The perils of not being data-ready

Before you even begin thinking about building a data platform, specialist database, or AI platform, you must first ensure your organisation is data-ready.

But what does it mean to be data-ready in 2025?  

A strategic approach is essential  

Successfully delivering a modern data project requires a strategic approach with clearly articulated use-cases and value statements. A tactical, or siloed, approach to data will lead to a range of complex challenges in the current age of AI and digital-first business.  

As we’ve discussed in our recent whitepaper, a strategic approach should ideally be the starting point for any data project. That’s equally true whether you’re building a proprietary data platform or migrating a database from your current system.  

Before you begin planning any kind of data platform project, ensure your data strategy is not just focused on technology, but also people, processes, and culture. A successful data strategy, as you’ll find out later in this article, is just as dependent on those other factors as it is on digital tools.  

Use cases must be aligned with business goals  

We’re all familiar with the concept of “crap in, crap out,” which often causes people to focus entirely on data quality. But it can be misleading to apply this adage to a data platform project on its own. This idea is overly reductive when looking at your organisation’s data, as data quality isn’t actually the most important factor in data-readiness.  

Linking to our previous point about a strategic approach, it’s more important to have a clear plan and strategic use cases for your data platform or database than having perfectly clean, structured data.  

Before you begin taking steps to improve data quality and governance, you must first understand, at the business level, the strategic purposes your data needs to serve.  

You can’t judge whether the quality of your data is good enough unless you know what it’s going to be used for.  

Determining your use cases should usually be based on how your new data platform or database will support the delivery and realisation of the organisation’s strategic objectives.  .

Examples of such use cases could include:  

1. Developing a multi-faceted view of your customers by integrating and mastering data from multiple systems, improving your teams’ understanding of your customers and your overall customer experience  
2. Monitoring hardware performance using IoT devices, to predict when a machine needs to be repaired, rather than reacting when it breaks down  
3. Forecasting footfall in a retail shop during busy periods of the year, to ensure resourcing needs and budgets are allocated effectively

Data quality and consistency are critical  

Of course, even with the right use cases, trying to implement a new data platform or database without consistent, clean data is still dancing around the problem.  

So, once you've defined your use cases, you can then move on to preparing and cleaning your data to ensure it functions specifically for those use cases.

Data quality and consistency are so difficult to achieve in most organisations because, for many years, businesses have operated without a holistic data strategy or without company-wide best practices.  

If your organisation contains disconnected, siloed systems or teams, or loose standards for data consistency, it will require a lot of work to ensure your data is fit for purpose.

For instance, say you want to build a data platform using data from two different databases, such as Salesforce and ServiceNow. Each system will have a different schema or data model for structuring and relating data.  

As a simple example, you may identify customers differently in each system:

• Salesforce might use “account.id”
• While ServiceNow might use “contact.sys_id”

Additionally, you may have trouble reconciling information about a customer where there are crucial differences. For example:  

• The client’s name might be “ACME” in one system
• And “ACME Ltd.” in another

These differences are problematic when consolidating and mastering data from two systems into one data platform.  

Some examples can arise from human error, but most often it’s due to lacking best practices for organisation-wide data consistency, including insufficient education for the workforce on why this is so important.  

This is why a data strategy that prioritises the human aspects of data – such as company-wide standards for data input and hygiene – is so vital. Modern employees must understand the potential negative impact that poor data practices can have on the business.  

It’s therefore wise to introduce education and incentives for all employees to accurately capture, input, and validate the data they use, to maintain consistency across all the systems in your organisation. After all, considering the importance of data in decision making, data-readiness directly influences organisational maturity and success.   

Red flags suggesting you may not be data-ready  

Incomplete data models or lack of data asset intelligence  

If your stakeholders don’t have up-to-date knowledge of all the data present in your organisation, including where and how it’s stored, that’s a sign you’re not “data-ready” currently.  

There may be sources of data throughout your IT estate hidden away in silos, with those responsible for data governance and management unaware of its existence.  

Creating a comprehensive, company-wide data inventory will establish asset intelligence over all your organisation’s data.  

Once you have this in place, the next step will be to integrate it effectively to ensure your data models are complete. However, data integration and consolidation can be complex, challenging tasks. With that in mind, working with IT managed service providers is often necessary to gain the necessary data governance practices and tools. 

Fragmented or inaccessible technology  

Up-to-date awareness of where your data is stored is only half the battle, as data accessibility is just as valuable in many cases.  

It’s common for data to be inaccessible to stakeholders due to the technologies involved. This is particularly true with a fragmented IT estate that includes several different data systems, which is common in so many businesses these days.  

Certain systems and tools, along with certain types of data, present challenges in extracting the data and structuring it in a way that’s compatible with your new data platform or database. For example, if some data sets are stored in an Informix database, but your only Informix database administrator has departed your organisation, you’ll need to either hire new talent or upskill another employee before that data can be extracted. Both of those are time-consuming and expensive tasks.

Establishing a full inventory of all the organisation’s IT systems is another important task on the way to data-readiness, but this can also be a challenge. A complete IT asset inventory will enable you to conduct a company-wide data inventory here to ease this challenge.

Poor data hygiene and inconsistent data  

Let’s return to the data consistency scenario mentioned earlier.

You’re aiming to build a data platform using data from two different databases, such as Salesforce and ServiceNow. However, the data stored in each system has inconsistencies that will harm the effectiveness of your new data platform.

This problem often arises from a lack of best practices and workforce education on consistent, accurate data input, storage, and management across the organisation.

But how can you overcome this challenge efficiently and cost-effectively?  

Cleansing the data will involve extracting all your data into a data warehouse, then standardising the input fields.  

With the earlier example of:  

• An input field in one system may be “account.id”  
• While another might be “contact.sys_id”  

The data cleansing process would result in all your data points made consistent under the input field of “account.id”.

On the point of consistency, this is where a data-conscious company culture becomes so valuable, as that empowers users to prevent many of these challenges by following best practices.

That data-literate utopia is ideal in theory but, in reality, the majority of businesses today are in a position where they have large data sets with a vast range of inconsistencies like these, even across mission-critical areas of the organisation. Therefore, data cleansing and consistency is a critical step in most new data platform or database implementation projects.  

Still, large-scale data cleansing projects are highly complex and time-consuming, especially when approached without specialist experience, expertise, and tools. Working with a managed service provider is essential for a thorough cleansing of your data, to ensure your organisation is data-ready and able to embark on a successful data project.  

Don’t go without data platform and database management capabilities

Once you’ve clearly defined your strategic use cases, improved data quality, and gained data-readiness, then you can move on to the implementation of a new data platform or database. But a successful data strategy doesn’t stop at the implementation of a new data platform or database.  

Once those new systems are up and running, you’ll require dedicated skills and tools to manage those systems.  

In addition to cleansing your data, managed service providers are the ideal partner because they also provide invaluable support in the ongoing management and optimisation of your data platform or database. For example, with database-as-a-service (DBaaS), you can hand over the configuration, administration, and management of your database to a trusted expert.  

DBaaS removes the stress of managing complex systems internally. It also significantly reduces the risk of future data challenges, such as weak ROI from low adoption, lost revenue due to poor decisions made on inaccurate data analysis, or even data security and privacy breaches.  

Being data-ready, with a holistic data strategy, is crucial if you want to avoid being left behind by your more forward-thinking competitors.  

However, this is a challenge even the most advanced organisations still struggle with. That’s why those organisations work with experienced partners to support their data strategies and platforms.  

Don’t get left behind in the never-ending race to keep up. Ensure your organisation is data-ready.

Get in touch today to find out how Claranet can help you use your data to transform and empower your business.  

This includes:  

• Data and AI solutions to transform your organisation

  Make your data more accessible, visible, and secure, using managed and professional services and AI solutions that sharpen your competitive advantag

• Data platform services

  Make your data visible and securely accessible with a scalable, flexible, and cost efficient cloud-based data platform.