However, making use of all that data is no trivial feat. Hence the concept of data management. Though data management is a new, evolving practice, in this guide, we'll define several of the common concepts necessary for understanding data management.
What Is Data Management?
In case you aren't convinced data management is a big deal, there's an entire organization dedicated to it -- the Data Management Association (DAMA). DAMA defines data management as "the development and execution of architectures, policies, practices, and procedures that properly manage the full data lifecycle needs of an enterprise."
Since this is a no jargon guide, we'll state it in plain English: Data management is the practice of collecting all possible data and storing it in a way that helps your company make sound business decisions.
Customer Data Platforms vs Data Warehouses
There are two common ways that data is collected and stored: a data warehouse and a customer data platform.
A data warehouse is essentially a database a company transfers all of its data - usually from disparate sources - into. For example, at LawnStarter we put our web activity, financial data, product analytics data, CRM records, help desk records, and even search engine rankings all into a single Amazon Redshift database so that everything can be queried together. Data warehouses are often called data lakes or data marts.
A customer data platform is a more user-friendly platform that also collects data relevant to your customers and displays the data to end users in more relevant ways Often a customer data platform is simply the abstraction or ‘front end' of a behind-the-scenes data warehouse.
In both cases, typically a business is taking all the data from its CRM, help desk, web analytics, financial and other internal systems and putting them together.
First Party vs. Second Party vs. Third Party Data
First-party data is the data your company collects directly. This includes, but is not limited to, data from your company's CRM, marketing automation system, help desk, web analytics, and financial records It's the data your customers, prospects, and web visitors give you directly.
Second-party data comes from a secondary source, such as another company or publicly available information you source directly. For example, if you partner with an adjacent company and compare your customers' data with theirs, that's second-party data. Or, if you manually collect data on your sales prospects from LinkedIn or the company's website, that's also second-party data. Second-party data is the least common type of data.
Third-party data is that which you collect from data brokers. It's third-party because the company that you obtain the data from obtained it from somewhere else. A common source of third-party data is credit bureaus like Experian and Axiom. Banks report loan-related data to these credit bureaus about consumers, where it can be purchased by almost anybody. Often this data is anonymized for privacy purposes. For example, if you create a custom audience on Facebook, they will use third-party data to help target similar prospects via ads, but will not tell you the attributes of your individual customers.
Customer Data Platform (CDP) vs. Data Management Platform (DMP)
Customer Data Platforms and Data Management Platforms are two different concepts that can be easily confused. Though the names sound similar, they typically have different meanings.
A CDP typically pertains to the data your company collects from and about your customers. It's mostly first-party data that you can directly access. You may append second and third-party data to it, but typically a CDP involves you being able to see a line item with your customers' names and all of the info you have. CDPs are best used for making decisions about your current base of customers and prospects.
A DMP is typically a collection of cookie data used for programmatic display advertising. A DMP maps things like cookies, IP addresses, and device IDs to third-party data from sources like Experian and Axiom.
Customer Data Platforms vs. Customer Relationship Management Systems
A CRM is a subset of a CDP. A CDP will typically take in data from your CRM, and marry that data with other sources such as your help desk or marketing automation system. A CRM collects data as it is used in employee workflows.
Other Data Management Concepts
Data Migration: The process of moving data from one database to another.
Extract, Transform, Load: A process that involves pulling data from a database (extraction), manipulating it via code in some way (transformation), and writing it back to a database (loading).
Metadata: Data that describes other data within a database or data warehouse.
Fact Tables: Tables containing core business metrics that have been prepared in a way that they are easily understandable and user-friendly so that stakeholders across the business can access. These are often called "single sources of truth."
Business Intelligence: The practice of analyzing and presenting data in a way that provides insight into making business decisions. Often the product of a business intelligence team is a metrics dashboard or a report with insights.
Schema: The structure that defines how a database is organized.
Data Cleansing: The process of preparing data in a way that incorrect or not useful data points are removed.
Data Governance: The rules and procedures that define how a company's data is managed. Often a team or individual will be responsible for data governance, and that person will be responsible for things such as access requests, definitions of column names, and maintenance of database records.
Data testing: The practice of making assertions about your data, and then testing whether these assertions are valid. This concept can be used to test both the quality of your source data and to validate that the code in your data models is working as intended.
Benefits of Investing in Data Management
Now that we've covered a number of common concepts and definitions of data management, here are some of the benefits of investing in proper data management practices.
Understanding Your Most Profitable Customers
In the book The Inside Advantage, author Robert H Bloom asserts that one key to business success is understanding the customers who are most profitable and whom you enjoy working with most.
For a modern technology business, that answer may be easier said than done.
You can't simply look at which customers spend the most money with you. You also need to assess the cost of supporting those customers, which likely comes from your help desk software and potentially your payroll system. Additionally, larger customers likely cost more to acquire - that data point comes from your CRM, marketing automation, and advertising platforms.
Only when you put all of these together can you fully understand and identify your most profitable customers.
Evaluating Customer Acquisition Channels
The lifetime value of each customer depends on which channel you acquire that customer from. Especially when it comes to interruption marketing and paid acquisition in general, or when offering first-time signup discounts. A CDP or data warehouse allows you to connect your customer acquisition costs with your customer retention data, and understand your full ROI.
Grasping Your Full Buying Cycle
In most cases, customers typically don't simply click on an ad and immediately purchase your product, especially in B2B. There is a buying cycle that can sometimes last months.
In an enterprise funnel, your company is typically reaching your customers via cold email, ads, phone calls, nurture emails, trade shows, in-person meetings and proof of concept demos. It's an extremely complex process which can only be understood by managing one's data properly.