Image Source: James Briers
By James Briers, CTO & Co-Founder of Intelligent Delivery Solutions (IDS)

Member Article

Supporting Businesses During Digital Transformations

The Outlook for Businesses Embarking on their Digital Transformations

Digital transformation is no longer about progression, It’s about staying competitive. According to Gartner, by 2024, more than 45% of IT budgets will be spent on modernizing system infrastructure, while software and business outsourcing will shift to the cloud. However, simply storing data in the cloud is not enough to ensure a competitive advantage in 2022. The data stored there must be of high quality and offer value to those who plan to use it.

Data quality solutions need to work across all environments and platforms, and remain secure throughout any transformation, regardless of whether gigabytes or even zettabytes of data are shifting every second of the day. A new approach to data transformation is needed, one that simultaneously cleanses and assures the data through 100% of a migration to a new environment.

Ensuring the quality of your business’ data before transformation or migration to a new system is vital. Having disparate data sources is often the main cause of flawed, incorrect, and duplicate data being transferred.

The Common Issues Surrounding Poor Data Quality and the Costs, Implications/Risk to Businesses

Messy data is a challenge. But it is not as dangerous as duplicated data. Duplicated records are often the most concerning data quality-related challenge. For example, if inaccuracies stemming from duplicated payment records lead to incorrect perceptions of business performance, costing companies between 0.5 – 0.1% of annual invoices, that could equate to overall costs of £1m+.

Businesses simply can’t let this happen.

The challenge for businesses is that preparing, ingesting, and transforming newly acquired data can be time-consuming and complex. This can put projects at risk and see costs and timescales spiral.

It is essential to have a single point of truth when it comes to data. By consolidating data in a single source as quickly and cleanly as possible, pre-transformation, this prevents months of effort to cleanse the data afterwards. As well as the obvious costs associated with late and flawed delivery, and the potential for breaches, the reality is that you end up paying over the odds to host bloated and bad data in the cloud, and at the same time, skilled data teams are taken away from value-added activities to focus on fixes.

More critically, in the interim, senior decision-makers remain without that single point of truth for decision-making. And in a complex environment, whether in a regulated or unregulated sector, making decisions based on assumption, when competitors have a much more granular window into not just their own business, but into customer and market trends, can be fatal.

Best Practice, Insights from the Delivery of Effective Digital Transformations

An effective digital transformation, by anyone’s definition, will deliver a measurable change to the organization: better market insights, quicker time to market, a consolidated single point of truth; improved valuation; better customer service leading to an uplift in NPS (net promoter score), for example.

Business results can take time to be realized, however, so the immediate wins relate specifically to the projects themselves.

A local UK council enjoyed a 48% reduction in time by placing data quality first when migrating data from SAP to a new ERP. A scripted ingestion process delivered a fully automated mapping document to agree the most effective approach to transforming and migrating the data. This standardized approach meant transformation scripts were edited in line with the mapping rules set at the start of the process. A fully repeatable, end-to-end transformation solution was delivered, allowing 100% data quality assurance every time tests were executed.

Contrast this with MillerCoors who planned to roll out a unified SAP implementation for the entire company. The IT support failed to roll out the project smoothly. MillerCoors found eight ‘critical’ severity defects, 47 high-severity defects and thousands of extra problems during an extended ‘go-live hypercare’ period.

The greatest wins in any transformation come from improvements in the visualization of data; the handling of data; the quality of the data and, put in the simplest terms – the alacrity with which the transformation can be completed, and the completeness of the transformation. To achieve this, a tool such as IDS’s iData toolkit, which has been used in more than 150 transformations over the last five years, combines data quality, data transformation and migration, and test data management (including data synthesis and obfuscation), is not just nice to have. It’s essential.

Even with multiple stages of data quality assurance, the rapid process of transforming and migrating contaminated data into destination systems saved clients 45% in terms of delivery time.

By prioritizing data – almost above all else – organizations like Capita, the NHS, and a central UK bank have achieved 50% savings in budget and delivery time.

It does not matter if the data is 1GB or 100 petabytes or a single yottabyte, it is vital to have a data quality management plan before any digital transformation, no matter the scale.

Whatever the transformation, if you address data quality first, it’s unlikely to be a decision you regret.

This was posted in Bdaily's Members' News section by Intelligent Delivery Solutions .

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