Data Strategy
April 18, 2024
As the goalposts of digital transformation continue to move, how should multinational enterprises begin their own journeys? Start with a data governance team to instill trust in your company’s critical customer data.
With 2020 right around the corner, it’s time to acknowledge that we’re living in a Groundhog Day-esque time loop. Every year, we hear about the “new” digital transformation— what it means, its importance, how to achieve it, the technologies you’ll need, etc.— but at the core, we’re still living the same day— err, challenge.
In 2019, digital transformation risk was the number one concern of senior executives, but these initiatives are still failing at alarming rates. Research from McKinsey found that only 16% of organizations’ digital transformations have improved performance and equipped them to sustain changes in the long term.
And yet, digital transformation remains a critical business goal. According to a recent Masergy survey, nearly 80% of technology decision makers claim that digital transformation is critical to the survival of their organization. (As an aside, I’d like to meet the 20% of technology decision makers who don’t believe digital transformation is key to their organization’s survival. But I digress.)
It’s little wonder why they believe this. Consumers have rapidly adopted to digital channels, starting with the web and now migrating towards spending 3.5 hours per day in a mobile ecosystem. Companies who started out digital— the Facebooks and Ubers of the world— are often much quicker to adapt to changing consumer trends and new enterprise technologies. Most corporations, however, have to deal with a massive structure and legacy systems that are difficult to sunset.
Trading out a bespoke, legacy system is not a simple replacement, and considering that the average company changes out over 40% of their SaaS stack every two years, that calculus becomes more complicated for a multinational enterprise.
Thus, the challenge of the digital trend is that the goalposts are constantly moving as new technologies supplant the “old” new technologies. Companies are left with a Sisyphean task, constantly pushing the digital transformation rock up the hill, only to have it come barreling back down to the bottom. For enterprise companies with multiple brands and customer touch points, this challenge is magnified. Smaller companies with a single product or who are limited to smaller geographic footprints have more agility to adapt to new technologies and processes across the organization. Scaling digital transformation across distinct brands and teams and regions and languages inserts seemingly nth degrees of complexity.
How do you break the proverbial time loop of digital transformation and move on with your life?
Understand What’s at the Core of Digital Transformation
When people talk about digital transformation, usually what drives the conversation are the new technologies: AI/ML, blockchain, better analytics, 5G connectivity, etc. The central tenet of digital transformation can’t be the technology, though, as that’s what leads to failure, according to HBR:
Fundamentally, [digital transformations fail] because most digital technologies provide possibilities for efficiency gains and customer intimacy. But if people lack the right mindset to change and the current organizational practices are flawed, DT will simply magnify those flaws.
New technologies provide new possibilities, but the new possibilities get to an old truth: to run a business, you need to understand your customers and how your business is working. And at the heart of that truth is data.
“Data” that once lived in the tribal knowledge of employees who ran physical stores now is dispersed and siloed across every SaaS platform, laptop, data warehouse, spreadsheet, and paper record in your organization. The digital transformation turned that tribal knowledge into digital tribal knowledge, but unlike people, it’s much more difficult to make all of these disparate technologies share information.
Thus, the aim of digital transformation is not for companies to “become digital,” because the world is run in the digital realm whether or not your company is transformed. Digital transformation efforts aim is to remove the barriers between the technologies and the people who use them and to make the exchange of information seamless. That requires you to change the way people think and work as much as it requires you to change technologies.
Again, this challenge is magnified the larger and more complex the business organization, but at its core it remains the same. Starting with a technology as the “solution” to the problem is doomed to fail unless you change the mindset and organizational practices across the organization.
Trust as KPI
So, if we turn away from technology as the driving force behind the digital transformation, what’s left to take its place? What’s the measure of a successful digital transformation?
Trust in data.
Trust should be the guiding principle for any digital transformation effort, and data— not tools— is the common denominator to instill that trust.
According to HBR, research shows that only 3% of companies received an “acceptable” data quality score based on the loosest-possible standard. Furthermore, on average, 47% of newly-created data records have at least one critical (e.g., work-impacting) error. If you’re putting bad data in to any digital transformation technology, you’re going to get bad data out. You’re digitally transforming garbage into different garbage, but would you recognize it as garbage?
Thus, placing trust as the foundation for any digital transformation effort, regardless of your companies’ size, is key.
But, as I’ve mentioned, the complexities of large enterprises make trusting the data more challenging.
Let’s talk about addressing that challenge.
Establish a Data Governance Team
How do you instill any level of trust in the data, between teams, across brands, all the way up to the executive level?
For many companies, data exists in a black box. They may know where data originates and where the data ends up, but what happens in between can often be shrouded. The lack of transparency is what makes data untrustworthy.
In a world of privacy regulations like the CCPA and GDPR, the lack of transparency around data can be very costly if it leads to data breaches or just being able to respond to requests to be forgotten.
Even if these data regulations weren’t around, there is a clear business need for a data governance team to ensure transparency in the company’s data.
First of all, enterprise companies with distinct brands and teams oftentimes rely on some overlapping tools and centers; the interlocking Venn diagram of service centers and brands share data and tools, but they are all focused on their own success. That is to say, they’re not focused on the quality of the data beyond the bounds of their own use cases. Who is responsible at the highest level for the transparency and safeguarding of data? Oftentimes, no one.
In part, because the digital transformation is so driven by piecemeal technology purchases, this creates new silos of data each time. On top of that, the teams that own these technologies may be protective over the data they create. While data transparency can feel like a burden, the risks associated with siloed data are much greater.
Furthermore, this lack of an enterprise-wide data governance team means that companies are missing out on the cross-brand experiences of consumers. For companies with similar portfolios of brands— i.e., brands that all fall within the same consumer sector— the streamlining of a cross-brand experience makes perfect sense. However, even large conglomerates with disparate brands selling unrelated consumer goods can benefit from a cross-brand data governance approach.
One way may be through machine learning, which can glean previously unthought of insights; for an enterprise with an unimaginable amount of data on consumers in various industries, an enterprise-wide approach to data governance, in which data is collected and stored using a universal set of rules (and thus, is compatible with data across multiple brands), can set the stage for a robust ML-initiative. But without the data governance team overseeing standards across the enterprise, any ML project will be a Herculean task.
Still, machine learning initiatives require a mature data organization, and the vast majority of companies aren’t there yet. In fact, companies are possibly getting worse when it comes to data. In spite of an increase in Chief Data Officer roles, only 31% of companies say they’ve created a data-driven organization in 2019, which is down from 37.1% in 2017.
In a large enterprise company, an independent data governance team can help jumpstart a successful, organization-wide digital transformation by identifying where data is becoming more of a liability than an asset.
Here’s how you can get started.
Make data governance a strategic business initiative across the enterprise and every department
Data governance is as much about the people and the processes you’ve got in place as it is the technology. With buy-in from the top-down, a “data governance team” will be possible.
Establish a Data Governance Team/Center of Excellence
An enterprise-wide Data Governance team can help establish rules and norms for data handling, as well as vet new technologies from a different perspective than the end-user, who may not be considering the broader uses of the data created in that technology.
Map out all of your data and systems of record
With a large enterprise, the flow of data between systems is going to be unique. Likely, it’s a Frankenstein-esque assemblage of legacy, on-premise technologies and more contemporary SaaS and cloud-based offerings.
Determine where there’s a lack of transparency
Complete an audit of your data. Are there teams or technologies creating silos of data?
Determine the process and technology changes needed to fix it
Once you know where your data lives and where you’re losing sight of it, then you can start to diagnose the process and technology changes that are needed to fix the data problem. This may include improving integrations and buying new technologies to supplement missing capabilities.
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A Data Governance team is intended to help bring trust to your company’s data. By establishing one before any enterprise-wide data initiatives, like standardizing customer data through a Customer Data Platform, you have the chance to address the data quality issues that will inevitably diminish the value any technology purchase.
Learn how pairing Tealium’s Customer Data Hub with enterprise data storage solutions can provide the foundation for a complete enterprise customer data strategy.
