Four Pillars of Data-Driven Business Transformation
Leading Nordic businesses are investing in strong data foundations to supercharge data initiatives and get analytics into the hands of decision-makers in real-time
Building digital capabilities that drive productivity, real-time insights and business growth is a key priority for Nordic leaders in data and analytics. However, digital transformations are complex and difficult to execute successfully. In fact, research from Boston Consulting Group shows that 70% of digital transformations fail to reach their targets.
Despite this, the benefits of achieving successful digital transformation are clear. BCG research also shows that digital leaders generated 180% higher earnings growth than digital laggards in 2020.
To help data-focused leaders achieve these results, we’ve uncovered four essential data-driven pillars that are underpinning successful digital business transformations across the Nordic region.
1. Data Architecture and Infrastructure
To enable digital transformations, data must be accessible to those who need it. However, legacy infrastructure often isn’t fit for this purpose.
As such, former Nobia Digital Analytics and Insights Lead Emine Olausson Fourounjieva suggests that before embarking on the digital transformation journey, enterprises should conduct a thorough assessment of what data, technology and skills they already have.
“I recommend evaluating what there is already there – what the company has in terms of solutions, competence and connections,” she advises. “And then to understand what is really needed to get better and what might be too heavy to take with you into the future.”
70%
The proportion of companies that fail to meet their digital transformation goalsSource: BCG, 2020
However, Fourounjieva also warns that, when upgrading infrastructure, care should be taken to provide the necessary resources and skills to onboard it successfully.
“Oftentimes, I see stories where a great solution was taken on board but couldn't be onboarded properly because of lack of resources, or because of a lack of understanding about this solution,” she recalls.
“This creates other types of challenges, which are not always easy to deal with,” she concludes. “So, it's important to have a clear vision of the final goal and to have a clear strategy about how to get there.”
2. Data Governance and Management
High-quality data underpins every successful data and analytics initiative. Providing consistent and accurate source data is essential for ensuring data-driven insights are accurate and that staff trust the new technologies they’re asked to use.
Strong data governance and management processes also ensure enterprises comply with data regulations and prevent legal issues while they establish their reputations as reliable stewards of customer data.
However, as Husqvarna Group Director AI Lab Girish Agarwal notes, setting up these processes and rolling them out across business units takes time.
“Data governance has been one of the biggest challenges for us,” he says. “With a broad company like Husqvarna, that is making construction products for B2B sales and B2C sales, it's actually quite difficult. It takes time. It takes buy-in. It takes a lot of convincing.”
“I recommend evaluating what there is already there and what the company has in terms of solutions, competence and connections”
Emine Olausson Fourounjieva, Former Digital Analytics and Insights Lead, Nobia
Besides organizational challenges, companies that operate in European markets must contend with regulations including GDPR. This challenge is particularly acute in highly regulated industries such as banking.
“The really big challenges were on the legal side,” says Morten Bunes Gustavsen, Head of Data at DNB Asset Management. “[Such as ensuring] we are using the data in a correct way according to GDPR, and also the ethical considerations around using data.”
“Our main asset is really the trust that our customers have in us,” Gustavsen adds. “It's very important that we don't lose this trust. It will be very hard to rebuild it if we destroy [customer trust] by misusing data.”
3. Digitization and Automation
Applying data to streamline business processes and create new business models is the very essence of digital transformation. Once organizations have the right governance and data quality foundations in place, their data and analytics leaders can accelerate data-driven innovation projects and begin scaling up successful pilots.
A common strategy is to start by selecting a few use cases that can deliver value in the short-term and use them to secure buy-in and budget for a comprehensive enterprise-wide transformation.
“Build small success stories which you are able to share across the organization, so that you can start small and scale up across the organization along the way,” suggests Fourounjieva. “Then, it’s a lot about storytelling and knowing how to build the solutions which are going to help the organization to reach its business objectives proactively.”
“Managing a complex data pipeline becomes a heavy workload for analysts. Skills in analyzing data and revealing the potential of data so they can aid decision-making are then wasted”
Nick Nowlan, Nordic Lead, Fivetran
Scaling data-driven projects across business units efficiently requires a modern data stack. Automating elements of the data pipeline is saving Nordic data and analytics leaders time and helping them deliver insights and self-service capabilities to staff more quickly.
“[Automation] saves engineering dollars with automation of tedious processes like maintaining data pipelines without losing the quality of the data,” explains Fivetran Nordic Lead Nick Nowlan. “That is critical, so that engineers can focus on priority projects.”
“Managing a complex data pipeline becomes a heavy workload for analysts,” he continues. “Skills in analyzing data and revealing the potential of data so they can aid decision-making are then wasted on time-consuming tasks that don’t necessarily overlap with [these] skillsets.”
4. Data Literacy and Change Management
People are at the heart of the success of any business, and it is no different in digital transformation. Advanced technology and business analytics are worth nothing if staff can’t use these to achieve better business outcomes.
Data literacy is an essential skill that must be nurtured by data and analytics leaders. Staff should be able to use new tools, tell stories with data and draw the right conclusions from data-driven insights.
For Electrolux Global Workforce Analytics Lead Manana Rtskhiladze, promoting these skills has meant creating a program to train her Human Resources Business Partners (HRBPs) on the skills that will help them make data-driven decisions with people analytics.
“It’s a lot of engagement work; it’s a lot of selling the idea, explaining and demonstrating the benefits”
Gabriela Ayres, Global Credit Risk and Analytics Lead, Care by Volvo
“We have some webinars and we have, once a week, some open workshops where we look at the questions they have,” she explains.
“We also have the HRBP Academy, which is for employees interested in becoming an HRBP or for HRBPs that need to be retrained,” she adds. “We are looking to add analytics as part of that curriculum.
As data leaders in the Nordics upskill and expand their staff, change management should be a key focus. While it is not necessary for every staff member in every business unit to be a data whizz, changing working practices across the business is key to achieving successful digital transformations.
“It’s a lot of engagement work; it’s a lot of selling the idea, explaining and demonstrating the benefits,” concludes Care by Volvo Global Credit Risk and Analytics Lead Gabriela Ayres. “In the end, you can have all the tools and technology, but if you don’t have people utilizing them, then there is no point for the investment.”
This is an extract from our Nordic Data and Analytics Success Stories 2021 report. For more exclusive reporting on the state of data and analytics innovation in the Nordic region today, click here now.
