Why data remediation is key to digital transformation

How effective data management drives digital success

Just five years ago the term ‘digital transformation’ was largely shrugged off as a buzzword, but now, according to our research of 950 IT and business-decision makers in France, Germany, Italy and the UK, nearly two-thirds of businesses (63%) are “engaged to a great extent” in digital transformation.

There is, however, still some way to go, as the research suggests there tends to be low collaboration between the IT department and departments on transformation projects. For example, three-quarters of businesses (75%) still believe it’s “something that the IT department does” when in fact it has implications for and requires buy-in across the whole organisation. Alternatively, 65% of respondents say that individual departments drive their own digital transformations, which shows that their efforts are still highly siloed.

Businesses are also under increased pressure to deliver digital transformation quickly. As a result, nearly one-third of respondents (30%) advise that technology hasn’t been implemented properly, 29% say change isn’t well communicated, and 28% say digital strategy is poorly thought out. Unsurprisingly, given these observations, the vast majority of businesses (94%) report major challenges in delivering digital adoption at a functional level.

Getting data remediation right

When addressing data remediation, the first requirement is a system of robust analytics. A software solution, managed service or process that enables you to audit your complete data set will help you to identify what you have, classify the information and cleanse it to eliminate the ROT content.

Following your initial data remediation efforts, it’s critical to adopt data strategies that will ensure as little ROT content is stored and shared as possible. Here are three questions every IT decision maker should ask themselves when creating their data strategy:

1. What can I do to minimise redundancy?
   
   Develop a strategy that allows individual data assets to freely flow between and be used by multiple departments and for multiple purposes. Embrace the mindset of “repurpose versus recreate,” and for each asset look to create a single ‘source of truth’.
   
   
2. When is the best time to capture information?
   
   It’s key to capture information as early in the process and as close to the first touchpoint as possible. This information could be captured through different channels such as mobile devices, as data through an electronic form or from an image scanned at a remote field office.
   
   
3. How can I best leverage capture and recognition technologies?
   
   Ensure that technologies including optical character recognition (OCR), data capture, analytics and auto-classification are integrated with core enterprise systems that include ERP, CRM, and content management systems.
   
   Data remediation can be a time-consuming and costly effort, but it is vital to delivering an effective digital transformation. With the right information governance program and tools in place, and using analytics aligned to support your policies and processes, you can expedite data remediation and meet your digital transformation goals.