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.
According to Gartner, by 2021, the chief data officer (CDO) role “will be a mission-critical function comparable to IT, business operations, HR, and finance in 75% of large enterprises.” Why? CDOs are proving to be the linchpins of digital transformation.
No longer just a “governance overseer,” CDOs are data innovators who understand the importance of having an information game plan – one that’s tied to business outcomes. If you want to realize the value of your data – such as using machine learning and artificial intelligence (AI) to accelerate opportunities or advanced analytics to move the needle on data-driven business transformation – you need a strategy and an executive responsible for making it actionable.
Why is an enterprise data strategy important?
Today’s business strategies increasingly depend on data to automate processes, optimize customer and employee experiences, and grow or break into new markets. While the digital economy is driving the need for information to be connected, readily available, trusted, and even monetized, an outcome-driven enterprise data strategy is the only way to set a clear path forward in a business transformation journey.
Often, companies jump into new technologies like AI for “fear of missing out” only to realize they don’t know what to do with all that data. How should it be organized? Governed? Standardized? What steps are necessary to ensure quality?
A good data strategy addresses all of these questions in a holistic manner that resonates with all levels of the organization. It prioritizes the work to be done, focusing on the activities that bring the most value, and:
- Outlines all the capabilities necessary to achieve the business outcome (data quality management and tools, organizational structure, data acquisition, network strategy, compliance, ethics, etc.)
- Lays out a multi-year roadmap for achieving the capabilities
- Sets clear expectations on what’s possible (timeframe, costs, etc.)
Most importantly, an outcome-driven enterprise data strategy is actionable and based on the strategic priorities of the business. Without such a strategy, employees are more likely to be reactive, focusing on work that adds little value and is less likely to move the organization forward.
What does a great data strategy look like?
First and foremost, a transformative data strategy is “board ready,” meaning that you can communicate it across the across the company and up to the board level. It clearly conveys – in business language, not data language – the what, why, when, and how data and analytic capabilities contribute to accomplishing business goals. And it explains the value of the related investments in terms of business metrics and key performance indicators (KPIs).
You do this by starting with the business needs (so a well-defined business strategy and goals are a must!) and moving backward to the data and analytic requirements necessary to achieve the desired outcomes. This is true whether the goal is financial, customer-focused, or strategic, such as:
- Increased efficiency in employee onboarding
- Supply chain digitization
- Improved customer experience during renewals
- New business market penetration
- Growth through acquisition or divestiture
Secondly, it shouldn’t boil the ocean. A great strategy is focused. It’s not all things to all people. Pick the data that’s most important to advance the business.
Finally, a transformative data strategy doesn’t stand on its own. It requires strong support from senior executives and cross-functional management – and a leader (like a CDO) who can manage the work, promote the roadmap, and communicate the outcomes and achievements. Without this executive sponsorship and clear accountability, a well-defined strategy is just wallpaper. It’s ineffective.
“How organizations approach data will be a major determining factor for future growth and success – and thus, having someone to make the most of data is crucial. To guide these kinds of decisions, many organizations are making a structural change to their C-suite: the addition of a chief data officer (CDO).” – Visual Capitalist
What are the key components of an enterprise data strategy?
An enterprise data strategy is comprehensive and covers all the business, technical, and organizational data capabilities necessary to achieve the desired business outcome, which can be grouped into the following four categories:
- Organization and governance: strategy, change management, metrics, and RACI (responsible, accountable, consulted, and informed)
- Data lifecycle processes: for creating, updating, deleting, archiving, and protecting data
- Ongoing data maintenance: proactive data-quality maintenance to data standards
- Tools and technology: to automate, scale, deploy, and innovate
How do you get started?
The first step is to grab a copy of your corporate strategy and meet with senior executives and functional leadership to:
- Identify key initiatives that are data-rich and where data management or analytical insights are key
- Determine the gaps to be filled and outline the capabilities necessary to achieve the business outcome
- Prioritize the activities – because there will always be more work than people in the data space
- Read the next four blog posts in this series, each of which dives deeper into the four key components that we outlined above
For more information
- Check out the Data Strategy Guidebook: What Every Executive Needs to Know for more details.
- Gain a better understanding of your current organizational maturity with SAP’s executive-focused, next-generation Database and Data Management Assessment, which is based on top KPIs and best practices. Please click on “Start Survey” and proceed to register.
- Reach out to us (Maria for North America and Tina for everywhere else) to inquire about a 1:1 enterprise data strategy discussion
And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.
