Data strategy and insight: realising long-term goals - DataShapa
Data strategy and insight: realising long-term goals

11 May 2022

Data strategy and insight: realising long-term goals

The full potential of Business Intelligence for delivering data-driven insights continues to be realised across global industries, as more and more enterprises seek to integrate a data-driven culture in order to: 

  • Optimise a wide range of business processes 
  • Elevate strategic decisions 
  • Gain access to previously unavailable trends  

However, many are left wondering how to begin. 

It’s tempting for enterprises to implement advanced solutions such as Machine Learning (ML) and other Business Intelligence (BI) tools as soon as possible. However, doing so without proper consideration can be detrimental to your data, results, and overall business operations.  

The solution: create a detailed data strategy relevant and unique to your business goals.  

What are data strategies? 

In its essence, a data strategy functions as a checklist of core factors that must be considered to facilitate data analysis and BI solutions. Additionally, it serves as an ongoing roadmap by which you can monitor progress towards the short and long-term goals of your business. 

Fail to create a data strategy, and your insights will suffer. Results will become prone to error or variability as they lack overall continuity and trust – factors that are vital in gaining critical buy-in from stakeholders.  

Interested in learning more about the dangers of varying results and poor data optimisation? Read our full guide here – Understanding and navigating the dangers of missing data 

It’s important to note that data strategies are unique and tailored to a specific enterprise, and for good reason.  

Each organisation will not only have different short and long-term business performance goals, but will also have different demands from business users, unique data sources, and will be at different stages of sophistication in their BI tools.  

The data strategy needs to reflect this to help you fully leverage your current solutions as you define the next steps.  

Understanding short and long-term goals – how to begin creating a strategy  

Understanding the importance of a data strategy is one matter, but creating one for your enterprise is another challenge in itself.  

Before you can begin, it’s important to define your enterprise-wide short and long-term data goals, which will then inform the steps your strategy must take. Unique to your business, these goals should be achievable, realistic, and provide demonstrable value and growth, in proportion to the investment.   

What do short-term data goals look like? 

Examples of short-term data goals can include:  

  • Establish a centralised data warehouse over the next three months 
  • Migrate all on-premises data to the Cloud within five months  
  • Create a Report or Visualisation within three weeks and share with all teams 

These short-term goals are specific, pertain to a designated timeframe, and are all designed to provide distinctive value to a business. Migrating data to the cloud and establishing a data warehouse can ensure that all data collected is secure, centralised, and trusted.  

If a business has already completed these steps, they may consider the third example – creating reports and dashboards for at-a-glance insights. A valuable use case, this simplified view enables users to easily leverage centralised data.  

Exploring some long-term data goals 

The long-term goals of a business should still be designed to deliver measurable and distinctive value to an organisation, yet may be less specific in scope and form. Examples of long-term data goals may include:  

  • Introduce Machine Learning processes to take advantage of predictive analytics 
  • Incorporate automated modelling to better understand a strategy’s risks  
  • Design and build a unique reporting application and distribute to all stakeholders 

Once these goals have been defined and agreed upon, businesses can begin creating their blueprint for success. This ensures that they meet other key considerations throughout to keep progress consistent and risk-free. 

What do I need to consider when creating a strategy?  

There are a wide variety of factors to consider that can shape your data strategy, from the technical capabilities of your team to the presence of any ongoing quality assurance.  

Perhaps the greatest factor that businesses need to consider when creating a sustainable data strategy are the size and variety of datasets and their sources. This is a critical challenge that can present various issues to any business.  

The larger and more diverse a business’s data sources are, the more complexity is introduced into migration and analysis. A centralised storage location can help bring your isolated and varying datasets together for better overviews, insight, and clarity. 

If you want to learn more about the core factors that shape your data strategy, read our blog here. 

Providing an outsourced solution 

A leading data strategy can provide a value-led blueprint for driving business results, but can quickly lead to misallocated investment and scope creep if formed incorrectly.  

Using our innovative methodology, the DataShapa team can work alongside your business, investigating your current architecture to create a bespoke data strategy tailored for triumph. We offer a fully managed or hybrid solution to project integration, empowering your teams to focus on what matters.  

Visit our diverse range of case studies to see how our innovative solutions have enabled leading success today.  

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