In this blog, we discuss the reasons you should review and refine your data strategy, who should be involved in making changes, how to actually update your data strategy—and how to ensure it is adopted.

Your data strategy is the foundation to all your data practices across the organization. And as your business strategy, structure, or needs change—so should your data strategy. But when was the last time you looked at your data strategy and thought about updating it? Where do you begin, and how do you actually update it?

It starts by taking a step back and taking a fresh look at your business. Not just the data; but the people, processes, and technology that drive your business decisions—all of which encompasses a data strategy.

Why—and How Often—Should You Reassess Your Data Strategy?  

To maximize the value of your data strategy—and to truly be successful with data and analytics—you should reassess your data strategy both routinely (at least every 6 months) and whenever a significant event occurs, including:

Changes in your business strategy

Any change—intended or not—in your business strategy will likely lead to changes in the kinds of data you collect, the metrics you want to measure, as well as what you should be prioritizing. We’ve identified how some changes to your business strategy can impact your data strategy, including:

  • Organizational restructuring: This will lead to new initiatives and business goals, often requiring the reallocation of resources as well as a review—and possible change—of your existing data architecture stack. You will need to reassess your data strategy to tweak data architecture layers, possibly adding a complementary tool to the existing stack—or for large org changes—potentially re-evaluating the stack completely to modernize it (especially if there were legacy tools the company was not able to change during the creation of the initial data strategy).
  • Changes in company size: Whether you have experienced downsizing or company growth, any utility tools and workarounds that may not have been a high priority before may become more urgent now. You will need revisit your data strategy to ensure there is a sustainable data and analytics infrastructure, data governance, and support for the company’s new size and updated projections.
  • Introduction of new product or service: These kinds of changes require new processes be put in place, as well as new metrics that need to be measured and compared. Updating your data strategy will allow you to meet new requirements but also adjust for the expected growth.Illustration of a modern data architecture that represents all stages of the data lifecycle—this image represents some of the tool options for each phase of the lifecycle including extract and ingest, data storage and replication, data transformation, data warehouse, and data visualization.

A modern data architecture considers all stages of the data life cycle. As you update your data strategy and assess your current data architecture—check to see where you need to address gaps when thinking about your future state.

No matter the reason for your business strategy change, it is critical that your data strategy aligns with it so that you can make informed decisions when they are necessary.

Changes in the marketplace

Many things—inflation, workforce shortages, supply chain issues, etc. — can affect the marketplace, and often these things happen without notice, and quickly. You need data and analytics to understand and to adapt to these changes, and you need to ensure your data strategy reflects that agility.

Progress in data and analytics maturity

It’s no surprise that companies will become more analytically mature after implementing a data strategy. This is great progress, but don’t stop there!

Your original data strategy may have included some exciting optimizations, advanced analytics, or even machine learning/artificial intelligence use cases parked in that ‘future phase backlog’. Chances are that there are even more potential use cases that your business users are better able to contemplate now that they are better enabled with foundational analytics and supporting technologies.

In this stage, business users will have a better understanding of the art of the possible and revisiting requirements and priorities with them can reveal some impactful use cases with big ROI. And the company is now in a better position to achieve these advanced use cases with a solid foundation to leverage.Gartner analytic maturity model with blue boxes for each type of analytics.

The Gartner Analytic Ascendancy Model is a useful way to look at your organization’s analytical maturity and align your data strategy with your company’s goals.

Mergers and acquisitions

Mergers or acquisitions present an obvious need to update your data strategy to incorporate corporate goals from newly acquired organizations, assess new risks, build a plan to integrate data sources and analytics, and create an acquisition playbook to prioritize what should be integrated immediately versus in later phases.

Tracking progress and showcasing success

Revisiting your data strategy is an opportunity to identify, advertise, and celebrate progress made since the last strategy update. Planning for routine data strategy assessments and updating it accordingly will allow you to meet any obstacles—planned or unplanned—more effectively, as well as celebrate wins across the organization.

From small “course adjustments” to complete reinvention of a business, your data strategy should empower everyone at every level to use data and analytics to support your business objectives. As such, it is critical that you routinely update your data strategy to make sure that it aligns with your current business objectives.

Who Uses a Data Strategy and How: Putting Together a Team to Update Your Data Strategy

A data strategy is relevant to more than just executives. Every decision maker benefits from the analytics that result from a data strategy. To maximize the benefit to your company, you need to start by putting together a small data strategy team with representatives from your executive suite, directors and managers, and individual contributors. Here’s why:


Executives oversee making business-wide decisions that will make or break the company—they demand a lot from your data and analytics. An executive’s role in a data strategy effort should be to make sure it is supporting their most important business initiatives. They should not delegate their voice in defining the goals of a data strategy.

Especially in a time of crisis or uncertainty, an executive should be sure to consider their data needs to support their operational decisions (crisis mitigation, capital preservation, efficiency, etc.) and to support innovation (how will we know if a new business idea is having the result we need?)

Thinking about operational and innovation activities separately can help focus and prioritize data needs. You’ll need analytics to support both, but different stakeholders may be involved. An executive will also have an excellent perspective into where and how the work of individual departments intersects to achieve results. Make sure those connections are reflected in your company’s data by having an executive play an active role in refreshing your data strategy.

Directors and Managers

Directors and managers are often in the lead role for developing or updating your company’s data strategy. They are responsible for making sure the data strategy team is on task and hearing from the appropriate stakeholders. They also play the role of an advocate for different departments and teams—looking for common requirements with peers in other departments, especially those needs which cross departmental lines and are likely to result in the highest priority projects to flow from a data strategy effort.

A leader of a department or of a small team will be better equipped to set the culture for that team. They can use a data strategy effort to build a trusting data-driven culture and ensure it is widely adopted.


Contributors are the ones working with customers, suppliers, partners, or other employees to get the business of your company done. They depend on having the right data, tools, and training to make the best decisions on a daily basis.

Too often, individual contributors are spending a disproportionate amount of time collecting and combining data using individual spreadsheets to create reports as opposed to focusing on making progress on foundational goals. They’re input in updating your data strategy is invaluable.

They can expose the difficulties they have using data to make decisions. Additionally, if they have figured out something on their own (maybe they have an incredible spreadsheet that others could use), they can share that with the team. Many of the most useful enterprise-wide analytics came from an innovative employee’s personal project.

Talk with an expert about updating your data strategy

How to Update Your Data Strategy 

Once your data analytics team is formed, here are a few practical steps to kick off a refresh of your data strategy.

White and blue graphic illustrating four steps to updating your data strategy.

Once your data analytics team is formed, here are a few practical steps to kick off a refresh of your data strategy.

1. List out your business initiatives and the data needed to support those initiatives.

Take note of any new data sources you might need to pull from. For example:

  • We’re launching new direct-to-consumer web store → We need to analyze the sales data, web traffic, and customer sentiment on social media.
  • We have an updated diversity and inclusion initiative → We need to improve insights about candidate tracking and employee engagement reporting.
  • We need to better understand our customers → We should consider doing customer surveys and a customer segmentation project.
  • We need to reevaluate our supplier relationships → We should analyze supplier pricing and service level data.

2. Take stock of the specific analytics capabilities you already have.

Talk to each business department about reports/spreadsheets/visualizations they’re using.

  • What is already working really well?
  • What is widely adopted? What isn’t but should be?
  • Who is getting value from your current analytics? Who is not? Who needs to get value from your analytics?
  • What capabilities do you have in-house to do more? Do you have the skills and the time?
  • Is anyone doing really cool analysis on the side that could be mainstreamed?

3. Determine if your data infrastructure will support the kinds of analytics you want to do.

  • Are you on the cloud? If not, consider the potential benefits: new capabilities, opportunities for cost reduction, and performance improvements.
  • Do you have an appropriate relational database at the heart of your analytics capabilities? There are great, cost-effective options for any amount of data you work with. There have been massive improvements in database technology in recent years—make sure you are aware of them.
  • Do you have a good general-purpose business intelligence (BI) platform? And if so, are your people using it? If they aren’t, it’s likely because it is poorly deployed, not because the tool isn’t capable. You may need a new tool, but before you assume you do, take stock of what you actually have and what your business needs are. Don’t shy away from spending some money here if you truly need to, but don’t just assume that the first thing you need is a shiny new tool.
  • Can you easily incorporate new data sources without straining your infrastructure? Will data ingestion tools you use today be cost-effective with the volume and frequency of data required for new analytics initiatives? If you have the right infrastructure in place (especially if you are deployed in the cloud), you can better handle integrating data from anywhere without capacity issues, long query times, or manual efforts.
  • Can you reliably and consistently move, replicate, and cleanse your data? This starts with an ETL/ELT tool. There are many options for this technology—Python is one of our favorites and it’s free.
  • Will data science or machine learning help you make better decisions and innovate your business? Don’t jump in until you identify what business problem or use case you want to solve, and then figure out the data, technology, and infrastructure you need to get there.

4. Reassess if you have the people and process in place to support your data strategy.

Remember, being data driven requires more than just technology—you need people and processes in place as well.

  • Do your business users have the skillsets needed to carry out the objectives of your data strategy, or will they need to be trained on new technology?
  • Do you need to hire more people? Are managed services an option you’d consider?

Ensure the Data Strategy is Driven by The Company’s Current Business Goals

A data strategy alone can’t affect change. You need to get buy-in and support from everyone in the company. Measuring and communicating the impact of your data strategy over time will increase support for data and analytics initiatives; foster adoption and promote a data-driven culture; and uncover ways to improve the data strategy going forward.

Here are some tips on how to ensure your data strategy is adopted across the organization—and driven by your current business goals.

1. Show how data and analytics support and advance your company’s mission and vision.

Start by revisiting the driving business reasons and the company’s mission and vision when the data strategy was drafted and highlight the data and analytics initiatives that contributed to progress in these areas. Demonstrating this will not only showcase the importance of your data and analytics efforts, but it will also further promote support for your efforts across the organization. A good opportunity to do this is during routine reviews of your data strategy.

2. Celebrate successes and wins and promote a data-driven culture.

Reinforce everyone’s role in the company’s success and highlight the ROI of their contribution by digging deeper than just metrics about ‘hours saved’ per week or quarter due to automation. Start by interviewing business users to find out how they were better able to allocate their time to analysis with the freed-up time and better quality, timely data, and then document and share the business decisions or actions their new analysis contributed to and how that ultimately impacted the company.

Celebrate increased user adoption—the percent of users who moved from spreadsheets only to relying on governed data and analytics tools. Share specific stories of business users leveraging analytics for positive outcomes. Examples could be something like a customer service rep who was able to make better recommendations to a customer on a call because they were able to see their full history in one spot, or a successful marketing campaign that was designed based on recent trends in customer data.

3. Communicate, communicate, communicate: Keep improving your data strategy.

Your data strategy is a living document, and your business users should know that. Communicating with them how and why it changes is critical to ensuring it is successful. Create a communication plan that lays out who should be informed, when, and by what methods. Consider which metrics and success stories you need to share, as well as changes to existing content or processes, updates such as new content available, upcoming initiatives, and educational content including data literacy. Be selective about the metrics you choose to track.

The most effective and influential approach will include a few focused KPIs showing meaningful information about the data strategy’s progress and business impact paired with showcasing specific success stories.

Now is the Time to Update Your Data Strategy

Circumstances change rapidly, and your ability to remain agile relies on a well-defined and widely adopted data strategy. When we help our clients with their data strategy, we never presume to know the details of what they might need to do next. Rather, the focus is laying the foundation with a strong data infrastructure so that no matter what changes occur, they’re equipped to make informed decisions with the right data.

Christina Salmi Christina leads the Data Strategy Service Line, helping our customers to think and act strategically about data and analytics.

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