An Outcome-Driven Enterprise Data Strategy: Data L...
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This blog was originally posted on the D!gitalist Magazine as a part of the Enterprise Data Strategy series, which explores the importance of leadership and accountability in directing an overall data strategy tied to business outcomes. This is part 3 of the series, authored by Tina Rosario and is being reposted here to share with the SAP Community.
According to Gartner, “a digital business cannot exist without data and analytics.” I agree and would add “processes for managing that data” to this list. Why? We don’t create data just to create data. We produce it to help the business run, and to be successful, an outcome-driven data strategy must include processes detailing how to create, update, and delete (CRUD) business-critical data.
These CRUD processes are often part of a larger business process, such as the personal data used in lead management, the product data in research and development, and the vendor data in supply chain management. How we handle them varies based on the company, business goal, standards, data in question, and a host of other factors.
Some processes can be automated via business rules, while others require manual input. They can also be done en masse or at the individual record level. Whatever the approach, though, it’s important that the processes are as simple, automated, and friendly as possible and designed according to the data quality standards set by your organization.
In this third installment of a five-part series with my colleague Maria Villar, I’m going to dive into data lifecycle processes and their role in an outcome-driven enterprise data strategy.
Why are data lifecycle processes important?
Processes help us focus. They streamline our work. And they unite us towards a single goal: making data lifecycle processes an integral component of an outcome-driven strategy.
The volume of data and landscape complexity are growing and showing no signs of slowing down. Without processes, those volumes can get out of control. And without proper levels of management, those big data lakes and warehouses are ineffective and just data for storage sake, unnecessarily adding to the complexity.
Poor or no data processes often lead to poor data quality. If the data is unfit for use, if it’s not timely, if it doesn’t meet your defined standards, it won’t help you achieve the targeted outcome detailed in your data strategy. Processes help ensure that your data is useful, current, and still aligned with your business needs.
More and more data is governed by external policies and regulations. Having a well-honed CRUD process makes compliance with GDPR and other regulations easier.
Inefficient or complicated processes often equal poor data. Conversely, simple, easy processes equal a positive customer experience and more complete data.
What are the keys to success?
Simple, easy-to-use, and timely data processes are a given. Without that, you might as well “pack it in.” (See “gotchas” below.)
Automation is equally important. The ability to use business rules to validate the data or auto-populate fields enhances both the data quality and the overall process.
Each process needs an owner, a data steward. Often called line of business experts or data and process experts, these stewards keep an eagle eye on the data-quality nuances that others might miss. They also know the business, what the desired outcomes are, and the right balance of quantity vs. quality. For example, when it comes to Big Data and testing machine learning algorithms, sometimes more is better and quality is less important. There’s a balance, and these experts understand that.
What are the “gotchas”?
I can’t overstate the need for simplification. Most companies lack internal expertise in designing processes that are straightforward, make the data easy to use, and consider the users’ perspective. And that simplification isn’t just for the end user (customer, employee, etc.). It also covers the business process. You don’t want the data process to be so governance-heavy that it brings the process to a halt.
How do you get started?
Like every other component of an outcome-driven enterprise data strategy, if you start with the business need, you’re more than halfway to your goal. From there, develop a process map that starts at the point of data creation. Let’s use the lead management example to illustrate:
The business need is driving leads into the sales funnel.
How do I get the leads? What are the ways that I’ll collect data from the leads? When is that data collected and updated?
What are the different sources, data types, standards, and requirements at each step?
What data is needed for an actionable lead? Name, address, email, industry, company size, location, something else?
What validation rules do I need to ensure I get correct email addresses and other data?
For more information
Read all five of the blog posts in the
According to Gartner, “a digital business cannot exist without data and analytics.” I agree and would add “processes for managing that data” to this list. Why? We don’t create data just to create data. We produce it to help the business run, and to be successful, an outcome-driven data strategy must include processes detailing how to create, update, and delete (CRUD) business-critical data.
These CRUD processes are often part of a larger business process, such as the personal data used in lead management, the product data in research and development, and the vendor data in supply chain management. How we handle them varies based on the company, business goal, standards, data in question, and a host of other factors.
Some processes can be automated via business rules, while others require manual input. They can also be done en masse or at the individual record level. Whatever the approach, though, it’s important that the processes are as simple, automated, and friendly as possible and designed according to the data quality standards set by your organization.
In this third installment of a five-part series with my colleague Maria Villar, I’m going to dive into data lifecycle processes and their role in an outcome-driven enterprise data strategy.
Why are data lifecycle processes important?
Processes help us focus. They streamline our work. And they unite us towards a single goal: making data lifecycle processes an integral component of an outcome-driven strategy.
The volume of data and landscape complexity are growing and showing no signs of slowing down. Without processes, those volumes can get out of control. And without proper levels of management, those big data lakes and warehouses are ineffective and just data for storage sake, unnecessarily adding to the complexity.
Poor or no data processes often lead to poor data quality. If the data is unfit for use, if it’s not timely, if it doesn’t meet your defined standards, it won’t help you achieve the targeted outcome detailed in your data strategy. Processes help ensure that your data is useful, current, and still aligned with your business needs.
More and more data is governed by external policies and regulations. Having a well-honed CRUD process makes compliance with GDPR and other regulations easier.
Inefficient or complicated processes often equal poor data. Conversely, simple, easy processes equal a positive customer experience and more complete data.
What are the keys to success?
Simple, easy-to-use, and timely data processes are a given. Without that, you might as well “pack it in.” (See “gotchas” below.)
Automation is equally important. The ability to use business rules to validate the data or auto-populate fields enhances both the data quality and the overall process.
Each process needs an owner, a data steward. Often called line of business experts or data and process experts, these stewards keep an eagle eye on the data-quality nuances that others might miss. They also know the business, what the desired outcomes are, and the right balance of quantity vs. quality. For example, when it comes to Big Data and testing machine learning algorithms, sometimes more is better and quality is less important. There’s a balance, and these experts understand that.
What are the “gotchas”?
I can’t overstate the need for simplification. Most companies lack internal expertise in designing processes that are straightforward, make the data easy to use, and consider the users’ perspective. And that simplification isn’t just for the end user (customer, employee, etc.). It also covers the business process. You don’t want the data process to be so governance-heavy that it brings the process to a halt.
How do you get started?
Like every other component of an outcome-driven enterprise data strategy, if you start with the business need, you’re more than halfway to your goal. From there, develop a process map that starts at the point of data creation. Let’s use the lead management example to illustrate:
The business need is driving leads into the sales funnel.
How do I get the leads? What are the ways that I’ll collect data from the leads? When is that data collected and updated?
What are the different sources, data types, standards, and requirements at each step?
What data is needed for an actionable lead? Name, address, email, industry, company size, location, something else?
What validation rules do I need to ensure I get correct email addresses and other data?
Reach out to us (Maria for North America and Tina for everywhere else) to inquire about a 1:1 enterprise data strategy discussion.
About Tina Rosario
Tina Rosario is head of Data Innovation and chief data officer at SAP. In this role, she is responsible for helping businesses in the EMEA region drive data innovation, typically as an important component of an enterprise-wide transformation initiative. Tina works directly with senior business executives to design a data management approach, future-state information capabilities, and execution of their data strategy. She has more than 25 yeas of experience in business process reengineering, defining business impact, and leading transformation programs. She also serves as president of the European chapter of the Women in Big Data network, whose mission is to promote diversity in the field of data management. She lives with her family in Paris, France.
