An Outcome-Driven Enterprise Data Strategy: Organization And Governance

Part of the Enterprise Data Strategy series, which explores the importance of leadership and accountability in directing an overall data strategy tied to business outcomes.

If “data is the new soil” (as the data journalist David McCandless said), then organizational structure and governance are the irrigation of an outcome-driven enterprise data strategy. Both are essential for building a solid strategy and effectively managing your most critical enterprise data. And in a time when organizations increasingly depend on data for every aspect of their business, you can’t afford not to have an information game plan.

In Part 1 of this series, Tina Rosario and I explored why an enterprise data strategy was important, what it looks like, and what the key elements are. In this post, I’ll dive into the organization and governance component. We all know these words, but in the context of an outcome-driven enterprise data strategy, what do they really mean?

Organization and governance lay the foundation for all other aspects of a data strategy (hence the irrigation analogy) and define:

  • Data scope: master, transactional, operational, analytical, big data, etc.
  • Organizational structure: roles and responsibilities between accountable owner, head of data, IT, business team, and executive sponsor
  • Data standards and policies: guideposts that outline what you’re managing and governing and to what outcome
  • Oversight and metrics: parameters for measuring strategy execution and success

Why are organization and governance important?

In the Big Data and AI Executive Survey 2019 by NewVantage Partners, only 7.5% of respondents cited technology as the roadblock to becoming data-driven. Conversely, 93% of them listed people and processes as a critical obstacle to adoption, along with lack of organizational alignment (40.3%) and cultural resistance (24%).

Every business transformation requires accountable roles and responsibilities with a champion to lead the change. It also requires a culture shift from viewing data management as a boring low-level job to one of extreme importance. If employees touch data – especially critical data – and if they create it, change it, use it, move it around in some way, they need to understand the role they play in properly maintaining that data and take accountability.

A business outcome-based enterprise data strategy helps drive that culture shift because it communicates broadly – in simple business terms – what data matters most. It changes the perception that data is a back-office job of little importance to one that is tied to successful business outcomes. And the organization and governance component of the strategy defines the who (critical roles); what (change management needed, including how to measure accountability); and how (roadmap) of achieving your goal.

This is also where you define the data standards and policies for data quality, architecture, security/privacy/ethics, and CRUD (create, read, update, and delete), to name a few. These guideposts are then used to define how you’ll execute in each of the other areas of your strategy, like detailing the process for how data is created to these standards.

What are the keys to success?

Everything under organization and governance is key (that all-important irrigation), so let’s focus on what’s often overlooked when building organization and governance:

  1. Defining a reasonable, targeted scope: You’ll always have more data work than budget and resources can cover. It’s a fact of business life in today’s data-rich world. Hence, scoping a realistic, doable roadmap – along with what will not be covered – is crucial. And make sure to do the scoping and expectation-setting in partnership with your business executive sponsor. Don’t do it alone.
  1. Securing a business executive sponsor: You need a leader to visibly champion the data strategy and actively advocate and communicate the strategy to the broader organization. The sponsor enforces accountability, models the desired data mindset, and helps arbitrate data issues between business units. And guess what? You can have more than one executive sponsor, creating a “Friends of Data” circle, further facilitating that culture shift.
  1. Defining business value metrics: Establishing how to measure and communicate the value of your data strategy – in business terms – helps ensure business engagement, commitment, and ongoing funding.

How do you get started?

Data transformation happens when an organization understands the value of data, the role it plays in making business decisions, and the importance of tying the data strategy to business outcomes. After all, you can’t have an outcome-driven enterprise data strategy without a business outcome.

  1. Prioritize which business outcome is most important (If you don’t have defined business outcomes, do that first.)
  1. Do the three steps outlined under keys to success, starting with securing a business executive sponsor
  1. Go after some quick wins first to establish credibility and validate the program, such as data-quality cleansing of the critical data that’s required or establishing dashboards that improve visibility
  1. Don’t forget the need for a culture shift

And of course, read the posts in the “Enterprise Data Strategy” series.

For more information

  • Gain a better understanding of your current organizational maturity with SAP’s executive-focused, next-generation Database and Data Management Assessment, which is based on top KPIs and best practices. Click on “Start Survey” and proceed to register.
  • Reach out to us (Maria for North America and Tina for everywhere else) to inquire about a 1:1 enterprise data strategy discussion

And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.

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