Part 2 in the 6-part “Data-Driven Enterprise” series examines the challenges, leadership requirements, measurement models, and best practices to become a data-driven enterprise.
ERP systems have continuously evolved. The boundaries of the enterprises have grown, and correspondingly, the capabilities of ERP systems have continued to grow, as illustrated below.
The explosion of data from social media, video, voice, and machine data, as well as the proliferation of new powerful technologies like machine learning (ML), IoT, blockchain, and chatbots, have led to huge data platforms that are built beyond the boundaries of ERP systems. Enterprises have invested in a multitude of (on-premise and cloud) platforms to analyze the vast amount of data now available on customers’ preferences, operational performance, asset reliability, etc. In effect, enterprises must now manage a much more complex IT environment that includes algorithms, platforms, applications, micro-services, and architectures.
As powerful data platforms have emerged, enterprises are facing several new challenges to establish a complete value chain from the ability to capture new insights to ensure that they are utilized optimally across the enterprise. Here are the three critical steps required:
Capture new data insights. ML-based models are never at rest. These systems are “self-learning,” and with the right data platform, the resulting insights get more and more advanced as data expands and evolves. Training and retraining ML models is challenging and requires continuous work.
Rely on powerful information management. New data insights lose their value very fast. To turn new insights into actions that are aligned with other processes often requires a very different integration platform that can secure immediate response to events and consumer inquiries.
Redesign business execution. Accelerating to near-real-time responses requires that processes, priorities, and cost parameters be codified to avoid unfulfilled promises, delays, or losses. Business execution systems need to be ever more automatic and even autonomous.
Enterprises that shift from being people- and process-driven to the trifecta of being people-, process-, and data-driven must undertake profound changes that span both the IT and business domains.
|IT enablers||Business enablers|
|Data platform: Identify and deploy a new technology stack that can tease out valuable new insights in the moment. Such a platform must be able to reliably and easily evolve with changes in data patterns.||Business process impact: Identify practices, policies, and processes that need to be redesigned to maintain operational integrity while ensuring a holistic execution that leverages new real-time insight.|
|Integration platform: Identify an information management platform that can stream new insights into the relevant execution systems in real-time with context to make it fit for operational use.||Business leadership: Promote and endorse the new practices and ensure that structures, KPIs, and incentives are amended to fit the new data-driven process|
The critical enabler for turning data into action is a technology platform that can virtually connect and harmonize all data assets, analyze different data in context, and feed insights into business processes. In this way, humans and machines can stay aligned, as data and processes are in sync.
There is often a discrepancy between how enterprises believe their customers feel about their products and services and how their customers actually feel. Enterprises typically have an immense amount of operational data (O-data) – like costs, revenues, and sales, which depict what is happening in the enterprise. But enterprises are increasingly collecting customers’ actual experience data (X-data) to understand the feelings of the people involved and why things are happening.
These two views are radically different: X-data provides the outside-in view, whereas O-data provides the inside-out view. SAP is offering a unique solution to combine X-data and O-data named SAP Experience Management. By matching the X-data and O-data, enterprises gain a radical new understanding of experience gaps – and can act to eliminate them.
Here’s a good example:
A large oil and gas company needed to create data transparency between a wide range of systems in order to obtain a full understanding of materials, orders, capacity, and cost developments across the global operations. Each of the execution systems maintained critical data to this end. However, due to the complex integration, the old platform only allowed aggregated insights, which made both global alignment and decisions difficult and iterative. It was not suitable for the needs of the business.
The company wanted one financial plan, one customer view, and one operational view with real-time connectivity. They also wanted an open and scalable platform that would not decrease performance as more data, connections, and analytics were performed. At the same time, they needed on-the-fly data normalization and lineage to source systems to execute on decisions, anytime and anywhere.
The company chose a modern technology platform with in-memory engines and powerful embedded analytics engines and rich integrations. It provides linkages to ERP systems, and the ability to convert and harmonize different source-data systems with different names, taxonomy, and hierarchies into one “golden record.” Yet lineage is not lost, ensuring that the business could take ownership and direct decisions at any level.
The company gained a quantum leap in operational financial transparency, customer sales and service, and inventory and operational performance. This has resulted in much better operational alignment and financial excellence. With global transparency, the company continues to build ever more advanced predictive models and simulations to empower the business to make optimal decisions based on both internal and external factors.
In Part 3, we’ll take a look at the IT lifecycle challenge.
Read Part 1 of the Data-Driven Enterprise series here.