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The expectation for forward-thinking organizations to utilize business intelligence (BI) or data visualization is increasingly becoming mainstream. BI tools provide users with a single interface to log in and access all company data under the same roof. Ideally, this data has been compiled, strategically modeled, and displayed in a format that empowers users to make informed decisions. While the benefits of data access are understood, the path to getting there is still a little fuzzy.

After some light Googling, you may already know that a digital transformation journey can quickly become a blind and bumpy ride. Whether researching the best BI tools on the market or the optimal ETL (Extract, Transform, Load) toolset available, it’s clear how technical the analytics adoption process can be regardless of company size or industry. Though the process is technical, organizations without an in-house analytics & engineering team can still successfully adopt cutting-edge technologies and utilize meaningful analytics in their day-to-day operations.

Below, we’ve outlined a short path to adopting business intelligence for your organization.

Identify Your Data Sources & ETL Tool

A healthy path to adopting a Business Intelligence tool does not actually begin with selecting a BI tool, but rather by traveling back upstream to each source of truth. In other words, the first step when launching a BI roadmap is determining every individual data source or application that contains valuable information. The data within each of these sources will have unique meaning or formatting (which data engineers call schemas), and therefore require an ETL tool to connect and individually load into one location for interpretation or data modeling. This secure location is referred to as a data warehouse.

etl tools for pulling data sources

Setup Your Data Warehouse

Data warehouses serve as a playground for engineers and analysts to access data in a raw form and then tidy and transform the information into a BI-ready format. This data warehouse serves as a singular point of truth where all information will be stored and live. Once data is in the warehouse and transformed, now it’s time for an analyst to model and convert these insights into sexy dashboards. The journey may be tedious and technical, but the results are customized insights that empower teams to make data-driven decisions in real-time.

example schematic for a data pipeline

Overwhelmed? Let’s Recap

To summarize, valuable BI is rooted in a strategic data pipeline, connecting all your company’s data sources together and transforming that data into meaningful analytics. Well-structured data pipelines that incorporate a layer of analytics are the foundation of organizational data adoption and success. Without a data pipeline, the end result from your BI tool will likely be dashboards displaying siloed data from individual sources rather than holistic business insights from all channels.

Companies that are growing and investing in the future will inevitably modify processes, change applications, or invest in new revenue streams. When organizational data is ever-changing, requirements across the entire pipeline are also going to adjust on an ongoing basis. Investing upstream in a well-built data pipeline allows seamless technical evolution alongside the velocity of growing information and data sources.

Untitled Can Help

As a team of technical insiders and data advocates, Untitled believes all businesses should have the opportunity to adopt digital transformation without the headache of data pipeline orchestration. Untitled offers a single-stop platform that houses the full data ecosystem needed to conduct analytics and BI more effectively.

Let Untitled simplify the path to BI for your business.

Emma Hixson

Author Emma Hixson

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