Your organization, if it has reached certain thresholds of size or complexity, has data stakeholders at all levels and across all domains. A data stakeholder, defined broadly, is someone who uses, affects, or is affected by data; it could be a person, a group, a department, even a subsidiary corporation! When we discuss data stakeholders in the context of data intelligence, data governance, or data management, we tend to focus on people or entities who take action based on data, or who make decisions based on data, or take actions that affect the use and collection of data. In this blog post we will discuss the various data stakeholders and how they can be helped.
Another way to organize these data stakeholders is to think of them in these categories: data producer; data provider; data consumer.
Data producers are those who collect, obtain, store, and maintain data, or who cause data to be collected. Data producers make decisions, establish practices, train employees, and otherwise operationalize data for their organization.
Data providers tend to be those who extract data stored in various systems, transform or aggregate or otherwise manipulate it, and then deliver it in some format to a downstream recipient, whether that be a user within the organization, another system or data store, an external agency or body, etc.
Data consumers, as the name suggests, are among those downstream recipients. In some situations, data consumers interact directly with organizational data in a system of record, but as the amount, variety, and breadth of data collected and processed by organizations continues to grow, data consumers tend to interact with data that has gone through many levels of curation and layers of transformation.
Many organizations now find themselves at something of an inflection point when it comes to producing, providing, and consuming data.
Data producers have become involved in a whole different set of discussions: about integrations, downstream reporting, meeting increasingly broad or otherwise advanced compliance expectations, etc. They also face additional challenges just trying to do their traditional work of collecting, storing, and maintaining operational data. Many of them have outgrown legacy systems and tools, but few of them have the expertise to make confident choices about upgrading, overhauling, or replacing, to say nothing of doing this work in the face of financial restrictions or staffing challenges.
Data providers are increasingly expected to provide insight and discover opportunities for competitive advantage. It is not enough, if it ever was, to pull data together and deliver it to end users; today business analysts find themselves needing to dig deep into into business operations and terminology, and to get up to speed using a multitude of analytics tools and platforms; data engineers have to navigate dizzying new technical environments and also regulatory and governance needs; modelers must incorporate ever more sophisticated transformations and error-checking into their work.
It may be data consumers, however, whose historical relationship with data has seen the most upheaval in recent years. Gone, or at least rapidly disappearing, depending on industry and organization, are the days when consumers would be presented with a carefully selected and formatted set of data that demonstrated whether an organization was on track to meet its goals, or which tasks were up next, or (for more sophisticated organizations) which potential course of action seemed most appropriate given historical data. Data consumers, especially those at the higher ranks in an organization, expect (or are expected) to make more sophisticated data requests, to interact with data products (or data as a product) more directly, to draw inferences from curated data, and to make business decisions informed and underpinned by data analysis.
We have noted in this space before that data consumers have seen data resources and expectations shift substantially, and consumers themselves have not seen their skills and training opportunities shift at the same speed or to the same extent. But the same is true for providers and producers as well. Business intelligence teams that once focused on sophisticated report production and tuning the performance of data models now need to understand, operate in, and sometimes even create multi-cloud data fabrics! Data producers who could once rely on subject matter expertise within their domain to drive actions and decisions, now find themselves culling and selecting vastly increased amounts data on the fly, working in a (sometimes forced) collaborative model to establish sharing agreements, upgrade security/access standards, and overseeing multi-domain data flows/integrations.
Organizations face this explosion in the volume and variety of data. They face tremendous pressure to do more and to perform better using their data. They face a growing skills and data fluency gap. In these circumstances, what can be done?
First, organizations should remind themselves that this situation did not arise overnight, and there is no reason to think that one response right now will solve everything. As with any attempt to leverage a strategic asset, or to respond to market challenges or opportunities, priorities must be articulated, strategic and contingency plans must be created, and responsibilities for deployment and triage must be assigned. Remember that the goal here is to use data to make better decisions, to run your organization more effectively, to identify opportunities to be more efficient and competitive. Understanding and organizing your data might be a worthy objective in and of itself, but it is certainly more valuable in the service of decision support.
Second, it is almost certainly worth the time and effort to determine which data options are buzzwords, and which are buzzworthy. This will vary by organizational size, type, resources, staffing, industry, and so on. Some organizations can probably spend their way out of existing data challenges, and they may be able to weather the storm of a failed deployment or unproductive experiments. Some organizations may well have the staff, knowledge, culture, and general wherewithal to innovate their way to the next level of data and analytics maturity (by, for example, investing in best-of-breed systems and discovering in-house talent to take advantage of them). But many organizations will have to be precise in their diagnosis, realistic in their expectations, and surgical in their response so as to maximize return on investment.
Third, instituting robust data governance practices will not catch an organization up to its peers or competitors overnight, nor undo the lasting effects of years or decades of underinvestment. However, committing to and executing these practices will help stop the bleeding, so to speak, and will be foundational in efforts to manage data securely and effectively, to build and maintain an analytics and decision support pipeline, to align data operations with organizational strategy, and to create a sustainable environment where data producers, providers, and consumers (data stakeholders) can add value to the organization. Even the wealthiest and most innovative organizations will see benefit; indeed, in our experience, data governance and data intelligence has often secured a strong foothold in just those types of companies and institutions.
Our data intelligence solution, the Data Cookbook, helps meet the needs of your data stakeholders today, and it supports your data governance efforts for tomorrow and beyond. For providers, the Data Cookbook unites technical metadata and organizational know-how with business terminology and functional accountability, helping speed the progress from question to answer. For producers, the Data Cookbook brings their combined expertise together in a collaborative repository, and it helps free them from routine and repetitive tasks so that they can better position themselves to handle new data collection and maintenance challenges. For consumers, the Data Cookbook catalogs data products and helps guide their understanding of those products in a business context.
If you have not started this work, we believe you should do so, immediately. If you have started, think about increasing the pace, redoubling effort, or whatever it will take to solidify your data foundation and build for the future. And if you are one of the lucky few who are well down this road--well, do not get complacent. Your data stars can get poached, your top-of-the-line systems can age into irrelevancy, or your suppliers can go belly-up.
You can hire new people, provide training to existing employees, and make plans to replace employees who leave. You can purchase new data applications, and invest in modern architecture and updated infrastructure. If you have already done that, you can identify mistakes to avoid or bottlenecks to resolve next time around. You can create or revise policies regarding the use and role of data in your organization. If they already exist, then you can investigate whether they are current, how well they are observed, and who is (or is not) abiding by them, and how thoroughly.
In fact you should do all of these things! Not all at once, of course, and not right away. But what you can do right away is to begin to catalog your data assets, products, and resources. Know your data stakeholders: who they are, what they want, what they can do now and what they might be able to do with the right support. You can identify and better understand gaps and shortcomings, and you can create the roadmap to becoming truly data-enabled. Hope you enjoyed this blog post. For additional data governance / data intelligence resources (recorded webinars, blog posts and videos) then please check out our "Wealth of Resources on Data Governance/Data Intelligence Topics and Components" blog post.
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