What are the challenges of building a data strategy framework?

26 May 2022

What are the challenges of building a data strategy framework?

Putting in place your data strategy is one of the most important aspects of any data-driven enterprise. It provides a blueprint to guide enterprises in their use of data, analytics, technology and people to help reach their short and long-term goals.

However, while a well-planned data strategy can provide the necessary foundations to enable strategic precision and intelligence, creating one can be challenging to both SMEs and global enterprises alike.

Gaining the critical buy-in from key stakeholders and investors, and encouraging wider data-literacy across the business to help overcome the challenges of creating a data strategy, can provide a wealth of additional benefits. Learn more about some of these core challenges, and how to overcome them, below.

Challenge #1: Overcoming the investment leap of faith

Creating a bespoke data strategy, and then adhering to it for the long-term, can require significant investment.

With potentially costly projects arising from the blueprint, and an intensive time period required to facilitate completion, it may be difficult to gain the necessary buy-in from stakeholders.

However, the cost and time needed to complete projects to reach strategic goals aren’t the only investment required. Commonly, we encounter users that are still committed to traditional legacy processes – reluctant to convert to modern data platforms.

Investing in your data strategy doesn’t just mean enhancing and optimising architecture. It can also involve the persuasion of these users to migrate to contemporary alternatives.

Navigating this challenge often involves educating both users and stakeholders on the tangible benefits of completing and enforcing a company-spanning data strategy.

For investors, this may include highlighting the greater level of efficiency, and the ability to accurately pursue strategic business decisions that bring greater profits.

For users committed to legacy solutions, educating them on the benefits of removing complexity, centralising results, and streamlining time-consuming tasks can help gain their support in adoption.

Learn how we approached this challenge first-hand as we created and enabled a data-driven culture in our case study with Inspired Villages.

Challenge #2: Creating strategy with structure

A unique and fully-tailored data strategy should be just that – unique to your enterprise.

If your data strategy doesn’t take into account your complete architecture, it may lack consideration of key components needed to take full advantage of your data.

What’s more, with key information missing, your data strategy may suggest a less than optimal route forward, leading to the implementation of costly projects that may not be needed. Not only does this waste financial resources and valuable time, but it can also deteriorate the trust of stakeholders in future projects.

We always start any project by conducting an initial evaluation of current architecture and processes. This ensures that any data strategy created or modified takes full advantage of all assets to inform a more intelligent, high-level, and value-led approach. Taking this approach can mitigate risks, and avoid unnecessary disruption later.

Challenge #3: Difficulty in estimating scalability

Any comprehensive data strategy can provide an informed blueprint based on current data sources.

However, does your strategy anticipate the natural growth of data, and account for the integration of new data sources?

A scalable data strategy will assess increasing data volumes, consider the expansion of a business and its processes, and provide a solution for the impact of increasing and more complex workloads.

Without navigating this challenge, your BI platforms and processes will suffer from bloat, turning rapid analysis into time-consuming ordeals.

What’s more, deteriorating efficiency may turn your users back to manual processes – a step that threatens to reintroduce harmful data silos and destabilise collaborative and centralised efforts.

Anticipating this natural evolution, and putting platforms such as a scalable cloud-based data lake or warehouse, can help overcome this challenge and solidify trusted intelligence.

Learn more about the dangers of reverting to manual processes, and how it affects a complete enterprise, in our blog on missing data here.

Challenge #4: Maintaining a long-term perspective

Your data strategy has been created with critical buy-in from investors – and data-literate users have been converted from traditional legacy processes to streamlined and agile platforms.

The next challenge remains to be seen – ensuring that the strategy is adhered to throughout the long-term. It can often be tempting to steer away from your data strategy, especially when short-term benefits are slow to deliver.

However, it’s important to recognise that with the correct data strategy in place, both short and long-term goals will be catered to. Implementing new types of data platforms or processes can bring it extra complexity, and in the some cases, it may unravel months of dedicated project work.

This isn’t to say that new platforms and tools can’t be implemented if they are shown to provide measurable benefits to your enterprise. Small, focussed deliverables – that build towards strategic objectives allow for agility and the introduction of changes that benefit the strategy. However, changes must be carefully evaluated to ensure that they don’t harm your strategy’s long-term approach.

  • What benefit does this new tool or platform bring to your enterprise?
  • Will introducing this new aspect influence current architecture?
  • Does this tool threaten a centralised approach to data analysis?
  • What will happen to architecture if this tool malfunctions?
  • Does the tool offer a competitive advantage?

Answering these questions can help ensure that your data strategy stays aligned with business needs and adds value to the benefit of the strategy. Technologies move forward, and disruption is good when managed in the right way.

Deploying data that drives decisions

At DataShapa, we’re fully committed to enabling companies to realise the full potential of their data. From integrating Machine Learning capabilities to introducing the core foundations needed, we work alongside our partners for an agile and value-led approach using a tried and tested methodology.

Designing and building a bespoke data strategy for our partners, we’re passionate about architecture that’s always delivering the greatest intelligence possible while helping enterprises navigate commonly associated serious and complex pitfalls.

To learn more about how we’ve enabled trusted intelligence and enhanced insights, and how we can provide market-leading services to your enterprise, in our case studies page here.

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