How do we get there? Implementing a data strategy

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⸻ Beyond Data

Turning your data strategy ambitions into an actionable plan you can execute and measure

This article is part of a series, The Four Question Data Strategy Framework if you’ve only just joined us you can find the rest in the following articles here:

Step 1 – Where Are We Now?

Step – 2 Where Do We Want to Go?

Welcome to Step 3 – How Do We Get There?

Implementing a data strategy can be a daunting task. In this article, I’m going to share the method I use to turn the understanding we’ve developed in the first two phases of the Four Question Data Strategy Framework, and turn that into an executable plan. We’ll discuss the three categories of data initiatives and how to approach each to ensure the successful implementation of your data strategy.

The three categories: Core, Complex, Commodity

By this point we should have a good list of potential outcomes the organisation wants to achieve. These will always require a mix of people, cultural, data, and technical challenges to overcome.

It’s important to make the distinction between software/infrastructure/technology and data initiatives (more on that in a future post). Replacing your whole CRM system to get better data touches on much more than a data strategy, for example.

But when you’ve got a good idea of what is in the remit of the data team and what the organisation wants to achieve I often feel it’s a good point to take a step back and categorise them into three buckets.

The three categories of data initiatives I use are core, complex, and commodity. Each category plays a crucial role in developing a comprehensive data strategy that can help drive business growth and success.

You can broadly find them with the following questions:

  • Does this deliver competitive advantage or differentiation?
  • Can we build it?

These three categorisations align well with your Buy Build Partner Strategy.

Core – initiatives that are critical differentiators to your business

The core initiatives are the key differentiators for your business and are often the most difficult to implement. In the hype around LLMs, ChatGPT, and AI just now – this is getting referred to a lot as “the moat”.

These initiatives require a deeper understanding of your business and the data that can help drive business goals. Place into this category, initiatives such as:

  • developing customer segmentation
  • predicting customer behaviour
  • analysing proprietary data other organisations can’t get
  • building bespoke data sources that will give you an edge over the competition.

For example, a company that offers online shopping services may use customer segmentation to group customers based on their shopping habits and preferences. This information can help the company tailor its marketing campaigns to each group, leading to higher engagement and sales – a key advantage that’s impossible for competitors to replicate without the same data.

Complex – data projects that your industry will find challenging to achieve

Complex initiatives require high levels of expertise and are typically difficult to achieve in a particular industry. For example, these initiatives can include:

  • predictive maintenance for machinery
  • detecting fraud in financial transactions
  • new tools and architectures your organisation is unfamiliar with

Successfully achieving complex initiatives can create significant market advantages and often require a strategic partnership with a vendor or tech provider.

For instance, a manufacturing company may use predictive maintenance to reduce downtime and improve machine efficiency. By analysing data from sensors on the machines, the company can predict when maintenance is needed and schedule it before a breakdown occurs. This can save the company significant costs associated with machine downtime and repairs.

Commodity – low value, must-have data capabilities

The commodity initiatives are low in value but essential for business operations. Every company must have these capabilities, including data warehousing, reporting, and data governance. It is essential to ensure that all data is clean, validated, and appropriately governed to support the other data strategy initiatives.

With that in mind, do you want to write your own data warehousing tool from scratch?

With these, take a look at what’s on the shelf and if there’s nothing that fits, find a local friendly partner to farm this out to quickly or use it as a learning exercise for underutilised talent.

Forming a plan: How these fit together

Identifying the categories of initiatives is just the beginning. Formulating a plan that aligns with business goals and objectives is critical to a successful data strategy.

When creating a data strategy, it is essential to consider the business’s long-term goals. This means taking into account not only current needs but also future objectives. As a result, a well-crafted data strategy can help a business stay ahead of the competition, make better decisions, and improve overall performance.

Core initiatives should be prioritised based on business impact and feasibility. By focusing on initiatives with the most significant potential to impact the business, a company can ensure that its resources are used effectively. However, feasibility should also be taken into account. Ensuring that initiatives can be realistically achieved within the given timeframe and with the available resources is vital.

Complex initiatives may require a more considerable investment due to the expertise needed to achieve them. These initiatives may involve new technologies or processes that require specialised knowledge. Investing in the necessary expertise can pay off in the long run by allowing a business to take advantage of new opportunities and stay at the forefront of its industry.

Commodity initiatives may appear straightforward, but ensuring their proper implementation can still pose challenges. For example, these initiatives may involve data cleansing or standardisation tasks. While these tasks may seem simple, they can be time-consuming and require attention to detail to ensure that they are done correctly.

One way to approach this is to create a roadmap that outlines specific initiatives, dependencies, and their sequencing. This roadmap can prioritise initiatives based on business goals, identify quick wins, and help manage costs and resources. In addition, by organising the data strategy initiatives into a roadmap, the entire organisation can clearly understand the initiatives and the expected outcomes of each.

It is essential to regularly review and update the data strategy roadmap to ensure it remains aligned with the business goals and objectives. This can help a business stay agile and responsive to changing market conditions and customer needs. This should be a living document that someone owns!

Building your data culture

Achieving a data strategy is more than just tech implementation; it requires an organisation to embrace data and analytics across the board. Creating a culture that values and integrates data into decision-making processes is key to a successful data strategy. A data-driven culture can provide insights into new business opportunities, reduce costs, and minimise risks.

Building and growing your data culture in line with your strategy is essential for success. How to do that is a topic for another post.

Final thoughts

Implementing a successful data strategy can seem daunting for any organisation. Core initiatives that offer the most significant impact require a deeper understanding of your business and the initiative’s feasibility. Complex initiatives require more significant investment and typically involve a strategic partnership with a third party. Commodity projects, while low value, are essential for all round success.

Get started and find out where each of your lofty ambitions falls – and be sure to reach out if you get stuck.

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