By Pedro Cardoso, BackOffice Associates
The importance of data as an enterprise asset is now well understood. On any given day it seems we hear about the latest innovations in Big Data and how world-class organizations are mining vast troves of data to obtain actionable intelligence, leading to deep customer insights, improved business processes and higher profits.
While there is no doubt that Big Data is here to stay and is a reality that many organizations need to deal with, Gartner’s well known "Hype Circle" places Big Data at or around the "Peak of Inflated Expectations." ( This means it will be followed soon with a visit to the Trough of Disillusionment.)
Years of traditional ERP and business intelligence (BI) transformation initiatives have left the vast majority of organizations still struggling to achieve the elusive single version of the truth (SVOT). Fragmented and loosely coupled IT systems, department-specific point solutions and complex heterogeneous landscapes that are a patchwork of legacy software have resulted in the proliferation of poorly governed, difficult-to-integrate data islands.
To deal with this challenge, some organizations have attempted system consolidation or standardization approaches to leverage moving to a new ERP system as the catalyst for standardizing enterprise data and delivering standardized reporting. After years of effort, however, many organizations are realizing that the ROI they expected is not being realized. Associated costs and business impact are significantly higher than projected, and business outcomes often go unmet.
An alternative approach to the ERP/system centric approach is a "Data First" strategy, which can not only deliver enterprise-wide common data models and business value much sooner, but also de-risks other strategic initiatives that would otherwise need to address data challenges within their own work streams. This approach also imposes fewer resource and change management challenges for departments and businesses.
What Is a 'Data First' Strategy?
At its core, this strategy is about closing the knowledge gap that exists in your organization today. It begins by understanding the key strategic and operational questions that require answering, and for which in the current state answers simply don’t exist or are expensive to obtain using ad-hoc data collection and Excel spreadsheets as a single version of “someone’s” truth.
With many initiatives starting in the finance domain, standardizing an enterprise business budgeting, planning and consolidation capability is a common use case out of the gate. By using data consolidation and harmonization platforms that offer data stewardship capabilities to the business, a core foundation of common, harmonized, accurate and trustworthy data can be enabled.
Such data stewardship platforms can actually sit alongside your departmental and enterprise centric process/transaction stack, integrating data quality and harmonization solutions into your client-facing business processes. To support this shift, Gartner says global IT spend is increasing by 3.2 percent in 2014, with spend on database and data stewardship integration platforms growing even more significantly—making this area the single largest enterprise software market in 2014.
Why Is a 'Data First' Strategy Effective?
Starts with the End. This may seem obvious, but if you start with what you need from a data and information model perspective, ignoring restrictions of your current application- and ERP-specific data models, you will encounter far less resistance compared to system-centric discussions.
Having spent many years in the trenches of business intelligence solution delivery, I can tell you the pain of building data models to support reporting requirements, only to discover that the models were not sustainable or realistically attainable within a BI landscape. With the target information model in hand, it is now a matter of performing a fit/gap data assessment on your source systems to flesh out requirements for configuring your data harmonization and stewardship platform.
Moves Data Management from an Event to a Process. Standing up a data governance council, establishing a data stewardship community and enabling the required governance processes around data standards and attribute definitions are all examples of activities that need to take place in a "Data First" initiative. I can’t tell you how many companies that have recently implemented new systems and ERP solutions end up lamenting that their data quality challenges have continued to persist and sometimes eclipse the issues that existed in their legacy environment.
All too often organizations consider data management something you "do" to satisfy a different need. For example, a data migration plan may include steps with data cleansing and data transformation requirements -- but without changing behaviors, systems alone will not result in the sustaining of quality data. A stewardship-enabled integration platform can be created for the purposes of a migration but would also persist in the solution landscape to ensure ongoing data quality and application of data governance.
Lower Risk, Early Reward. When an organization tries to "boil the ocean" and go with a combined system and data consolidation effort across the broad base of their enterprise platforms and solutions, risk abounds. Both from an organizational change management perspective as well as the more pragmatic resource draw and requirements on the business, the appetite for such big cost and multi-year "waterfall" approaches to solution delivery simply isn’t there.
Organizations going through change want to see results sooner – and early wins from taking the "Data First" approach means that as each business entity is harmonized (e.g., vendor, customer, material), parallel activities can support moving from a federated model to establishing authoritative master data sources within the enterprise.
Separated Attention To Data. When developing your harmonized enterprise data model, the only thing that matters is getting this right. When this activity sits within a traditional system consolidation project plan, the only thing that matters is the plan, which in this case is all about system consolidation and making sure that occurs.
Separated attention to data also means the deployment of a specific talent pool of data analysts and experts to develop the strategy and approach. This requires a team that is business facing and business process focused, with technology and technical implementation details playing a back seat to the more critical discussion of business strategy to operational alignment of information requirements.
People and Process Focused. Studies have shown that approximately three quarters of business re-engineering efforts fail to achieve the expected outcomes, with a big reason being the failure to align the technical solution, not just to the business process but also to the requirements of the end user and organizational cultures. Stewardship-oriented data management platforms offer the benefit of an incremental approach, to deploy the right solution to your business users required today while providing the foundation for frictionless extending of functionality and controls required in the future.
With a harmonized data model approach, the reality of achieving a single version of the truth is possible much faster and more predictably, with less organizational and business risk and in a sustainable fashion. This is not to suggest that ERP and landscape consolidation initiatives do not make sense; they most certainly do. Rather, the intent of this article was to introduce you to the concept of "Data First" and enabling an enterprise-harmonized view much sooner.
In practice, the two strategies would be mixed. A strategy of system consolidation would run in parallel, but decisions being made in that stream of activity can be separated from the data story as harmonization is still being achieved, independent of where the box may be sitting in the ecosystem.
Pedro Cardoso is a senior information and data governance consultant at BackOffice Associates, a provider of information governance and data migration solutions, focusing on helping Fortune 1000 customers manage one of their most critical assets – data.