Turning Data Initiatives into Business Value: A Guide for Data Leaders
In the dynamic world of modern business, data has emerged as the new strategic capital. Many organizations have recognized this, investing heavily in data management initiatives such as master data management (MDM). However, in 2021, Gartner reported, “Through 2025, more than 75% of MDM programs will fail to meet business expectations because of a failure to connect MDM value to business value.1 Why is this happening, and how can data leaders avoid falling into the same trap? This blog post brings together key insights from a recent webinar that focused on aligning data strategy with business outcomes and provides a comprehensive guide to building iterative, high-impact data programs.
The Pitfall: A Disconnected Approach to Data Management
One of the central issues highlighted by presenters is that data leaders often become entangled in designing “ivory tower” solutions. These projects, though technically robust, become overly complex and often detached from real business needs.
Consider this: MDM and data governance projects often focus on technical details like APIs and governance policies, with no clear line of sight to business objectives. As teams spend months, sometimes even years, over-specifying and running proof of concepts, the resulting solutions are launched too late, unable to address the current needs of the business. In the end, it’s not surprising then that Gartner believes 3 out of 4 MDM programs will fail to meet expectations in the next few years.1
This pitfall is often rooted in the perception of data as an end goal, rather than an enabler of strategic outcomes.
The Solution: Begin with Business Value, Work Backwards
To escape this common trap, the speakers advocated for a “working backwards” approach. This strategy involves first defining business objectives and then determining the minimum data needs to accomplish them. This scientific method approach of experimentation to realize iterative benefits over time is broken down into four core stages:
1. Set Vision and Context: Understand the market and organizational dynamics, future business vision, and pain points. Identify stakeholders and executive sponsors who care about these challenges and summarize the goals.
Understand the Market and Organizational Dynamics
To create a data strategy that delivers value, you must first comprehend the external market dynamics and the internal workings of your organization.
In terms of market dynamics, it’s vital to understand the industry trends, competitor strategies, regulatory changes, and technological advancements. Knowledge of these factors helps identify opportunities and threats that could influence your business strategy, and in turn, the data strategy.
Internally, understanding your organization’s dynamics involves assessing its structure, culture, key processes, and technology landscape. This awareness helps identify the capabilities and constraints that may influence the execution of your data strategy.
Future Business Vision and Pain Points
Your data strategy should be a direct response to your organization’s future vision and current pain points. This means understanding the strategic direction of the company, its long-term goals, and the challenges it faces in achieving them.
For example, if the company’s future vision is to provide a personalized customer experience at scale, the data strategy should address how to collect, manage, and analyze data to understand customer behaviors and preferences. If a significant pain point is poor data quality impacting decision-making, the strategy should focus on improving data governance and quality management.
Identify Stakeholders and Executive Sponsors
Identifying and engaging the right stakeholders is critical to the success of your data strategy. Stakeholders may range from executive sponsors who can champion your initiatives, business unit leaders who will use the data, to IT staff who will implement the technology.
Engage with stakeholders to understand their needs, expectations, and concerns regarding the data strategy. Executive sponsors, typically C-suite or other senior leaders, are particularly important as they provide strategic guidance, secure necessary resources, and help overcome organizational barriers.
Summarize Goals
Once you have a clear understanding of the market and organizational dynamics, business vision, pain points, and stakeholders, you can then articulate the goals of your data strategy.
These goals should be specific, measurable, achievable, relevant, and time-bound (SMART). They should also clearly relate to the broader business objectives. For instance, a goal might be to “improve the accuracy of customer data by 20% within the next six months to support sales and marketing personalization efforts.”
2. Discover and Prioritize and Specify: Discover user stories aligned to the goals and pain points. Prioritize these user stories and specify minimum solutions, while estimating the potential value of each story. Focus on the must-haves first, and specify the people, process, and technology changes for the next most valuable story.
3. Deliver, Measure, Course Correct: Implement the prioritized user stories, track engagement and business KPIs, and make adjustments if outcomes are not achieved. Create internal customer success stories.
Deliver: Implementing Prioritized User Stories
At this point, you’ve identified and prioritized user stories based on their potential to deliver value aligned with your strategic objectives. Now, it’s time to implement.
In Agile terminology, a “user story” is a simple explanation of a software feature from an end-user perspective. But in this broader context, we use it to represent a business objective that relies on data.
Implementing user stories often involves developing new capabilities, such as establishing new data sources, enhancing data quality, or implementing analytical tools. A good practice is to adopt an iterative, agile approach, allowing you to deliver value incrementally and respond quickly to any emerging needs or issues.
Measure: Tracking Engagement and Business KPIs
Once your data initiatives have been implemented, the next step is to measure the impact. Measurement helps determine whether your efforts are delivering the expected value and informs any necessary course corrections.
Key metrics could include technical indicators like improvements in data quality or reductions in data processing time. But equally important are business KPIs that gauge the impact on the organization’s strategic objectives. These could include operational efficiency measures, customer engagement metrics, or financial performance indicators.
For instance, if one of your user stories aimed to improve the quality of customer data to enhance marketing campaign effectiveness, you might track metrics like the percentage of records with errors (a technical indicator), and the response rate to marketing campaigns (a business KPI).
Course Correct: Making Adjustments as Needed
Despite the best planning, things don’t always go as expected. Perhaps a data source didn’t deliver the expected quality, or a new analytics tool didn’t integrate well with existing systems. Or maybe the business context has shifted, and the priorities identified at the outset are no longer as relevant.
Course correction involves taking the insights you’ve gained from measurement and using them to make necessary adjustments. This could involve technical fixes, changes in focus, or even a pivot in your overall data strategy. An agile approach, with its emphasis on learning and adaptation, is particularly effective in this regard.
Create Internal Customer Success Stories
One often overlooked aspect of this phase is the creation of internal customer success stories. These stories provide tangible evidence of the impact of your data initiatives. They can demonstrate how better data quality improved marketing outcomes, or how a new customer segmentation model increased cross-selling, for example.
Success stories serve a dual purpose. Internally, they help build credibility and secure ongoing support for your data initiatives. Externally, they can enhance your organization’s reputation as a data-savvy business, attracting customers and partners.
4. Recalibrate: Continuously recalibrate by refreshing context and discovering new opportunities.
By focusing on driving business value, data leaders are more likely to secure necessary funding for their initiatives.
An Outcomes-First Data Strategy Framework
The webinar outlined a specific framework to put the ‘working backwards’ concept into practice. Start by identifying target business outcomes, such as reduced costs, increased revenue, or lower risk. Then, identify associated metrics and KPIs, followed by the processes and applications that influence those metrics. This process helps map required unified data elements like customer, product, supplier, and evaluate necessary sources and governance.
This framework creates a roadmap directly linked to strategic goals and specific metrics. It facilitates effective internal storytelling, showcasing how data initiatives enabled success.
Think Big, Start Small, Deliver Value
While having big-picture outcomes in mind is important, the speakers recommended starting small and delivering value quickly.
Data leaders should avoid attempting to tackle every data domain at once, instead focusing on the first priority user story with the highest potential ROI. Implementing just enough data and capabilities to enable that story, ignoring nice-to-haves and distractions, and expanding iteratively are all key parts of the process.
With this approach, early successes can be leveraged to build momentum and trust for larger initiatives.
Overcoming Common Roadblocks
Even with an outcomes-centric approach, several challenges may arise. The speakers offered tips to tackle some common roadblocks, including lack of access to business stakeholders, unclear business context, failure to prioritize, no connection between data and metrics, and lack of visibility into progress.
Sustaining Momentum Over Time
Maintaining alignment with business dynamics is crucial for a successful data strategy. Regularly re-examining market context, company vision, stakeholder challenges, and tracking metrics can help identify lagging areas and uncover new opportunities. There also needs to be an emphasis on managing a micro-community of cross-functional stakeholders over time, to ensure the data strategy is aligned with business outcomes.
Embracing an outcomes-first approach might be challenging, but for organizations able to do so, the payoff is immense. By engaging closely with the business, focusing on must-haves over nice-to-haves, and continuously recalibrating as conditions change, data leaders can build agile and adaptive data strategies that deliver lasting business impact. The ultimate reward? Data initiatives that tangibly move the needle on strategic success.
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1 Gartner®, “Magic Quadrant™ for Master Data Management Solutions”, Sally Parker, at al, 6 December 2021. GARTNER and MAGIC QUADRANT are registered trademarks and service marks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
