While most companies recognize that their data is a strategic asset, many are not taking full advantage of it to get ahead. In this blog, we discuss the key elements of a successful data strategy that will help you make informed decisions based on data analysis rather than intuition.

Even as companies make larger investments in data and analytics initiatives than ever before, age-old obstacles like siloed and untrustworthy data, inefficient data management practices, and a lack of meaningful insights continue to get in the way of unlocking your data’s potential.

A good data strategy framework is proven to help companies overcome those obstacles and define the path to become more data driven.

In this blog, we discuss the key components of a data strategy, including:

What is a Data Strategy? (And What it’s Not)

A data strategy is the foundation to all your data practices. It’s not a patch job for your data problems, and it addresses more than just data — it’s a long-term, guiding plan that defines the people, processes, and technology necessary to solve your data challenges and support your business goals.

Creating a successful data strategy requires business leaders to take a deliberate — and objective — look at the business through the lens of data and anticipate what needs to happen to bring about specific objectives the company has defined. Business leaders should consider:

  • What employees need so that they are empowered to — and more effectively — use the data.
  • The processes required to ensure enterprise data is both accessible and of high quality.
  • The technology that will enable the storage, sharing, and analysis of the company’s data.

The goal of a data strategy is to answer the question of how the entire organization can leverage data in support of making business decisions, and to build a plan that weaves the role of people, processes, and technology to make the plan a reality.

Why is a Data Strategy Important?

It’s not enough to just have data — you need a strategy in place to realize your data’s value and to bring to bear meaningful outcomes aligned with your business goals. A data strategy enables your organization to be innovative, business users to be effective, and the business to be competitive. Without a data strategy in place, you can encounter common data challenges including:

  • Inability to make timely, data-driven decisions ​
  • Reporting on the past and not anticipating and preparing for the future​
  • Overlooking or misusing advanced approaches like generative AI
  • Low user adoption​ of technology
  • Being locked into a single vendor for various parts of the data lifecycle​
  • Inconsistent, poorly defined, or undocumented definitions for metrics and KPIs​
  • Data stuck in silos and departments working from different “truths”
  • Manually integrating data from disparate data sources​
  • Spending too much time preparing raw data
  • Data quality and data access issues​
  • Users too dependent on IT​

A data strategy framework serves as the foundation for all your data initiatives and allows your organization to remain agile under pressure.

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Our 7 Elements of a Data Strategy

We’ve helped hundreds of organizations with varying levels of analytical maturity and technical needs craft their data strategy and make better use of data. Our extensive experience has resulted in identifying the following key components of a data strategy.

1. Alignment with Business Strategy

Data initiatives must address specific business needs to generate real value — otherwise, you risk prioritizing the wrong projects, missed insights, wasted time and resources, and even loss of interest and faith in data initiatives throughout the organization.

Tying your data strategy to your business strategy sets you up for success. When your data initiatives support company goals, you get business buy-in — which means more prioritization of data activities — and the whole organization wins.

Here are ways to align your data strategy with your business strategy:

  • Identify relevant business drivers — big or small — that could be positively impacted by data and analytics.
  • Dig in to understand departmental activities and how they sync up with business goals.
  • Complete an interview process that starts at the executive level and continues down to department leaders. Guide the conversations to uncover what they are trying to accomplish, as well as what their day-to-day looks like and how it can be improved.
  • Note what they are trying to measure, questions they want answered, and ultimately, the KPIs to answer those questions.
  • Compare your findings to industry standards and note how your organization’s data is serving each business driver and which areas are missing out on data insights.

With this information documented, you can begin to build a log of use cases that will be included in your data strategy roadmap (see Tip #6!).

Now is the time to get business buy-in. No data strategy will succeed without executive support. Demonstrate how data supports their goals. Identify a business champion, important stakeholders, and subject matter experts who represent specific departments or functions.

2. Analytics and Data Maturity Evaluation

You need to know your starting point — your current analytics maturity level — before outlining your desired future state. This helps you set attainable goals and take realistic, incremental steps to be more data driven and utilize advanced methodologies like generative AI to transform your business.

According to Gartner, modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

To get a full picture of your analytics and data maturity, you need:

  • An inventory of the tools, technologies, and systems you use today.
  • A comprehensive overview of your data infrastructure, as well as your existing data architecture.
  • An assessment of people skills and organizational processes related to data and analytics.
Five light blue boxes and categories on the Analytics8 maturity model: chaotic, reactive, defined, managed and optimized. Below each category includes different components: analytics, management processes, KPIs, confidence in data and employee capabilities.

We use the Analytics8 Maturity Model to define where our clients are today and what it would take to move them forward on the scale.

With an understanding of your current state, you can identify where you have gaps, where there are known issues, and what you need to optimize — whether it be technology, processes, people, or all — to meet business objectives across the organization.

Your data and analytics maturity level is a tool to prioritize your projects and serves as a benchmark to measure progress as you increase capabilities and perform tasks from your data strategy.

3. Data Architecture and Technology

It’s easy to get caught up in the hype and latest technologies and have the inclination to want to choose the “newest” tool in the market. It’s also easy to get overwhelmed by the ever-growing number of choices and decide to stick with what you have or take a single-vendor approach.

There are effective ways to cut through the noise of the market and choose technology that works best for your situation:

  • Focus on how modern tools enable your people to be more data driven (i.e., avoid the mindset of modernizing to modernize). Think about the relevance, accessibility, and performance of the technology.
    • Relevance: Who will be using the tech, and will it meet their needs? Technology should organize and present data in a meaningful way for business users.
    • Accessibility: There are so many obstacles that departments and business users face when accessing data. Consider a tool that enables everyone across the organization to make data-driven decisions.
    • Performance: There are powerful technologies on the market that speed the data transformation process. Consider tools that will enable business users to be proactive and not reactive.
  • Use established methodologies and proven tech combos.
    • Instead of identifying a universal best-in-class approach, we use a customized tool selection based on maturity level, data types, size and velocity, as well as team size and structure with our clients.
    • There are proven data architectures and technology combinations that are known to work very well together (for example, BigQuery, dbt, and Looker and databricks and AWS/GCP/Azure). Understand what works well with the tools you have or plan to adopt.
    • Address technology for every stage of the data lifecycle. Data goes through a lot to make it analytics-ready — ensure each stage has the right technology and processes in place to maintain data integrity and produce the most value.
Graphic displaying the stages of the data lifecycle including data extraction, data replication, data ingestion, data storage, data integration, data transformation, data cleansing, data augmentation, data validation, and data presentation.

Stages of the data lifecycle.

When choosing your tools and technology, remember that they are not standalone components, but rather integrated parts of your data architecture.

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 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.

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4. The Data Analytics Team

Becoming a data-driven organization requires more than just technology — you need the right people in the right roles to ensure technology and processes are adopted and that business objectives are being met.

The first step of building an effective data analytics team is to choose or identify your operating model. Your operating model dictates the team structure and roles necessary for you to meet your goals.

There are three types of operating models an organization can subscribe to: decentralized, centralized, and hybrid. One isn’t any better than the other; the decision comes down to the size and resources of your organization and its current and future data needs.

  • Decentralized operating model distributes responsibilities across different lines of business and IT leading to a collaborative approach to things like data management, data strategy, and business intelligence.
  • Centralized operating model is more structured with everything falling under the responsibility of a specific executive function. This allows for easier data governance and improved decision-making.
  • Hybrid operating model combines decentralized and centralized models with one central authority for data management and decentralized business unit groups across the organization. This model allows for consistent data management and data governance and freedom for each line of business to take charge of their data and analytics initiatives.

Then, you should assess the skillsets of your team. Start with understanding your staff’s strengths and where they’ll need support.

  • If you’re adopting new technology, including innovations like generative AI, or updating your architecture, data modeling, or development methodology standards, does your staff need training?
  • What level of data literacy does everyone have?
  • Do you need to hire more people?

This assessment should also be tied to your operating model — should data analysts be aligned to a business unit or to IT? And how will IT support the business in their analytics needs? Even topics like employee reviews and incentive plans should be evaluated. After all, these levers can be used to encourage employees to use data in the way the organization is intending.

4x6 box representing skills gaps in your data analytics team. First row states: Upper Management, Line of Business Subject Matter Expert, IT Analyst. Rows 2-6 are marked with X symbols aligned to different data and analytics practices/responsibilities on the left.

Focusing on your business users’ skill levels in various tools and analytics competencies is a critical consideration for your data strategy. Create a training plan that will address gaps.

5. Data Governance

Data governance is what ultimately leads to high quality data and allows enterprise-level sharing of data across the business.

While data governance is very important to your data strategy, it’s important to understand that just like your level of data and analytics maturity is unique to your organization, so is your need for data governance.

The A8 Golden Rules of Data Governance

  1. Practical, maintainable, and proportional
  2. Data governance is a journey, not a destination
  3. Data governance is not a software application or widget

Although there are some great tools on the market to support the effective application of governance, data governance itself isn’t a tool or platform you can purchase, and there isn’t one way to approach it. Implementing data governance carries a high risk of low adoption if not done correctly and can get costly in a hurry. To avoid this, the data governance program you outline should account for your company’s needs, size, urgency, maturity, and capabilities.

User adoption of your data and analytics happens when data governance is realistic and something that blends into your everyday operations.

A good place to start with data governance

Data governance takes leadership and sometimes navigating through difficult conversations. Developing a business glossary is a good place to start. A business glossary is a living document in which all available end-user measures and dimensions are formally defined. During these conversations, misunderstandings about terms are identified and corrected.

6. Data Strategy Roadmap

The data strategy roadmap is the culmination of all the work you’ve done to this point and what makes all your previous work actionable. You’ve identified all that needs to happen to bring you from where you are to where you’d like to go, but before getting started with any design, build, training, or re-engineering of a business process, it’s critical to prioritize the activities.

For each recommendation that will help bridge the gap from current state to the future state, define the feasibility and expected business value it will provide. The plan should prioritize activities that are easiest to implement but also provide quick wins to the business.

Graphic illustrates a Prioritization Matrix used during a Data Strategy Assessment to the identify high feasibility/high value projects that should kick off data and analytics initiatives.

Using a Prioritization Matrix, each planned project is scored and plotted based on its business value and technical feasibility.

Other factors to include in the data strategy roadmap are:

  • Staff availability and whether outside help is required
  • A company’s budgeting process, specifically if a capital investment is required
  • Competing projects that might prevent the right resources from participating
  • Major company milestones, such as expected new product releases or planned mergers and acquisitions

Having a timeline in your roadmap that allows for celebration of incremental wins that are earned along the way will help keep your team motivated and morale high.

Various shade of blue boxed data strategy roadmap progresses from Q1 to Q4 (left to right) with text boxes underneath each quarter stating components of building a data strategy. Four use cases to the left of the roadmap state: customer 360 analytics, sales forecasting analytics, inventory efficiency analytics, freight and logistics analytics.

No roadmap is complete without a thoughtful timeline to execute all planned activities in your data strategy.

7. Culture Change and Adoption

You’ve successfully created your data strategy. Equipped with a roadmap, you are ready to proceed with data initiatives.

Last, but not least, is addressing change management, because your teams will be dealing with a lot of change and maybe new responsibilities and expectations. Without a culture change, your data strategy efforts will not see their fullest potential.

Consider training and enablement, budget support, and communication in order to promote a data-driven culture, increased adoption, and improved decision-making.

  • Training and Enablement: Having done the hard work of assessing your staff’s skillsets and addressing the gaps, now you need a plan in place to equip them with the knowledge they need to be successful and productive.Think about providing appropriate orientation and training around data literacy, new technology, best practices, and institutional knowledge.
  • Budget Support: Creating a data strategy isn’t a one and done process. You need to consider ongoing budget support for all the items in your roadmap and unforeseen changes.To maintain financial support, it is critical to measure and highlight very specifically how your data strategy has helped your organization meet business. Dig deeper than providing metrics around ‘hours saved’ per week due to automation and discuss how that time was spent to be productive and add value. Lean on your champions and stakeholders to support and vouch for your ROI messaging.
  • Communication: A lack of communication around why and when changes to a data strategy occur can be detrimental to your data initiatives. You need a communication plan in place that details who should be informed, when, and by what methods.Consider things like changes to processes or technology, which metrics need to be discussed, upcoming initiatives, as well as educational content like data literacy. Stay consistent with your message, show meaningful stats on progress and business impact, and celebrate wins — small or big. Executive support in communication will make a big impact.

Get Started with Your Data Strategy Framework

A data strategy is the foundation for all your data and analytics needs — especially as your organization looks to become more analytically mature. It is not focused on a short-term project, but rather a long-term plan that takes a holistic look at people, processes, and technology.

As you develop your data strategy framework, remember the seven key elements defined in this blog — alignment with business strategy, analytics and data maturity evaluation, data architecture and technology, data analytics team, data governance, data strategy roadmap, and culture change and management — are all critical pieces to the puzzle as you look to overcome data challenges, improve decision-making, and support your business needs.


Get In Touch With a Data Expert Today

For More in Our Data Strategy Series

Start Your Data Strategy with an Assessment [Template]

Plan Out Your Data Strategy Roadmap in 5 Steps

Client Success: From Assessment to Data Strategy Roadmap

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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