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Level the Playing Field with Cloud BI & Analytics

Make Data Your Startup’s Competitive Advantage

Introduction

If you run a startup or a small enterprise in any industry, you probably already know the following:

Cloud services are great

Platforms like Salesforce, Hubspot, ang G Suite keep your organization running. You love the flexibility of not having to depend on on-premises and internal IT resources.

You need to leverage data to scale

While you have found success making decisions based on individual app data, you know you’ll need interconnectedness to draw more meaningful insights.

Team members with interdisciplinary skills are valuable

You save time and money when domain experts are able to analyze data and make sound business decisions based on that information.

With that said, how do you make the leap into being a truly data-driven organization? It seems like the most well-known success stories come out of large enterprises with the resources to manage on-prem data warehouses and hire an army of IT talent and skilled SQL analysts.

So how can a startup compete when it only has one data engineer, maybe a SQL analyst or two, and limited budget for an on-prem BI program?

We’re in an exciting time in the world of data warehousing and analytics. A growing number of providers offer every level of the BI stack (ELT/data warehouses/analytics) in the cloud. With this modern BI stack you can compete with the large enterprises in a way that is affordable and scalable from the beginning.

We’ve put this guide together to show you how a cloud BI stack can help you harness data for growth opportunities, while continuing to focus on your strengths.

Read on and learn:

1


How cloud BI helps you overcome common analytics hurdles

2


The components of BI in the cloud

3


How cloud analytics fits into overall organizational goals and strategy

Analytics Hurdles & Cloud Solutions

Analytics Hurdles & Cloud Solutions

Hurdle 1:
Disjointed Data Sources

Where are you getting data?

According to SaaS management platform Blissfully, the average SMB uses 20 paid SaaS products. Each of those SaaS applications probably generates data that is critical to at least one department in an organization.

If you’re a technology startup, then there’s also the data generated from the product itself. Information about reliability and customer usage probably guides your decision-making process. There’s also data directly related to your company’s bottom line from payments and financial sources.

While SaaS apps are affordable and provide the flexibility that many startups need, they leave much to be desired in the reporting and analytics department. In some cases the only way to view a report is within an app’s own system.

So what do you do when you want to build a model based on data from two, three, four, or 20 applications, in addition to your internal data sources? Depending on the tools that you already have at your company, one of your resourceful analysts would probably do something like this:

1


Download multiple CSV file

2


Designate a primary key and join tables with SQL

3


View and perform basic analysis in Excel or an on-prem analytics tool

The above process assumes that all of your data sources are organized in a relational schema and there is no unstructured data to deal with. As we generate more IoT and mobile app data, this scenario becomes less and less likely.

Solution 1:
Cloud ETL

To overcome this first hurdle, you need a cloud ETL provider that’s connected to a cloud data warehouse (more on cloud data warehouses in a bit). ETL (Extract, Transform, Load) is the process of moving data from a source like a database into a data warehouse where it can be analyzed. Some cloud ETL vendors also support ELT (Extract, Load, Transform) in which data is converted into analyzable form after it is already in the data warehouse.

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