previously as lecturer at St. Merry University, Ethiopia.