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In a recent Harvard Business Review article, the need for a comprehensive data strategy was confirmed once again.
In a survey of 185 global executives across a wide range of industries, 69% said their organization needs a comprehensive data strategy to meet its goals in the next three years, but only 35% said their analytics and data management capabilities are on track to meet those goals.
Additional challenges include multi-cloud environments, a proliferation of data (sensors, applications, and internet), and the need to manage and govern all this data all while building a scalable and flexible analytics capability. Sometimes that can feel overwhelming.
Having a data strategy is therefore “no unnecessary luxury,” as the Dutch would say.
In its purest form, a data strategy is a plan that describes the best way to use your data, which data to use, what technology you need, and how to organize yourself to get the most value out of this data, given your specific business objectives.
In other words:
A comprehensive data strategy starts by aligning the strategy with business objectives and supports the overall business strategy. Some questions to consider:
What are your strategic priorities?
What business questions are you trying to answer? What KPIs/metrics are you tracking?
How will your organization make effective use of data to support your strategy?
The strategy is made actionable by a data strategy roadmap. You should identify the use cases and/or projects to start with based on business value and effort.
First, identify quick wins. If we’re trying to improve revenue, can we do a quick cost analysis to understand which SKUs are not profitable and which are?
Next, tie these use cases back to the bigger picture. How will these use cases help you address industry trends such as process optimization and customer intimacy? How will these insights be integrated into your business processes?
Then ask which data you need to answer these questions. Is it internal or external? Is it available? Is it of the right quality?
A data strategy is completed by defining the necessary comprehensive change, both organizational and technical, to support the business objectives, use cases, and data needs.
Consider the necessary hardware and software improvements. How do you collect your data? Where will you store it? How do you make data available for consumption? What technology/tooling will you use for that?
What kind of data governance processes will you put in place? How will you ensure the enterprise data is correct and business-relevant? What policies and processes do you enact? What roles and responsibilities do we need?
What skills and capabilities do you need in your organization to become truly data-driven?
How do we guide users to adopt new processes and tools? Your data strategy should include a change management component.
Include an overview of project and program management. How will you ensure an efficient delivery of your data strategy? What framework will we use to ensure quick and iterative development?
Each of these topics deserves a blog post of its own, but for now it suffices to understand that a comprehensive data strategy includes a wide variety of questions that require an answer.
Defining your data strategy is one thing, but successfully implementing it is another thing entirely. As the numbers show, it’s incredibly difficult for organizations to deliver on their strategic data priorities, with only one third of the respondents stating that they are on course to meet their objectives.
What sets apart successful data strategies?
Data strategy success means three things:
It adds value – the data strategy helps you achieve your business objectives
It is tailor-made – the recommendations that make up the data strategy are aligned with the specific context, maturity, and needs of your organization
It works – the recommendations become embedded capabilities within the organization through effective project and change management
The following are key success factors for your strategy:
Executive buy-in and sponsorship. Without senior support, a data strategy will not succeed.
Plan. A solid planning phase ensures the data strategy adheres to business priorities and ensures we correctly establish destination and approach.
Start small, then learn and grow. For a lot of the components in an enterprise data strategy, it’s important to start small. We want to focus on quick wins that are relatively low-effort and high-value. They should be relatively easy to deliver, but demonstrate return on investment quickly. This creates the right momentum within the organization to move forward.
Change management. Implementing your data strategy will significantly impact your employees. Guiding them throughout this change will be essential in achieving long-term success and getting return on investment.
Clear objectives and well-defined metrics. Understanding how we track progress on our data strategy is fundamental to its success.
Solid program management. After the data strategy has been defined, the hard work begins. We’ve seen that solid program management with clear connections to organizational objectives can be a huge differentiator between those that succeed and those that don’t.
These factors provide you with a data strategy that is comprehensive in its scope and aligned to your business objectives, but with highly actionable projects that your organization can immediately start.
Making a strategy actionable is as much about starting as it is about proper program and project management. An important part of our work is therefore defining a data strategy that can be moved forward. The four steps below help define an effective program that can be managed and monitored to ensure you’re on the right track.
In short, these steps help set up a program management organization that tracks the delivery of new capabilities and specific KPIs to understand if you’re delivering on your data strategy. This intricate connection between mission and specific projects ensures that you’re actively delivering what will provide value to the organization.
Each data strategy yields organization-specific benefits. However, there are clear differences between companies that are successful at implementing their enterprise data strategies and those that aren’t. Will yours be successful?
Once you have your goals in mind, it’s time to start getting to know your data better. Check out our Data lifecycle in the AWS e-book to learn more about the five stages of the data lifecycle and which technologies you should implement to optimize your data analytics practice.
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The Keyrus team is excited to announce that we’ve been named one of the top 1000 companies on Clutch’s platform in 2022! This is the second year that Keyrus has been recognized by Clutch as a top company and B2B leader.
In 20 minutes, we’ll teach you how to use Alteryx Intelligence Suite to eliminate common problems and inefficiencies in accessing data from .pdf files. In the past, you’d need to run custom Python and complex parsing logic to get any usable data from a pdf. Now, you can parse PDFs with out-of-the-box features in Alteryx Intelligence Suite.
The cloud offers new opportunities to save you time and money, allowing you to shift focus from maintaining growing servers and upgrading infrastructure to making your data work for you and the success of your business. Watch the webinar and Q&A to learn how AWS, Tableau, and Keyrus worked together to help Red Ventures migrate to a powerful cloud BI tool that created new pathways for success and a modern data culture.
Keyrus & Anaplan Sponsor Life Science Gross-to-Net (GTN) Summit
Keyrus achieved Amazon Web Services (AWS) Data and Analytics Competency. To receive the designation, AWS Partners must possess deep AWS expertise and deliver solutions seamlessly on AWS.
Wednesday, November 9th, 2022 @ 12:00PM Central Time (US and Canada)
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