Fusing data opens up many opportunities for businesses. By fusing data we mean merging and normalizing raw records into a master dataset. For more details about fused databases, read our post “What’s a Fused Database?”Here are some of the key reasons to start fusing data:
#1 Generate Business Insights, Faster
Who Cares: Executives, Analysts, Department Leaders, Customer Support/Success, Product Managers, Operations, IT Leaders
Companies invest to grow their businesses. But if the systems that track those investments aren’t linked to the systems that track sales and other business outcomes, how do you know what’s driving growth?
In recent years, marketers have sought out better ways to connect their investments to sales outcomes, including pipeline and new customers. Most marketers are still looking for more flexible ways to slice and dice this data. And that’s only one use case. Each department is looking to understand how their resources and investments are connecting to revenue -- such as account management, customer success and product management. Meanwhile groups such as finance and business analytics are looking to understand the complete picture.
When raw data is siloed, or isolated from related objects because it’s locked in separate systems, building trust in analytics is a challenge. Fused data empowers teams to connect the dots between business outcomes and investments. From marketing to product management to account management and customer support, regardless of your role, fused data helps you understand which key activities and milestones are leading to success.
For more on reducing the time to analytics, see #5 below.
#2 Orient Culture Around Solving Business Challenges
Who Cares: Executives
Fusing siloed data puts data in better shape for analytics and insights. Not only does it save IT time from having to manually extract and wrangle data, it gives teams a data resource they can finally trust. Rather than debating its accuracy in a vacuum, departments can spend time solving problems and acting on customer data.
Without trusted data, when a business challenges is raised by executive leadership, then cycles are spent by teams to debate the data in their data silo. Too often, so much time is spent debating the data that there’s no time left in the day to solve for challenges.
With a trusted data set, the focus shifts from debating problems to solving them. You craft a healthy culture to work in, one that orients employees around driving growth by tackling challenges.
#3 Improve Customer Experience
Who Cares: Executives, Customer Success Leaders, Marketing Leaders, IT Leaders
A more accurate, unified view of your customers allows you to deliver a consistent experience. Today’s customers expect this cohesive experience, that you know who they are and the interactions they’ve had with your company. This is a challenge given the range of interactions that occur with a business across different departments and applications -- emails, marketing campaigns, support tickets, live chats, mobile app purchases, sales quotes, and so on.
With fused data, teams use their unified view to deliver that more cohesive experience for customers. Fused data acts as the foundation for a trusted, centralized customer data set -- and can even serve to feed customer applications with consistent, customer data in a SQL format.
#4 Solidify Compliance Programs
Who Cares: Compliance Leaders, CIOs, IT Leaders
Data privacy is a huge concern for any organization that manages sensitive information. With the EU’s General Data Protection Regulation (GDPR) mandate, handling personal data is more important than ever. Fused data gives you a trusted record of customer data so you can see what data you hold, and meet all the requirements necessary for compliance.
Fusing data across systems can be a “win-win”, whereby individual business applications continue to perform their jobs without interruption, but the business knows it has a trusted, consolidated customer dataset to deliver on compliance requirements.
#5 Reduce Time to Analytics
Who Cares: Executives, Analysts, Operations, IT Leaders
On average, data scientists spend 80% of their time ingesting data, and about 20% mapping and modeling trends. Analysts must manually convert raw data -- data that has not been processed for use -- before it’s ready for analysis.
Photo credit & source: Forbes.
Traditionally to get from data in applications to analytics, IT teams must go through this process -- making up the 80% of their time:
1. Extracting Data
From a confetti of datasets -- scattered across web forms, chat logs, support tickets, sales quotes, ERPs, internal systems -- you need to decide which sources to extract and how to ingest these datasets via external APIs; study the sources to understand how data were entered, if there are errors, duplicates, or format issues. During data discovery, analysts will learn how data are encoded and stored (.csv, JSON, XML, SQL logs), and document these findings.
2. Data Prep
Preparing data for BI takes the lionshare of an analyst’s time. After extracting data from applications, they need to remove duplicates, normalize formats, match records, and resolve conflicts. This is typically a cumbersome, time consuming process.
Mapping and modeling relationships between objects are critical to draw insights, but further elongate data preparation. Database schemas need to account for all related objects. For example, a given individual contact will be related to accounts, opportunities, tasks, activities, tickets, events, products, orders, marketing campaigns and more.
After connecting applications and normalizing the data, the third step in preparing data for analytics is warehousing the data. Depending on your process and tools, you may warehouse the data first and then perform the prep steps we outlined in our preceding step 2.
During this step, each system’s data traditionally enters its own warehouse using Extract, Transform, Load, or ETL. While data prep takes the most time, warehousing is also very complex. Joining tables typically presents unanticipated obstacles. And when data inputs invariably change, connected data sources could get out of sync.
4. Feeding Analytics & BI tools.
With a traditional approach of siloed warehouses for each applications’ dataset, you would either run SQL queries against each of these separate databases or through many separate business intelligence (BI) dashboards. This step is often very painful, too. Data values can differ. One SaaS system’s dataset may format dates as DD-MM-YYYY, another as YYYY-MM-DD. One may split customer names into two fields, first and last, another have them in just one field, which lack of consistency makes data messy. You may get SQL access, but querying a lot of different data sources becomes laborious, especially when repeated.
By reducing that time to analytics, you shift your business from analyzing data on a monthly basis to a more real-time view. Fusing your data creates an automated data pipeline with these automated, connected steps:
1. Connect Your Data Sources.
Connect your data from SaaS applications such as Marketo, HubSpot, Salesforce and NetSuite. In just a few clicks.
2. Fuse Your Data.
Automate data matching, de-duplicating, resolving data conflicts and modeling object relationships for more real-time reporting and dashboards.
3. Warehouse Your Data Instantly.
Automatically access and query your database via a cloud data warehouse.
4. Feed Analytics & BI tools.
Enter your Warehouse Keys into your analytics platform and feed your favorite BI tools or dashboards -- including Tableau, Microsoft PowerBI, Amazon QuickSight, MetaBase, and more.
See for yourself what you can do with fused data.