A data and technology industry executive with a successful track record driving worldwide growth and innovation, Kevin Campbell is the newly appointed CEO of BackOffice Associates, a trusted leader in solutions that solve enterprises’ most complex data challenges. We spoke with him about how organizations can map their digital transformation supported by a comprehensive data strategy.
Jean Loh: What should be the first step for any organization embarking on a digital transformation initiative, especially given that any digital transformation also requires a data transformation?
Kevin Campbell: Digital transformations are only as powerful as their ability to help organizations transform with certainty and evolve into their desired next state: profitability, cost reduction, growth, time to market, etc. As we’ve seen through our premier partnerships with SAP, businesses that move to an intelligent platform like SAP S/4HANA will realize competitive advantage and unlock new business value.
With this in mind, the first step should begin with defining the vision of your organization and intended identity post-digital transformation. From there, success measures should reflect the future state of the organization so forthcoming progress can be measured through forward-looking KPIs. These KPIs will serve as a guidepost for determining what business data needs to be reported on across the organization – and ultimately allow leaders to develop an underlying, comprehensive data strategy that reflects the value of these insights.
Establishing trusted business data is a critical – yet often underestimated – element for driving successful digital transformations. For example, SAP S/4HANA is a key enabler of digital transformation for many organizations. A recent study by ASUG [Americas’ SAP Users’ Group] demonstrates the foundational role that data quality, accuracy, and relevancy plays in delivering on the promise of digital transformation.
We often hear from customers that they feel overwhelmed by the number of seemingly critical data elements involved in digital transformation, making it difficult to decide where to start. A holistic outlook and approach to addressing your organization’s data journey is key. Executive leaders should determine their top priorities for driving business outcomes and let that guide their decision on where to begin and determine what data is needed most.
There may be lots of digital transformation projects offering sophisticated data analytics. But the most successful projects will cross-functionally leverage data with certainty in a way that moves the needle for an organization’s priority business outcomes.
JL: How can organizations effectively bring the promise of artificial intelligence and machine learning into a data-driven reality?
KC: It’s no secret that AI and machine learning have become the top wish-list items among CIOs and CEOs. However, the real potential of AI can be reached only if your organization’s data, which AI relies on, is accurate and business-relevant. You need to trust the source of the data being used to feed AI programs, and the data must be governed properly across the organization. This fundamental piece of the AI and machine-learning puzzle is critical for allowing AI and machine-learning technologies to “learn” how to evolve intelligence and make smarter recommendations for the business. It is also based on the premise that knowledge from the past and present must be preserved, as it ensures valuable reuse and time to market.
We’ve seen many examples of CEOs struggling to understand which versions of their data are accurate due to poor data quality and governance. These companies need to establish a trusted source from which their data is managed through best-practice, automated governance, including standardizing data definitions and rules – part numbers, terminology, and so on. Once data trust and governance are instilled, then AI and machine learning can be executed for business gains.
JL: Are there other key pieces to executing a data-driven strategy beyond managing the data? And if so, how should enterprises orchestrate all the moving parts?
KC: Trusted data can be a competitive asset for businesses only if it is set in proper context and in compliance with established business policies. Information governance is a key step along the data transformation journey, but organizations often underestimate the criticality of having an effective governance process. Executing governance for a low volume of data isn’t problematic.
However, when we’re talking about millions of transactions or data requests coming from a host of different systems and data types (structured or unstructured), you need robust tools and processes in place. This is the only way to help business stakeholders manage and leverage the critical data without becoming completely overwhelmed. These stakeholders need to know which data is the highest priority, what data sources can be trusted, and which individuals are allowed to contribute to maintaining data quality. And they need to know what business processes and rules will be utilized across the company to ensure a solid and sustainable governance program.
JL: Compliance concerns are rampant across nearly all industries. How can organizations establish and maintain real-time compliance through next-generation data automation?
KC: Organizations in every industry must align with regulations, many of which change frequently and have costly consequences for noncompliance. However, if you’ve carefully defined your organization’s critical data elements and established trustworthy data sources, then it is a straightforward step to map relevant compliance rules to your key data.
For example, in the case of GDPR, organizations must be able to track the use and storage of personally identifiable data in all systems with certainty. They can ensure ongoing compliance with even the most stringent regulations when they can identify which data falls under this category, and then automate rules around the treatment of the data – for instance, remove people from all of the company’s various databases if they opt out. Then they can more easily adapt to newer regulations and evolve their governance programs to accommodate shifting data elements, learn from incident responses, and so forth.
JL: How can you ensure that your data-driven transformation is generating ROI for your organization?
KC: With clear metrics in place to measure business results, having clean data is self-evident if the transformation initiative is producing intended benefits. There are situations, however, where business continues to be conducted without proper data practices in place – and these dynamics are tricky because there are hidden costs of bad data.
For example, if duplicate part numbers exist for a manufacturing company, the manufacturing plant is still able to ship products and may underestimate the impacts. In reality, the duplicate data ends up costing the company in the form of mistakes on both current and future orders and inability to report on sales across parts or SKUs. This can have a big impact on physical storage costs and a host of other snowballed impacts.
The bottom line is that ROI for transformation initiatives is dependent on establishing and maintaining clean, trusted, well-governed data as a fundamental piece of the holistic data equation. From there, ROI only increases as organizations leverage automation for increased productivity, response rates, and the addition of other value-added activities.
As an example in the retail industry, with trusted data, a retailer can accurately gauge the average sale per customer across both brick-and-mortar and digital platforms. This, in turn, enables the retailer to create new products and innovations based on prior purchases and preferences – and ultimately drive increased revenues.
For more on data management strategies for today’s enterprises, see “Extracting Value From Your Most Precious Asset For The Intelligent Enterprise.”