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.

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?

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?

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.

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.

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.

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.

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