The five key Requirements for a successful SAP data management strategy

6 min read
Oct 25, 2022


In our last blog article on data management and the DART methodology, we discussed the importance of Actions—or strategies—on enterprise data management strategy. Understanding what Drivers are exerting pressure on your business can help shed light on what Actions successful enterprises are leveraging to manage rapidly increasing data volumes.

Today, I’d like to introduce the concept of Requirements, what end capabilities and features businesses are prioritizing, and how managed service providers like Pythian can help you better navigate your data management journey.


Business Requirements within the DART model

Having already covered Drivers and Actions, Requirements are the next component of SAPinsider’s DART methodology—the research framework it uses to analyze trends in enterprise data management. Within this model, Requirements represent the people, processes, and capabilities an organization needs for its data management strategies to succeed. Simply, requirements support the effectiveness of data management Actions.


The five key Requirements 

In its 2022 poll, SAPinsider observed five critical Requirements that SAP professionals were prioritizing in the near future. These are especially pertinent as enterprises and IT departments consider approaches for future S/4 HANA migrations. 


Data archiving policy and procedures (89%)

Data archiving is the practice of shifting infrequently accessed data to low-cost storage repositories. Usually, the goal of creating and executing against an archiving policy is to reduce costs on ‘warm’ storage while retaining old (or ‘cold’) data needed for future reference or analysis, and information needed for regulatory compliance.

The right tools and procedures around data archiving support the broader enterprise data management strategy, improve analytic performance and reduce storage costs. For this reason, it comes to as little surprise that nearly all IT professionals are focusing on fulfilling this Requirement. As organizations face massive increases in the volume of data they ingest, create, and store, the importance of this Requirement will only continue to grow.


Reduction in cost of IT (88%)

It isn’t surprising that eighty-eight percent of respondents noted that cost efficiencies and/or savings are a critical Requirement of their data management strategy. In general, most business strategies search for organizational efficiency—or activities that lead to the discovery of said efficiencies—that result in higher market share, revenues, and investments.

Many IT departments would consider hybrid system strategies (highest priority Action at 50% by poll respondents), deploying modern data tools (37%), and other data management Actions to serve the Requirement of reducing overall IT costs in the long run. These savings are unlocked through the newfound optimizations in analytics, database performance, and data orchestration that S/4HANA and modern applications, tools, and platforms can deliver for today’s enterprise.


Meeting governmental and industry-specific regulations (88%)

Especially prevalent in finance and healthcare, governmental and industry-specific regulations play an essential role in the success of nearly all data management Actions. With eighty-eight percent of respondents noting this Requirement as a priority, its significance cannot be understated. 

When building hybrid enterprise databases, migrating databases and data warehouses to the cloud, or implementing a data archiving strategy, enterprises should establish a robust data governance culture. When pursuing these Actions, they should be mindful of where transactional and non-transactional data is stored, how their data governance policy must evolve to complement their digital transformation, and how global and industry regulations impact the migration of datasets to the cloud. 


Access control for critical data (86%)

The purpose of data access control is to manage how users interact with your company’s data while ensuring the data is secure, private, and compliant. Access controls for critical enterprise data usually address security, compliance, data quality, and process efficiency. As a result, it supports numerous strategies, including centralized master repositories on SAP HANA, cloud and hybrid migrations, and the deployment of various data management, integration, and orchestration tools. 


Supporting both SAP and non-SAP data sources (85%)

As organizations face larger data volumes to support their global operations, databases with SAP and non-SAP data have become increasingly common. More organizations than ever are turning to S/4 HANA for greater functionality with SAP Master Data Governance (MDG), taking advantage of high-speed data consolidation, real-time insights and analytics, and advanced search functionality. A robust master data governance approach underpins data quality and integrity and is thus essential to most Actions enterprises may take on their data management journey.


Practical examples of Requirements in data management 

A hypothetical example of Requirements within the DART model

In this example, a consumer health organization may experience competitive pressures to make more data available to internal teams and customers (the Driver). The business looks to leverage an organization-wide cloud architecture and data governance policy as core data management strategies (the Action). However, for this strategy to succeed, the business must ensure it complies with governmental and regional data privacy laws (the Requirement). 

In this case, the success of the data management strategy hinges on the business’s compliance with data privacy laws. Without absolute compliance, the business is open to extreme legal and organizational risk.


A real-world example of Requirements within the DART model

In a real-world Pythian example, I’ll refer back to a large global retailer that underwent an ecommerce evolution and turned to Pythian for support. The large retailer’s legacy ECC systems proved unable to scale with rising customer demand—20,000 orders a day—due to manual tracking process, outdated reports, and spreadsheet presentation layers. 

The amount of data collection continued to increase, and outdated reports that took two hours to pull meant internal decision-makers were missing out on the most recent information. To facilitate real-time insights with greater agility, the organization sought a partner with experience and expertise. 

The retailer faced many of the business pressures (Drivers) poll respondents identified as challenges: teams are seeking real-time data for better decision-making (46%), greater access and visibility to business-wide trends and analytics (32%), and want to better manage larger data volumes as they grow (21%).

Pythian worked with the retailer to develop an ETL process that sends their data to a warehouse in Google Cloud, then into Google’s BigQuery. The data is set up daily with near real-time feeds, creating easy-to-read shipping and warehousing Tableau dashboards. Critical business insights are delivered reliably every fifteen minutes. 

We can equate this to the ‘migrating to cloud databases and data warehouse’ as the Action taken, which thirty percent of enterprise respondents in SAPinsider’s poll noted as a focus. To ensure that the Action of ‘migrating to cloud databases and data warehouses’ was successful, Pythian and the retailer prioritized the following Requirements:

  • The reduction in the cost of IT: by enabling near-real-time insights from Google Cloud and creating easily digestible reports—even during peak time—the retailer’s warehousing team could focus on improving numbers, efficiencies, and improving customer shipping experience.  
  • Access control for critical data: easily assigning and revoking access to the right employees within Google BigQuery and Tableau allows them to review sensitive transactional customer data in a more streamlined, accessible way.


Takeaways: how Requirements impact enterprise data management strategies

As we’ve highlighted earlier in this series, the DART model is not a prescriptive methodology. Instead, it is a model that helps draw connections between what pressures organizations face, what strategies they implement, how they measure success, and what tools get them there. However, that does not mean we cannot tap into the wealth of knowledge gathered from this fantastic community of SAP experts and make some key recommendations.

When considering a holistic SAP data management strategy and the essential Requirements that dictate success, we must focus on the following:

  • A strong data governance policy cannot be ignored. Great data quality—what enterprises strive for—is synonymous with great data governance. An excellent data governance program clearly defines user access controls for the data and how it is maintained and secured. Don’t delay prioritizing governance at every step of your journey with front-end workflows and automation. This is especially true with master data, which has become more challenging in our world of increasing data volumes, new data sources, business processes, and stakeholders.
  • Have business and technology teams team up to tackle data archiving and tiering. Ballooning data volumes are a concern for most enterprises on—or looking to—SAP environments today. Faced with decreasing data quality, more frequent security risks, and increasing storage costs in the cloud, more organizations have begun investing in data archiving and tiering. By bringing both IT and business teams in to evaluate dynamic data tiering and archiving, your business can better align on what success looks like. Is SAP HANA’s native dynamic tiering sufficient, or should a third-party option be investigated? Could much of your data live in cost-effective ‘cold’ storage? 

Comment below if you enjoyed this blog; I would appreciate your feedback. In our final blog in the series, I will touch on the Tools essential to fulfilling these key Requirements—and ultimately how they can be used to maximize the value of your data and SAP environment.

If you want to read the entire research report published by SAPinsider, you can download it now.

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