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Martin Sokalski, KPMG

It is no revelation to say that the pandemic has disrupted most organizations. From sales and supply chains to finance and tax, even the best-managed organizations have scrambled to become more agile and collaborative. Despite some temporary fixes, it’s clear that taking decisive action in a constantly changing environment is hindered by data that remains heavily siloed by function and department. 

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David Jarczyk, KPMG

When we ask why these silos exist, responses vary. In our view, many professionals have been hyper-focused on the job at hand and have, therefore, become accustomed to creating their own systems and tools for acquiring and utilizing data. Challenging times serve to further fuel this mindset, with many organizations espousing the idea that employees should do what they need to do to get the information they need to keep the company moving forward. These dynamics serve to reinforce and perpetuate data silos.  

Managing a Mountain of Data

The biggest challenge organizations face is managing the sheer depth and breadth of data they need to analyze. In fact, as the battle against COVID continues, both internal and external data can be critical to organizational survival: Internal data might relate to supply chain bottlenecks, customer preferences for digital modes of interaction, and real-time inventory levels. Equally critical is external data that can shed light on economic indicators, infection rates by geography, demographic trends, and regulatory actions.

First, organizations must ensure that all data is accessible, reliable and fit for purpose.  This model will unlock information and insights for timely, informed decision making and mark the beginning of the end of the siloed structure.  Key word: “beginning.” This means democratizing data and ensuring that it is validated and broadly accessible from a trusted source of truth. Data must be reliable because some of your competitors are already using data to drive their growth and conceptualize product and service innovations.

Second, organizations must ensure they have the necessary skillsets at all levels of the enterprise so that data can be turned into actionable insights. Although there are exceptions, such as, say, pharma and high-tech companies, organizations in many sectors are still in the early stages of developing advanced data skills.

Third, we encourage companies to develop a clear strategy to guide all stakeholders in using data to achieve both specific and collaborative business objectives. Organizations should strive for a holistic plan that manages data content, ensures data quality and lineage, aligns with the organization’s ethical principles, and is governed by the necessary privacy and security controls. Regarding the latter, it is possible to de-identify customer data and encrypt personal identities in order to alleviate concerns about privacy breaches. 

Getting to a Post-Silo Environment

When systems are built, they are rigid and governed by much-needed rules and procedures. Companies typically don’t have the luxury of making operating model changes overnight. On the other hand, the pandemic amplified the urgency of acquiring and sharing information rapidly between customers, partners, suppliers and regulators. And it inspired companies to employ transformative analytics technologies that might have remained aspirational under less-daunting circumstances. As companies continue to rebuild or transform themselves post pandemic, they are on a perpetual quest for ways to work faster, more efficiently and more cost effectively. Those who have done relatively well during the pandemic recognize that a large part of their success comes from aligning their stakeholders and giving them access to the same data trail, so all parties have a line of sight into whether corporate initiatives are moving the company toward its objectives.

These are broad, market-wide issues. They aren’t relegated to a small handful of sectors. We’ve seen it in the retail food sector, with major grocery store chains using artificial intelligence-powered virtual agents to respond to customer inquiries. In healthcare, hospitals have looked to digital-twin technology to apply predictive analytics to fluctuating needs for personal protective equipment and other vital supplies. In global trade, blockchain has proven to be a transparent way to track and trace the movement of assets. And, clearly, information sharing between pharmaceutical companies has been critical to developing COVID-19 vaccines in record time.

Taking the First Steps

So, how do you start? Before embarking upon a data-driven digital transformation, ask yourself, and other leaders within your organization, these questions: 

  • Do we have an integrated data strategy?
  • What kind of data do we need across the organization and across stakeholders and are we converting such data into meaningful insights?
  • Are we missing data that our competitors have? If so, how can we acquire it?
  • Where is our data stored, validated, indexed and is it readily accessible and trusted?
  • What are the consequences of not getting data in a timely manner?
  • Are we overburdened with the number of tools needed to address important data-related challenges?
  • Have we vetted third-party suppliers for their data-sharing and interoperability capabilities and policies?
  • Are existing tools addressing systemic issues with the data or are they serving as narrow point solutions that require significant work to maintain and/or sustain the capabilities?

Perhaps the biggest thing to keep in mind is that you’re asking people to change the way they think about information. Getting the organization to adopt a data-centric mindset and understand data management isn’t easy. But it’s how you’ll move beyond COVID-survival mode and reorient your business to, once again, concentrate on competitive growth.

 

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Innovation Leader regularly publishes Thought Leadership pieces written by our Strategic Partner firms. A version of this piece also appears in our 2021 research report, “The New Imperatives.”