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The Start-Up's Guide to a Modern Data Stack
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Data collection is a critical component of any business intelligence strategy. Fortunately, today’s technology allows organizations to capture and store nearly all types of data that are either directly or indirectly relevant to business operations. However, just collecting this data is not enough. Organizations must be able to analyze and transform this data into usable information.
The reality is that attempting to work with your data in each individual data source (like a marketing platform) is insufficient, and using multiple databases is not ideal for data analysis for business intelligence, especially when storing vast amounts of data. This method can make it difficult to merge all your data together to deliver accurate analytical results. An all-in-one data platform like Mozart can help build the bridge between data warehousing and business intelligence. Mozart allows you to set up an efficient data infrastructure in under an hour, so you have the ability to centralize and organize your data. You can then export that clean, reliable data into a BI tool or platform for greater data analysis and business intelligence.
This level of business intelligence integration enables your organization to efficiently store, organize, and use its data when it needs it. Your data teams will have more time to focus on analysis, because they won’t need to worry about updating APIs, maintaining integrations, or working with multiple vendors. Keep reading to learn more about Mozart’s solutions and how they can improve the process of data integration in business intelligence.
There are a number of advantages of data integration in business intelligence, such as:
One of the best benefits of data integration is that it gives you the ability to store and organize your data from a variety of sources into one convenient platform. For instance, Mozart connects with over 500 data sources, so you can connect the sources you need for your business — with no extra coding requirements. This easy access allows you to quickly and efficiently pull your data from various sources into well-organized data stacks.
Your analytics team can easily export the specific data it needs, without assistance from the engineering department. If your team realizes that they need additional data to enhance their analysis, they don’t have to start the process over. They can simply export the data they need, when they need it.
Business intelligence accuracy is one of the biggest data integration challenges. When you have so many data integration points to track and maintain, it can quickly become confusing and disorganized. With the Mozart platform, you can overcome these challenges through proper ETL mapping. With Mozart, you can view the entire pipeline to show what data is being pulled from various data sources.
This step allows your analytics team to ensure it’s using the correct data points at all times. Additionally, you can easily mitigate incomplete data sets by quickly writing data transformations to improve the quality of your data. This combination helps deliver a more accurate and complete BI analysis.
Another advantage of data integration is that it saves time. It reduces the need to continuously call in the engineering department to rebuild or restructure your data for better analysis. Instead, Mozart can automate this process without the need for an engineering team.
What does this mean for your business? It means better analytical data, faster results, and more accurate business intelligence. Ultimately, it means that your organization can have the analytical business intelligence it needs to make sound business decisions now, rather than later.
Data collection, data analytics, and business intelligence hold little value unless you can use them to take action. Mozart helps to establish a universal source of truth by providing an all-in-one platform that allows you to extract your data from various sources and organize it. You can then transform this data into visual analytical reports, charts, tables, and complex graphs, via integrated BI tools, to ensure you’re making informed business decisions.
For example, suppose your company is contemplating expanding into a new market. Data is a valuable tool in making this decision, but only if the data can be organized into meaningful results, such as charts, graphs, and reports. Since this is a complex decision, additional data points can be pulled into the results as needed. This accurate data analysis and established universal source of truth can help business leaders determine which action to take — in this case, whether to expand or not.
Your business intelligence efforts are only valuable if they’re helping you see both the big picture and answer specific questions. A frequent challenge with that is poorly integrated data — either too broad and general, too messy, or too poorly organized to provide you with the views you need. Mozart can help solve that problem, as the tool is compatible with over 500 data sources, such as Google Analytics, Facebook Ads, Oracle, Salesforce, BambooHR, Braintree, and more. It allows you to pull your data from all of these sources and centralize it in your data warehouse. Many questions require combining data from multiple sources, which is incredibly difficult without a well-organized data warehouse.
What is data integration? The concept of data integration definition is relatively simple: it’s the process of bringing together data from different sources to gain a unified and more valuable view of it, so that your business can make faster and better decisions. This allows users to see data in a unified form that provides actionable business intelligence. There are several steps to the data integration process.
The first step of any data integration process is to identify the types of data you want to include in the integration process. Consider all the sources your organization is currently using to collect data, such as social media platforms, CRM software, accounting software, ad platforms, and HR platforms, just to name a few.
The Mozart Data platform connects with hundreds of sources, including Oracle, Salesforce, Facebook, Google, Shopify, Tableau, and many more. With this in mind, you can create a list of all sources your company is currently using to collect data. In most cases, you want to integrate all of the data you’re currently working with, so that it’s readily available if you ever need it, and to understand what else is available to you, so you can incorporate more data at a later data.
Exporting data from multiple data sources can be quite complex. If you’re not careful, it’s easy to lose track of where all the data is coming from and how it relates to your business outcomes. This is where data mapping, or ETL mapping can help.
ETL stands for Extract, Transform, and Load. It refers to the process of using data integration tools to extract data from various sources and transfer it into one centralized, organized data warehouse. Data transformation can occur before or after the data is loaded into the warehouse, allowing you to re-organize and clean your data as needed (more on that below).
Before you even start the data extraction process, it’s important to map out how the data is extracted and where it goes, including how you may be combining it with data from other sources. For example, you might know that you will be combining data from an advertising platform with data from a campaign optimization tool in your warehouse, and that you will want to take steps to transform that data to make it compatible.
One often overlooked benefit of organized data extraction, with proper ETL mapping, is the ability to more efficiently schedule your data to sync when you need it to — not more or less often. This allows users at your company to trust that they’re always working with reliable, up-to-date data, without wasting resources syncing data that doesn’t need to be refreshed yet.
While there are many data integration techniques, Mozart Data allows users to view the entire data pipeline. This can help teams better manage the vast amount of data collected, view data lineage from point to point, and identify relevant dependencies.
Once you know what data you want to utilize for business intelligence, it’s important to take steps to prepare this data. This process can be started either during the ETL process or during data transformation after the data is loaded into the warehouse, so we’ll talk a bit more about it here.
It’s important to understand why you need to clean your data. Small errors in a data set can corrupt the results of data analysis, rendering business intelligence insights useless.
This cleansing process includes checking for incorrect or incomplete data as well as duplicated or irrelevant information. This step might include deleting duplicate or outdated data sets and correcting any notable errors.
BI tools are excellent at transforming data into analytical information. There are some limitations to BI tools’ capabilities. For example, these tools aren’t designed to store and organize data. To maximize the efficiency of your BI tools, it’s best to combine your data into centralized data stacks. With the Mozart platform, data is extracted from siloed data sources using ETL, which is then loaded into a centralized data warehouse. The end result is a data pipeline (sometimes referred to as an ETL pipeline) moving all of your data from siloed sources to where you need it.
And with the Mozart platform, once your data pipeline is in place, you can save time by automating the extraction and data warehousing process for you.
Mozart’s all-in-one data platform includes a data transformation layer to help you transform your data further after it’s loaded (when needed). Data transformation is essentially the process of organizing or formatting data differently to help you work with it more easily. One of the most common uses is to combine data from two different sources into one table, without having to manually copy-and-paste or struggle to get the data to match up (e.g., to get a Hubspot record to line up with the same user’s Salesforce record).
Data transformation is also incredibly useful for ad hoc analysis. Sometimes a user doesn’t need all of the data in a table, but also doesn’t want to permanently edit the format of that table. A quick data transformation allows the user to pull a new view for analysis, without changing the source data.
The final stage is to convert your data into analytical results via BI tools. Mozart Data integrates with nearly all BI tools as well as Google Sheets. Best of all, it can do all of this without the support of the engineering team. This gives your team the ability to conduct the data extraction and analytics as needed, without waiting for the engineers to have time.
Most importantly, business intelligence integration enables your organization to collect, organize, and analyze vast amounts of data quickly and efficiently. The importance of data integration is that it can help your organization make informed business decisions, such as forecasting costs and revenue and tracking inventory levels.
A major issue many businesses face regarding database integration is that standard warehouses — such as file-based and row-based warehouses — are good at storing large amounts of data, but not so good at analyzing it.
However, many companies have some sort of database full of valuable data that they want to mine for actionable insights. When you need to analyze data across various data sources, you need a data warehouse, like the one offered in Mozart’s all-in-one platform. And with that, you need a way to integrate the data in your database with that platform, without having to spend so much time and effort on the process that you give up and prioritize other work.
A tool like Mozart can serve as a database integration tool, so your organization can move the data needed for analysis to your data warehouse for efficient analysis.
This also allows you to integrate the data in your database (like a production database or inventory management system) with other data sources. One common example for SaaS companies is the need to combine product usage data in a production database with campaign data from marketing and sales tools. When that data is integrated, it becomes much easier to understand how different types of customers behave based on acquisition source, what types of customers are at risk for churning, what a happy customer ready for upsell looks like, etc.
With database integration, your company can use BI tools to easily organize these insights and visualize them, making it easier to share data with the entire company to make better decisions.
Learn more about how tools like Mozart Data can improve your organization’s business intelligence outcomes.
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