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 elements of a data strategy that will help you make decisions based on data analysis rather than intuition.

Companies know their data is a strategic asset and they want to use it to make smarter decisions; but the problem is—it’s complicated. Often data is scattered in silos, stuck in departmental systems that don’t talk well with one another, the quality of the data is poor, and the associated costs are high. In conjunction with responding to market pressures, most companies are going to prioritize the urgent, tactical, day-to-day needs over the long-term strategic initiatives.

What Is a Data Strategy?

Moving toward a more data-driven culture is certainly attainable, and it starts with a data strategy.  A data strategy is the foundation to all of your data practices. It’s not a patch job for your data problems. It’s a long-term, guiding plan that defines the people, processes, and technology to put in place to solve your data challenges and support your business goals.

A data strategy is often viewed as a technical exercise, but a modern and comprehensive data strategy addresses more than the data; it is a roadmap that defines people, process, and technology. The exercise of creating a data strategy is one in which organization leaders take a deliberate look at:

  • What employees need so that they are empowered to use the data.
  • Processes that ensure data is accessible and of high quality.
  • Technology that will enable the storage, sharing, and analysis of data.

iFit modernized its data and analytics environment and experienced triple digit growth. Learn how a data strategy set the wheels in motion.

Become More Analytically Mature

A data strategy should lay out a detailed plan to mature in analytic capabilities and transition from making decisions based on hindsight to making decisions with foresight. As seen in the Gartner Analytic Ascendancy Model, the goal should be to move from using descriptive analytics (“what happened?”) to prescriptive analytics (“how can we make it happen?”).

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. It describes four different kinds of analytics and how they can be used to understand data.

Our 7 Elements of a Data Strategy

Let’s talk about how to get started with your own data strategy. We’ve helped dozens of companies with varying levels of analytical maturity and technical needs craft their data strategy. Each of the following elements have been identified through this experience and can be applied to any organization looking to get ahead with their data.

1. Business Requirements

Data must address specific business needs in order to achieve strategic goals and generate real value. The first step of defining the business requirements is to identify a champion, all stakeholders, and SMEs in the organization. The champion of the data strategy is the executive leader who will rally support for the investment. Stakeholders and other SMEs will represent specific departments or functions within the company.

Next is to define the strategic goals and tie department activities to organization goals. It’s natural for goals to exist at the company and department level, but the stated goals for both levels should sync up. These objectives are most effectively gathered through an interview process that starts at the executive level and continues down to department’s leaders. Through this process, we’ll discover what leaders are trying to measure, what they are trying to improve, questions they want answered, and ultimately, the KPIs to answer those questions.

By starting with gathering and documenting the business requirements, we overcome the first roadblock to many IT or technical projects: knowledge of what the business is trying to accomplish.

2. Sourcing and Gathering Data

With a good understanding of what questions the business is asking, we can turn to the next element: analyzing data sources, how that data is gathered, and where the data exists. It’s unlikely that all data will be available within the organization and that it already exists in a place that’s accessible. So, we need to work backward to find the source.

For data that can be found in-house, we note the source system and any roadblocks to getting access to that data. We also need to determine whether the data has the right level of detail and is updated with the right frequency to answer the question effectively. For example, is the data private (especially in light of GDPR and CCPA)? Is it guarded by restrictions brought on by software licensing?

For data that isn’t available, we’ll note that here and pick it up in the next step. For example, a retail company may want to know how a brand is perceived before and after a big product launch. Let’s say the retailer has call center data, traffic counts for stores and online, and overall sales and return numbers. That doesn’t measure how customers are feeling or what they’re saying about the brand. The retailer may choose to pull in social media data to measure sentiment. Call center data, traffic, and sales would be noted in this step, along with the opportunity to pull in social media data.

For one of our clients, we built a matrix that was slightly more complex than the following sample. We identified their business questions, understood the data needed to answer the question, and mapped each to a source system.

Blue and grey table showing business questions and what kind of data and data source is needed to answer them.

Walk through questions that need answers, document required data, and identify gaps in a source matrix when developing a data strategy.

3. Technology Infrastructure Requirements

Our first piece of advice is: Don’t get caught up in the hype and latest technologies; focus on the business reasons for your initiatives. Building a flexible and scalable data architecture is a complex topic for which there are many options and approaches, so here are some important things to consider:

  • To what extent can an operational system support analytics needs? Likely very little. It’s generally not best practice to rely on an operational system to meet analytical needs, which means a central data repository would be useful.
  • Does the organization have the skills and technical infrastructure to support a data warehouse on-prem, or would leveraging a cloud-based solution make more sense?
  • Where the data doesn’t exist today, how will the gaps be filled? Can the data be calculated or estimated? Can it be purchased from third-party market trend data or macro-economic data? Or can a new source system be implemented to generate the data?
  • Is there a standard integration tool to get the data from source systems into the central repository? Will this layer of the architecture be leveraged for business logic so that the data is ready to be used?
  • How will you provide or provision access to the data? Will IT create reports, or will you enable self-service? Will the reports be pixel perfect (printable) reports, or will the reports allow user interaction with the data? Will they be embedded into websites and provided to people outside the network?

All of these considerations will go into an overall architecture and data management plan. And, as with most designs, the more your requirements and future needs are taken into account, the more the solution will actually support the business.

Here is a conceptual diagram of the different types of analysis and storage that need to be captured through either technology or process. An architecture diagram will identify and represent all the source systems, the methods to ingest data and the landing spots for that data (data marts, data lakes, and data warehouses). It will also layer in processes like data governance and information security.

Diagram representing conceptual data architecture and the relationships between source systems, ingestions tools, data storage, transformation tools, and BI platforms.

A modern data architecture represents all stages of the data lifecycle, from original data source to reporting—all the way through to analytics. These are key components to understand when creating a data strategy.

4. Turning Data into Insights

A data strategy should provide recommendations for how to apply analytics to extract business-critical insights, and data visualization is key. Many companies still rely on Excel, email, or a legacy BI tool that doesn’t allow interaction with the data. Often a tedious, manual process is required, and relying on IT to create reports creates a bottleneck.

Data visualization tools should make the data look good, but more importantly, make the data easier to understand and interpret. Some factors that should be considered when choosing a data visualization tool include:

  • Visualizations: You should be able to quickly spot trends and outliers and avoid introducing confusion via bad presentation.
  • Story Telling: The dashboard should present the context of metrics and anticipate the user path of investigation and diagnosis.
  • Democratization of Data: Who has access to what data? Encourage sharing and wide-spread adoption and define common definitions and metrics across the organization.
  • Data Granularity: Be able to provide right level of detail for the right audience. An analyst may need more detailed information than an executive, and some people may need drill-down capabilities.

Download our eBook: 5 Principles for Better Communication with Data Visualizations.

5. People and Processes

As we’ve stated, becoming data driven requires more than just technology. In this stage we look at the people in the organization and the processes related to creating, sharing, and governing data. A data strategy is likely going to introduce more data and data analysis and maybe new tools. Based on this, it makes sense to look at the skillsets of the users to understand their strengths and where they’ll need support. Do they need data and analytics training? Do you need to hire more people? Organizational structure should also be assessed—should analysts be aligned to a business unit or to IT? And how IT will 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.

When employees are handed new tools but not shown how to think differently about their jobs, the end result is unlikely going to change. Here’s an example of a training plan we created for a customer that includes every level of the organization.

Table representing training plan for business function responsible for different elements of data and analytics practices.

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

When it comes to process, many organizations have unintentional roadblocks to utilizing their data in decision making. Business processes may need to be re-engineered to incorporate data analysis. This can be achieved by documenting the steps in a process and where specific reports are leveraged for a decision. We can also mandate that specific data be provided as rationale for a business decision. Recognition can also go a long way—when you gain a win that is based on new use of data, it should be celebrated and promoted to build internal momentum and encourage positive behaviors with data.

6. Data Governance

Data governance is what ultimately allows enterprise level sharing of data and the oil that lubricates the machinery of an analytics practice.

A data governance program will ensure that:

  • Calculations used across the enterprise are determined based on input from across the enterprise.
  • The right people have access to the right data.
  • Data lineage (where did the data originate and how was it transformed since that origination) is defined.

We don’t look to a tool to solve data governance; it’s people work, and it has to happen. Data governance takes leadership and sometimes navigating through difficult conversations.

Developing a data dictionary is a good place to start. A data dictionary 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.

Read more about how to start your data governance program.  

7. The Roadmap

The data strategy roadmap is the culmination of all the work we’ve done to this point and what makes all our previous work actionable. We’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.

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.

The roadmap should also contain a timeline that allows for celebration of incremental wins that are earned along the way.

Blue diagram representative of data strategy roadmap timeline.

Your data strategy roadmap should contain a timeline that allows for celebration of incremental wins earned along the way.

Get Started with Your Data Strategy

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, remember the seven key elements defined in this blog—business requirements, sourcing and gathering data, technology infrastructure requirements, turning data into insights, people and processes, data governance, and a roadmap—are all critical pieces to the puzzle as you look to overcome data challenges and support your business goals. Are you ready to get started with your data strategy—sign up for a data strategy session with one of our experts.

Dave Williams Dave is our Managing Director of Customer Success. He has a passion for questioning the status quo, helping our clients make smart decisions, and building business-focused solutions. He is the father of 3 young adults and enjoys exploring the mountains near his new hometown outside Salt Lake City.
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