Are you struggling to make sense of the overwhelming amount of data available to your business? You're not alone.
In our 2022 survey, we discovered that a staggering 99% of CMOs are working with 10 or more different sources of data. It can be a daunting task to compare and analyze data from such diverse sources, especially when you need to make quick, informed decisions.
Data gathering is essential to unlocking the true potential of your data, and it all starts with understanding your pain points.
In this comprehensive guide, we'll discuss these challenges and provide a data integration solution that brings all your data together in a single, unified view. From here, you can analyze and generate valuable insights for your business, and ultimately improve your decision-making process.
With a solid understanding of data gathering and how to leverage it for your business, you’ll confidently navigate the world of data integration platforms, and data collection tools, and gain insights into business intelligence and analytics, integrated data management, and forming a data integration strategy.
Are you ready to harness the power of data gathering and transform your business? Dive into our guide and discover the solutions you've been searching for.
Data gathering is the essential process of collecting data from various sources to empower your business with effective data-driven decision-making. Accurate and efficient data gathering provides the foundation for data analytics and business intelligence, enabling you to understand, visualize, and act on your data.
However, many businesses face the challenge of holding data in multiple locations with different formats. This can make it difficult to draw accurate comparisons, gain insights, and make important decisions based on your data gathering efforts.
The solution to this problem is data integration.
Data integration is the process of bringing all your different data sources together into a unified view, using methods such as automated data integration and real-time data integration. By transforming and standardizing your data, it becomes consistent and easy to compare, analyze, and make decisions from. This provides a single source of truth for different teams across your business.
There are multiple ways that your business can approach data integration, including:
Businesses that only have a handful of data sources, might consider manual data integration as a solution. However, this is often not an effective approach for a number of reasons:
Requiring a significant investment of time and effort from your data team, integrating data manually drains resources that could be better spent on analysis and insights.
Due to manual data integration being time-consuming, it’s often not possible for data teams to complete frequent updates. This means you could be making business decisions on data that are days or weeks old.
Having the potential for human error, manual data gathering involves multiple manual steps. Each step can lead to minor mistakes, having big consequences if they lead to incorrect analysis, reporting, or decision-making.
As the volume and complexity of your data increases, a manual approach to data gathering becomes less feasible.
By adopting data integration best practices, businesses can overcome the challenges of manual data gathering and streamline their data collection efforts.
To avoid the issues of manual data integration and streamline your process of data gathering, automated systems are often necessary.
Gathering data from disparate systems into one central location and ensuring that it's accurate and complete; automated integration helps you manage your data more effectively and ensure that it's always up-to-date.
We’ve put together a list of tips to help you ditch manual data integration and move toward an automated process:
Without a clear understanding of the data sources you need to integrate, your business might be wasting time and resources on extracting irrelevant data.
Decide on the specific data sources your business needs and can make decisions from.
Without a well-defined data integration plan, you run the risk of overlooking some important areas of data integration that can lead to issues with the quality of your data.
Make a data integration plan that lists your ideal schema mapping, naming conventions, transformation and enrichment requirements.
You’ll want to make sure that your data is as valuable to your business as possible.
It’s important to choose an automated solution that transforms and enriches your data, standardizing and enhancing it to deliver better value across your business.
Choosing the wrong data integration solution can lead to limitations in your data gathering capabilities and frustrations across the business.
Take advantage of the demos or free trials available on many automated data integration platforms and choose a solution that meets the current and future data needs of your business and the technical capabilities of your team.
If you don’t have robust data governance it can result in compliance issues, reduced quality of data, and a lack of trust in your business data.
Check your data integration platform has the right features to ensure the quality, compliance and availability of your data.
Without effective BI or data visualization tools, your business might fail to fully leverage the benefits of your integrated data.
Choose the right solutions to help teams across your business gain valuable insights and enable data-driven decision-making across the company.
By choosing the right tool to automate the process of data gathering, your business can benefit in a number of ways.
Selecting the right tool to automate your data gathering process can significantly impact your business, unlocking numerous benefits and efficiencies. Here's what you need to know about automated data integration and how it can transform your organization:
Automated data integration reduces the time your data team spends on manual data processing, allowing them to focus their talents on analysis and insights that drive business growth. This increased efficiency can lead to a more productive and agile organization.
By automating the data gathering process, you can minimize the risk of errors and inconsistencies common with manual data processing. This ensures that your business makes decisions based on accurate, high-quality data, ultimately leading to better outcomes.
The right automated data integration platform provides real-time or near real-time data access, enabling your business to make better and faster decisions.
Choosing a solution that enforces data validation rules, standardizes data formats, and maintains data lineage helps you ensure the consistency and quality of your data. This robust data governance framework not only supports better decision-making but also helps your organization maintain compliance with data regulations.
There are many ways to integrate data from multiple sources, but one of the most common approaches is to use an automated data integration platform or an Extract, Transform, and Load (ETL) tool.
ETL, or Extract, Transform, Load, is a critical process in data gathering and management that involves extracting raw data from various source systems, transforming it into a standardized format, and loading it into a target database for analysis.
When choosing the best ETL tool to manage this process, there are several factors to consider:
Select an ETL platform that is not only capable of handling your current data requirements but is also future-proofed to accommodate your evolving business needs. This ensures that your investment in an ETL tool remains valuable as your organization grows and your data landscape changes.
Choose an ETL tool that aligns with your team's technical skills. Opt for a user-friendly solution or one that offers sufficient training and support to ensure your team can effectively leverage the tool to manage your data integration processes.
Evaluate the pricing models of various ETL tools, comparing them against the features and benefits they provide. This will help you find the right balance between affordability and functionality, ensuring you select a tool that delivers the best value for your investment.
Ensure that the ETL tool you choose can seamlessly integrate with your current data sources and destinations. This compatibility is crucial for maintaining a smooth data integration process and minimizing disruptions to your existing workflows.
Manually managing data extraction, transformation, and loading can be time-consuming, error-prone, and lead to inconsistencies; hampering your organization's growth.
To overcome these difficulties of data gathering, your business needs an ETL tool that automates the entire data integration process, streamlines data management, and ensures data consistency and quality.
Evidently, automating the ETL process can significantly improve your organization's data management capabilities, but finding the right tool for your specific business needs can be challenging.
If you’re looking for a place to start, some of the best ETL tools on the market include:
A comprehensive ETL solution for large enterprises dealing with complex user data. It offers 100 pre-built connectors and is ideal for businesses in sectors like healthcare and financial services.
Designed for marketing and data analysis professionals, this ETL tool connects to over 600 data sources. It offers real-time data fetching and advanced data transformation capabilities.
This free open-source ETL tool is ideal for organizations with the technical expertise to configure and troubleshoot the platform. A commercial version offers additional customer support.
Offering flexible pay-as-you-go pricing, Fivetran's user-friendly interface makes it simple to integrate data across 355 supported data sources. However, advanced SQL knowledge may be needed for data transformation.
A marketing-focused ETL tool connecting to 100 different data sources that’s great if your business wants to load data into Google Sheets or Excel.
A straightforward ETL tool offering seamless integration with 150+ data sources and affordable pricing. Custom connectors can be coded for unsupported data sources.
In the world of data management, understanding the differences between structured and unstructured data is essential for effectively leveraging your organization's information assets. ETL tools play a crucial role in handling structured data, but it's important to recognize the value and challenges of unstructured data as well.
Structured data: the measurable metricsStructured data refers to organized, measurable information that can be easily processed and analyzed by ETL tools. Examples of structured data include marketing performance metrics, sales performance data, and website analytics. This type of data is typically stored in databases, spreadsheets, or other formats that follow a specific schema or structure.
Unstructured data: the hidden treasureContrary to structured data, unstructured data accounts for more than 90% of all data generated by businesses. This type of data encompasses a wide range of formats, such as videos, documents, emails, and social media posts. Unstructured data can provide valuable insights into customer behavior, market trends, and other aspects of your business that may not be captured by structured data.
However, unstructured data presents unique challenges when it comes to extracting meaningful insights. Its inconsistent format makes it difficult to analyze without the help of a data science team or advanced machine learning tools.
There are also differences in the way that structured and unstructured data is stored, processed, and used by businesses:
Stored in relational databases with predefined schemas
Stored in data lakes or file systems without strict schemas
Processed using ETL tools and SQL
Processed using NLP, image recognition, AI, and machine learning
Ideal for statistical analysis and trend forecasting
Useful for exploring sentiments and opinions
Scalability requires more processing power and can be costly
More affordable to scale, when stored in solutions like data lakes
Used in marketing analysis, financial reporting, and inventory management
Applied in social media monitoring, sentiment analysis, and brand reputation tracking
By having a data gathering process that includes both types of data, your business can make more informed and strategic decisions. You can gain a metric-driven, measurable view of your business from structured data, as well as a qualitative, sentiment-driven view from your unstructured data.
We’ve already covered a lot of important areas of data gathering, including the benefits of automated data integration, the ETL tools that can support the data integration process, and the different types of data that your business can gather.
But data's true value is only realized when the people who need to use it can understand what they're looking at, gain insights, and action that information to improve how they do their jobs.
This is where business intelligence and data visualization tools come in.
Business Intelligence (BI) solutions transform your integrated data into actionable insights, with user-friendly dashboards and pre-built visualizations making data easy to understand and action.
Data integration and business intelligence are closely connected, as each enhances the effectiveness of the other. A robust data integration process feeding a data warehouse with real-time data is useful only when it's paired with the right BI tool for visualization and analysis.
Two popular business intelligence solutions are Microsoft Power BI and Tableau.
Microsoft Power BI is a great solution if your business already uses Microsoft products like Excel and wants a familiar interface. It’s also good at creating interactive dashboards and reports, which can be shared with stakeholders across your business.
Tableau is better for creating visually stunning dashboards and performs better with large data sets, making it a good solution if you have a significant volume of data. However, it does have a steeper learning curve.
Data connectors play a pivotal role in streamlining the data integration process, making it easier for businesses to connect with various data sources without the need for complex coding. These pre-built connectors are a crucial component of ETL and data integration solutions, providing seamless integration and enhancing the versatility of these tools, without the need for complex coding.
Pre-built Connectors: Versatility and Ease of UseThe more pre-built connectors a data integration solution offers, the more adaptable and user-friendly the tool is likely to be right from the start. These connectors simplify the process of integrating with a wide range of data sources, such as marketing tools, eCommerce platforms, databases, and CRMs.
For instance, Adverity's automated data integration platform boasts an impressive library of over 600 data connectors, making it one of the most extensive offerings in the market. This extensive selection enables businesses to easily connect with various data sources and streamline their data integration process.
Custom Connectors: Tailoring to Your Unique Data NeedsWhen evaluating data integration or ETL tools, it's essential to consider the ease of setting up custom connectors for your specific data sources. Custom connectors allow you to integrate with bespoke or niche data sources that may not be covered by pre-built connectors, ensuring that your unique data needs are met.
The Adverity platform is flexible, with customizable connectors that allow you to easily connect to any data source you need.
We’ve talked about structured data typically being stored in a data warehouse, and unstructured data commonly being stored in a data lake - so what’s a data lakehouse, and why is it important?
A data lakehouse is a form of data architecture that combines the strengths of data warehouses and data lakes.
Data warehouses are great at consolidating data from various structured data sources to provide a single source of truth. However, they often struggle with unstructured data and can be expensive to scale and maintain.
Data lakes are great for storing massive amounts of raw data in its native format but can present challenges in data governance and often require skilled personnel or further investment in tools to extract meaningful insights.
Data lakehouses, as a hybrid solution, combine these strengths and address the limitations of both data warehouses and data lakes, providing businesses with a more comprehensive data management solution.
You might consider a data lakehouse as the destination for your data gathering if you’re looking for:
Data lakehouses allow your business to store large volumes of structured, semi-structured, and unstructured data without you having to worry about soaring costs.
A data lakehouse supports all types of data and can enforce a schema to provide a unified view of your business data in a single location.
You can leverage advanced analytics techniques like machine learning and predictive modeling to make strategic decisions.
A data lakehouse enables schema enforcement to ensure consistent data quality, making it easier to manage, track, query, and analyze data.
The metadata layer in a data lakehouse helps catalog every piece of data in storage, making it easy for everyone across your business to organize and analyze information.
Real-time data integration is a game-changer for businesses seeking to access up-to-date information for informed decision-making. By leveraging automated data integration, organizations can bypass the need for manual data processing by data and IT teams, resulting in quicker access to recent data.
Not all data integration solutions are created equal, and their capabilities can vary in terms of fetch frequencies – i.e. the regularity at which they update your unified data view with refreshed information. A higher fetch frequency such as Adverity’s ensures that your business has access to the most current data, enabling you to make well-informed decisions swiftly.
Implementing a data integration solution with real-time capabilities can unlock numerous advantages for your organization, including:
A powerful data integration platform can provide your business with many benefits, saving you time, improving your data quality, enabling access to real-time data, and allowing you to gain valuable insights for better business performance.
Here are the key features to look for in a data integration solution:
A good data integration platform should have a large library of pre-built data connectors for various marketing campaigns, eCommerce platforms, databases, analytics tools, and CRMs. It should also support custom data connections for bespoke data sources.
Your choice of data integration platform should be able to automate data transformation, helping to match metrics and dimensions across your data based on your data schema. It should also be able to enhance your data with a range of enrichments.
The platform you choose should be able to send your business data to any destination, including data lakes, data warehouses, data visualization tools, BI tools, and cloud storage.
Automated data integration helps maintain up-to-date and high-quality data, building trust in your business data for decision-making. The platform you decide on should have governance features to control data retention periods and user permissions, ensuring that your organization maintains control over its data.
It’s important that your data integration platform has a pricing plan that’s able to scale effectively with your business.
Pay-as-you-go pricing may initially seem attractive, but it can soon become prohibitive as the volume and complexity of data grows. It’s also important to take advantage of any free trials or demos to ensure the platform meets your specific needs before committing.
Adverity is a trusted integrated data platform used by leading brands such as Vodafone, reBuy, and IKEA. With one of the world's largest libraries of pre-built connectors, Adverity empowers businesses to harness the power of real-time data for effective data gathering.