Building the Data-Driven Enterprise for a Resilient Future

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Click to learn more about author Bas Kamphuis

Confronted with the current state of the world, business leaders are reassessing and recalibrating how they can better prepare themselves to become more resilient across their supply chain, their financial and customer operations, and the protection of their employees. Early in 2020, many companies were already looking to innovate faster; now, the imperative for data-driven insights and digital execution is a first-order priority that’s forcing businesses to accelerate business transformation efforts.  

Enterprise Data Strategy is critical for unlocking digital transformation, and 2020 is a wake-up year for many firms, as the total cost of getting data wrong will become apparent, according to Forrester. While it may be tempting for some organizations to put technology projects on pause, companies that invest in their digital capabilities will emerge stronger as data-driven enterprises, which I’ve defined as “an iterative approach to automate and optimize business processes as conditions evolve.”

Today, the speed of any company is determined by its technology and processes and its ability to tap into real-time insights to scale the business model and operations. Consider how a manufacturer of essential medical supplies can ramp up production and manage its supply chain to meet spiraling demand from the pandemic. In this instance, having a solid Data Management foundation is crucial for real-time monitoring, analytics, and automation to manage and streamline production processes, ensure quality control, and guarantee timely delivery.

The Complexities of Becoming a Data-Driven Enterprise

The rationale for many digital transformation projects is often to increase top-line revenue by enabling more intelligent decisions, improving customer relationships, or empowering sales teams. Alternatively, businesses may look to reduce operational overhead by increasing efficiency, designing products that get better with time, or enabling a data-driven discipline.

Despite the business case appeal and the easy access to advanced technologies like data analytics, AI (artificial intelligence), or IoT (Internet of Things), some digital initiatives may not live up to their potential. A fundamental challenge is anchored in the current IT landscape realities: many different databases, often multiple systems of record, and multiple ERP solutions that facilitate the processes that enable businesses to run and operate. Furthermore, each technology solution has its own proprietary and complex data model; data is stored and often protected in silos, and the skills and expertise to make sense of the data within these systems are often held by just a few.

In these dynamic times, the cloud has proven to be an essential enabler in keeping people connected and businesses running on a global scale, which also sheds light on the digital divide among consumers and businesses. While modern businesses have digitized their operations, the need for instant availability of IT resources enabled by the cloud has never been greater across industries. As such, more companies are speeding up their cloud transitions, which brings greater complexities in Data Management. Many large enterprises now run mission-critical applications across hybrid computing environments. Thus, beyond IT departments providing the connectivity between these solutions, there is a much larger challenge in translating the data held within these solutions into usable information for more than the data experts, but also for the business operators and decision-makers. For example, imagine the complexity in understanding how a procurement delay affects downstream manufacturing plans and, ultimately, the customer commitments that have been made on a theoretical lead-time assumption. This is difficult even in a singular ERP environment — and our reality is that many organizations are running on disparate ERP systems. As such, despite all the relevant data being captured, optimizing, and maximizing the outcomes on behalf of customers is nearly impossible.   

The data-driven enterprise is not just about our ability to capture and visualize the information from data sources. It’s about the transparency, comprehensibility, and relevance of the data models behind it. Without it, data fails to provide meaningful insights that empower action.

Consider these imperatives for realizing the true value of a data-driven enterprise.

Bring Order to Data Fragmentation: The first order in addressing data silos is to simplify the connecting of data sources, including ERP systems. With the democratization of data trend, business units not only create tremendous volumes of data, but also need to collect, store, and curate data to suit their own needs in disparate systems. A unified data environment addresses the inefficiencies and prevents errors from having multiple isolated versions of data. In short, connectivity among data sets and the ability to self-heal incorrect data in source systems are fundamental requirements for creating a clean version of the truth. Today, off-the-shelf data connectors for most common data sources give businesses a simple path to get started. Beyond the “flat” data, consider the application logic it lives in. A list of purchase orders is merely a list of purchase orders. The order type and downstream requests are much more important to understand the relative importance of the request: Is it a part to replenish inventory levels, or is it a raw material that is already late in enabling a manufacturing order? Designing solutions beyond the connectors and visualization layers is essential to enable data-driven decisions.

Boost Process Automation: Businesses must be agile and “do more with less.” This requires an emphasis on finding ways to remove inefficiencies, thus the appeal of process automation — the use of technology to automate repeatable, day-to-day tasks. For instance, with robotic process automation (RPA), companies have the opportunity to leverage AI, machine learning (ML), and other workflow technologies to augment a capacity-constrained workforce to drive productivity. For example, in procurement departments, the most common repetitive tasks include: material master creation, accounts payables, and general ledger posting within finance accounting.

Strengthen Self-Service Analytics and Capabilities: The trend toward self-service has accelerated in our current business environment. Self-service channels and platforms empower employees, customers, and partners to get what they need without relying on IT or business support. We’ve witnessed the importance of digital front-office operations, such as eCommerce and customer service apps to empower brands to stay connected and serve their customers. These digital channels generate tremendous volumes of customer interaction data, which requires adopting the right easy-to-use tools for businesses to gain faster access to data and glean insights about customers and their behaviors.

Current events underscore the need for speed and innovation to scale thinking — and this requires leveraging timely data for critical decision-making through high-quality analytics. Rather than diverting internal resources to build custom interfaces and solutions, the optimal path forward is through collaboration with trusted partners who can bring the expertise and software to simplify and ease data flows. To that end, businesses that invest in their ongoing digital transformation journey to become data-driven enterprises are well-positioned for a new era.

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