Digitise to Monetise: 5 digital transformation projects that can help achieve your business goals


Many business leaders are excited about integrating new technologies like generative artificial intelligence (genAI) into their workflows.

30 January 202412 min
digital projects


Many business leaders are excited about integrating new technologies like generative artificial intelligence (genAI) into their workflows. While these new technologies offer significant benefits, many companies aren’t prepared to fully take advantage of them. They haven’t yet done the foundational work necessary to maximise the value of these new tools.

This paper outlines five digital transformation projects that can not only help organisations meet their immediate goals, they also set the stage for implementing innovative new technology that will help companies achieve new levels of success.

1. Content clean-up

Cleaning never seems glamorous. Perhaps it starts with childhood chores, but for many people, cleaning seems like little more than drudgery. But when it comes to content, cleaning is absolutely essential.

Most organisations don’t understand what kind of data they have. Many still have siloed operations with records spread across a variety of applications and systems. They likely have a mix of both paper and digital records, and the paper records, in particular, can be difficult to categorise and search.

Making matters worse, a lot of the information your company stores may be information it doesn’t actually need. Storing data unnecessarily might not seem like a big problem, but it can be very costly. Physical and digital infrastructure can be expensive. In addition, large stores of files, both physical and virtual, take longer to search, slowing productivity. They can also increase your organisation’s carbon footprint when many are working to become more environmentally friendly.

And retaining records you aren’t required by law to keep can open your organisation up to legal liability. For example, if your records contain personally identifiable information, someone could access or steal that data, putting your company at risk of non-compliance or lawsuits.

A good first step in any digital transformation project is to take an inventory of what you have. You can then devise and apply retention rules that designate what you should keep and what you should securely destroy.

Going forward, you’ll want to make sure that you have a way to analyse the metadata for new documents you create. That makes it easier to apply retention policies automatically going forward, helping keep your data clean and ready for use by AI or other technologies.

2. Value-based digitisation

Content clean-up goes hand-in-hand with value-based digitisation. Your initial inventory that identified unnecessary data provides the basis for a more exhaustive inventory that can help you decide which data you should digitise.

Not all data in your organisation is equally valuable. And efforts to “digitise everything” can create tedious work without moving you any closer to meeting your business goals, or enabling you to use new technological advances.

To find the most high-value digitisation targets, you’ll need to ask a series of questions:

Where is the information or content? This can be a geographic location, but it also might be a format, like paper, tape, microfiche, PDF, etc. And if the data is already digital, it might also include an application or a storage system. You’ll want to consider

What is the content? Because every organisation is unique, you’ll likely have your own criteria for defining different types of data. For example, you might want to include customer type, line of business, operational functional area, record type, document type, and individual data points when determining what your content contains.

How do you classify your content? Different categories of content require different access controls and different retention periods. Consider whether your content includes any personally identifiable information (PII), any employee information, or any sensitive information like company secrets. Also consider whether the content is actively used, or whether it might have future value.

What do you do with your content? How are you using your data today? Do you extract and process your data or simply store it? Does it need to be transformed, digitised, or destroyed? Should it be analysed for additional business benefit?

How will you prioritise your content? This is the most important question. You’ll need to determine which data makes digitisation worthwhile. To do that, consider the following issues:

  • Compliance: Some data must be retained to comply with regulations or to protect the organisation from legal risks. Digitising that data can speed e-discovery and streamline compliance efforts.
  • Business value/need: Data that relates to important lines of business or important initiatives can warrant digitisation.
  • Access: Will people be accessing the data on a regular basis, or are you retaining it in the event of an audit? Data that requires “hot” fast storage is generally a higher-value digitisation target than data that requires “cold” slower storage.
  • Cost: Consider how expensive your content will be to store and how expensive it will be to digitise.
  • Impact: How will digitising the data affect existing processes, service-level agreements (SLAs), customers and employees? You’ll want to choose digitisation targets that have the most positive impact.

Going through these questions will help you devise policies and procedures around which content you want to digitise. Don’t forget that digitising data will also make it more easily accessible to AI and other advanced technologies that you might want to implement down the road.

3. Content enrichment

Most organisations have a combination of digitised data (data that originated in another format before being converted), and digitally born data (data that has always been digital). If you just combine all that data and try to analyse it, you probably won’t be able to extract a lot of value from it. Why not?

You can’t really understand your data unless you understand its context. That generally means enriching it with metadata to enhance its meaning.

For example, imagine you have customer addresses both from paper forms and from online sales. You could analyse that raw data and discover information like how many people from a particular state or county buy your products. That data might be somewhat helpful, but it has limited usefulness.

Now imagine that you enriched that data with metadata related to customer addresses. For example, external data sets might tell you that people in a certain neighborhood fit a particular socio-economic profile. Maybe you know their average income, household size, number of children, and even spending habits. Maybe you also add metadata about when the data was collected and tag it with other labels that provide you even more information about your customers.

With enriched data, you’ll have a much better understanding of your customers. You’ll be able to conduct analytics that help you better market your products, and increase customer satisfaction.

Ideally, you want this process of enriching your data to happen automatically. And that’s an area where AI and machine learning can help. IDC explains, “Enterprise automation means artificial intelligence continuously supports decision-making and automated actions that proactively optimise and enrich outcomes. This process spans across the entire organisation and will maximise the business value.”

Of course, in order for this process to work, you first need the accurate, digitised data that results from the first two types of digital transformation projects covered. You’ll also need to have AI models you can trust and a highly skilled workforce that knows how to work with AI. You can make the process of building those models and creating that workforce easier by bringing in a partner that understands this technology. For example, the Intelligent Document Processing (IDP) capabilities of Iron Mountain InSight give you the ability to automate workflows and automatically enrich data.

4. Information governance

Iron Mountain defines information governance as “the multi-disciplinary enterprise accountability framework that ensures the appropriate behavior in the valuation of information and the definition of the roles, policies, processes, and metrics required to manage the information lifecycle, including defensible disposition.” In a nutshell, it means that everyone in an organisation understands the value of data and takes the right action with it.

For an information governance effort to succeed, you will need ongoing commitment at the highest levels of your organisation. It requires guidance and oversight, as well as education efforts that impact the entire organisation.

Information governance can help organisations reduce risk, decrease costs, protect sensitive data, and extract more value from data. When implemented well, it can also improve efficiency and give management more insight into the business.

Like other digital transformation projects, information governance might not seem flashy and exciting. It requires more careful processes than cutting-edge technology. However, if you do it right, it can generate significant value for the organisation. And again, it helps lay the foundation for more advanced technologies.

5. Data monetisation

The ultimate goal of many digital transformation projects is to generate tangible value from the company’s data. One way to do that is to drive a differentiated customer experience

Unfortunately, few enterprises are accomplishing this goal today. According to IDC, “Only 12% of enterprises connect customer data between departments, using it to make the customer journey better.”

Right now, many industries are facing increased competition from a new crop of startups. As technology evolves, it enables new ways of doing business. New companies form with the intention of taking advantage of these new opportunities, and they are often nimbler than their larger, more mature competition.

In this environment, using data to improve the customer experience and generate value isn’t just a nice-to-have — it’s essential.

And when you have gone through the processes of cleaning, digitising, enriching, and governing your data, you have the solid foundation you need to better serve your customers and monetise your data.

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