Part 4 in the 6-part “Data-Driven Enterprise“ series, which examines the challenges, leadership requirements, measurement models, and best practices to become a data-driven enterprise
The journey to unlock the value of data requires business leadership just as much as data platforms. Pushing for ever more automation in data-to-action challenges the enterprise’s existing culture, processes, and decision logic. Data-to-action is thereby first and foremost more about rethinking processes, roles, and KPIs than connecting bits and bytes for new business insights.
Analyzing past failures has confirmed that the challenge of turning data into action is very different from the earlier process-optimization initiatives. In an analysis assessing the traits defining leaders and laggards in digital transformation in its 2019 Global Survey, McKinsey found the following themes to be strongly correlated with starting and succeeding in becoming a data-driven enterprise:
- Make data available. The survey points to the importance of getting data out of silos and into advanced analytics-based tools, as well as into the hands of decision-makers and even external partners across the supply chain. To do this well often requires reconfiguring organizational processes to allow the rapid sharing of data. Examples include setting up a data marketplace; building technical infrastructure; making use of automation to identify, catalog, and manage data at scale; and employing common querying and visualization tools across the enterprise to support widespread data use.
- Treat data as a product with real return on investment. Business leaders often view data as a raw material that supports analytics and decision-making. Instead, they should treat data as an internal product to be packaged and distributed to groups across the enterprise. Just as with consumer products, a manager’s remit is to create multiple revenue streams across channels, segments, and markets. The owner of each data domain should serve as the data product manager and have his or her performance tied to revenue, satisfaction, quality, and other similar measures.
- Take an agile approach to data transformation. High-performing companies have adopted data-culture practices more often than other survey respondents. Yet even high performers have room to grow. While nearly two-thirds of respondents at high-performing companies say their companies report effectiveness at encouraging employees to use data for making daily decisions, only 13% say they were very effective. Rather than tackling this gap all at once and risk creating large-scale disruptions, companies must focus on evolving their data cultures and competency incrementally. They can do so by ensuring that new and existing hires are educated in the use of data and analytics and by consistently communicating from the C-suite the importance of applying these tools every day.
In line with the McKinsey survey, SAP is seeing enterprises tackle data-to-action projects with equal importance to deciding how to launch a new service or enter new markets. There are defined clear goals, designated resources, investment in business design, and risk analysis. The objective is to secure success and ensure that data is accepted as the critical path to obtain new levels of business performance and competitive differentiation.
Building a data-driven organization
SAP helps customers achieve this new way of operating with the five-step methodology outlined below. The steps help enterprises identify the area to target and then outline all the key dimensions to ensure the transformation will obtain the targeted (data) outcomes and corresponding changes to business and IT.
The diagram below shows the necessary dimensions to analyze for enterprises to determine the business areas that will result in the ROI by being data-driven.
With the evidence from successful enterprises in unlocking the data promise, SAP sees that the chief transformation officer is becoming the more frequent title, rather than chief data officer – signifying the criticality of business leadership for driving culture change and process change alongside data platform investments.
Read the other posts in the Data-Driven Enterprise series here.