data ethics

How Organizations can Boost Digital Transformation with a Data-Driven Culture

Many organizations fall into a common trap when approaching data and digital projects. They’ve not asked themselves two key questions, ‘Why are we doing this? And what exactly are we hoping to transform?’.

Digital transformation has always been closely intertwined with data. In fact, one of the first steps on the road to digital transformation is taking control of data and removing the barriers that prevent the business from exploiting it. But while technology leaders are certainly paying lip service to data by acknowledging it as an asset in their organization, to really make the most of data they must first understand and advocate for its transformative potential.

At the heart of this promise of transformation sits the need for a robust data foundation. But not every organization needs to immediately implement advanced analytics capabilities to serve its customers or improve workflows – and many organizations are just not ready for this setup. Instead, organizations that want to put digital-first and become truly data-driven can still make great strides with a more measured approach.

In this article, I’ll provide some practical guidance for organizations that want to unlock the power of data to help accelerate their digital transformation.

By Ryan Moore

First things first – why are you doing this?

Many organizations fall into a common trap when approaching data and digital projects. They view these initiatives as way to upgrade technology, under the misguided assumption that upgraded systems and new applications alone will improve workflows and productivity. Whilst somewhat true, this approach lacks vital strategic focus. They’ve not asked themselves two key questions, ‘Why are we doing this? And what exactly are we hoping to transform?’.

Most digital transformation programs deal with the migration of offline, manual processes to online, digital environments. For this reason, digital transformation projects are, at their core, data projects, as migration requires the digitizing of data, content, and information across an entire organization. The two are more than just interlinked, they’re inseparable.

Similarly, every organization needs to establish desirable business outcomes from its digital transformation strategy, but many tend to ignore the impact of these transformational activities on their data. Successful transformations work two ways – digital plans have a profound impact on the proliferation and use of data, and the way that this data is managed has a direct impact on the success of the planned digital transformation.

That’s why aligning your data and digital strategies is a great place to start.

But even without a fully-fledged data strategy, you can still nail down a set of information and data-driven principles to drive a roadmap of activity that stems from digital transformation, marked by the adoption of new tools, technologies, and talent.

Prioritize data personas and experiences – and transformation will follow

The outcomes of your data strategy should be chiefly concerned with the consumption of data. In short, you’re asking ‘who will benefit from improved access to more accurate data?’.

Inspired by User Experience (UX) teams and their approach of researching and defining personas, a key aspect of any data strategy should be to put your users first. And to do that, you need to define data personas and understand their data experience. For example, utilities organizations will have teams of field engineers, as well as office-based teams in charge of organizing maintenance callouts. The information for both sets of users might be the same, but there are several significant contextual issues that must be reconciled. An engineer working underground will likely be equipped with a tablet device and may be working with gloves or other PPE on. This means form factor, application interfaces, and data visualization must be suitable for the user and the work they need to carry out.

I’m a big believer in this foundational approach. Get a clear understanding of who uses data in your organization, how they use it, and why, and it will be far easier to build towards a better data experience. Even the most accurate insights, if they’re presented in the wrong format or through the wrong channel, simply won’t be used.

When data strategy and digital transformation are both valued and aligned, relevant, accurate insights can make their way across the business and its users in the most effective way, employing the best tools and systems for the job.

Technology can put you on the right path

Today, most businesses operate using multiple siloed systems that serve multiple teams. But when you have siloed systems, it typically means data is not easily surfaced or immediately usable. To address this challenge, many businesses try to start their digital transformation journey with the lofty goal of bringing all their data into one centralized system.

Platforms like Azure offer the temptation of a single integrated solution and a full suite of enterprise and data tools to exploit. When moving to this monolithic structure we need to consider data governance, data quality, security and – most importantly – the domain expertise and business context of the data in these systems. Plus, building data management pipelines that reflect all of this can quickly ramp up the costs of your digital transformation programs, especially if you don’t choose the right technology stack.

Another factor is the emergence of self-service and low-code/no-code toolsets, like Power Apps and Betty Blocks. While these can be powerful tools in your digital transformation arsenal, decisions need to be made on how these user-built applications co-exist alongside enterprise applications and where the data they generate resides.

At the other end of the scale, there’s a data mesh – a decentralized approach that centers around the concept of leaving the data within business domains and systems, rather than moving it to a centralized repository. This approach also focuses on data as a product, self-service as a platform, and federated governance.

There is no single approach or correct technology stack. Very often, businesses will use a combination of technologies. Whatever you choose to do, just make sure that business objectives and user needs are the key factors driving the adoption of your chosen technologies.

AI is just a buzzword, until your data strategy is right

We all know that AI is on every organization’s digital transformation checklist. But without a solid data strategy and the right investment in data infrastructure, firms will fail to reach the data maturity needed to harness advanced, AI-powered analytics capabilities.

To build towards this, a good place to start is by experimenting with open-source machine learning tools in the early stages of their digital transformation strategy. It should be immediately apparent if sufficient high-quality data is available to enable advanced analytics – and if it’s not, you can use this insight to improve your data strategy. But if your models do show promise – and there’s a business case to productionize them – a machine learning platform like Databricks can offer a sensible route that’s specifically architected to ensure models are compliant and enterprise-ready.

Whatever the starting point, businesses must understand that digital transformation has no true endpoint. Though initial outcomes and goals may be realized, there’s always more that can be done to power digital transformation as your organization’s vast data universe continues to grow. That’s why data-driven strategy and increased data capabilities should always be a focus of your digital transformation strategy.

This article originally appeared in ITProPortal.

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