QuotaPath Implements an All-in-One Data Stack and Starts Leveraging Data Across the Organization
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As a modern business, chances are you have more data than you can realistically use, particularly if you don’t have a modern data stack in place. Though you may be collecting data from dozens of sources, that raw data has very little value if you cannot effectively analyze it. If you’re looking for a way to better leverage your business’ data, a business intelligence (BI) tool that offers sleek visualizations can be very tempting – but will be considerably less helpful without data warehouse tools. BI vs data warehouse is a bit of a false comparison since the two work best when used together.
While it might feel easier to limit the number of tools you leverage in your business, implementing a business intelligence tool without a data warehouse can end up making data analysis and decision-making more time-consuming and more complicated than needed.
This brief introduction to data warehousing and business intelligence should help you understand why you need a data warehouse for business intelligence and how these tools work in conjunction with one another.
What is data warehousing, and why might it be necessary for your growing business?
Businesses today are generating more data than ever before. From email marketing metrics to e-commerce platform sales data to social media engagement, we have a virtually endless number of data points on our business performance and our customers. In the right hands, these data points can be used to make data-driven business decisions, but it is not always so simple.
It can be technically and practically challenging to compare and analyze data from different sources. A data warehouse is a business tool that can be used to pull data from a multitude of sources into a centralized place to allow for that critical analysis. One of the characteristics of a data warehouse is that it can act as a single source of truth for an entire organization. Data warehouse integrations allow you to seamlessly load data from all your siloed sources into one location. With a properly configured data warehouse, you can be confident that the data you’re presenting and using for decision-making is accurate and consistent.
Some of the most popular tools include Snowflake data warehouse, which we use at Mozart Data, and Amazon Redshift and Google BigQuery.
The term “business intelligence tool” or BI tool is not as prescriptive as “data warehouse.” Many types of tools are used for business intelligence. However, we’re talking about the kinds of tools businesses use to visualize data and optimize their business performance. BI tools can be hugely useful since they make data analysis possible. In the right hands – and with the correct input – BI tools make data accessible to even the most non-technical members of your team.
Some of the most popular BI tools include Metabase, Tableau, Microsoft Power BI, Qlik, and Looker.
The difference between a data warehouse and a business intelligence tool is that data warehousing is about pulling, cleaning, and organizing raw data. In contrast, business intelligence tools take that clean data and provide a visually useful output. While technically a BI tool or data warehouse could be used independently, there is much more value to be found in using them together.
Business intelligence tools make it easy to turn raw data into the charts and graphs that so many businesses use to optimize their decision-making. It’s nearly impossible to make much out of the raw data that any service provides you without using some type of data visualization, especially when it’s coming from more than one source. For instance, you might have columns of numbers to tell you how many people are opening your marketing emails and more columns to tell you how much people are spending on your e-commerce site. Without a graph or chart to put them together, it will be more than tedious to try to connect one to the other and learn if your marketing emails are actually working.
However, as you collect all that raw data from all those sources, you will inevitably collect problems, too. Large data sets will no doubt include duplicated data, missing values, spelling errors, and more. Your business intelligence framework should include a plan to correct these issues so that you can actually trust the data from your BI tool — and that is the role of data warehousing.
Of course, clean data is just one of the advantages of a data warehouse. Pulling data into a centralized data warehouse can also mean fewer opportunities for data loss and fewer data breaches. When your data is centrally housed, it is much easier to develop and maintain a data governance plan and be confident that your data is both secure and that your business is in compliance.
By using a data warehouse in conjunction with your BI tool of choice, you make both tools that much more powerful. Leveraging a data warehouse to ensure your data is clean and consistent means even non-technical personnel can feel comfortable creating actionable data analysis in your BI tool.
At this point, you should have a foundational understanding of a business intelligence tool vs a data warehouse. If your BI tool makes it more difficult to parse your data instead of easier, it might be time to implement a data warehousing solution. Contact us to schedule a demo and learn how Mozart Data can help.