In conversations with our research network and advisory clients, a central theme commonly surfaces: how to maximize the value of data and analytics throughout the entire organization every day. This journey takes on trendy names and headlines such as “becoming a data-enabled organization,” “becoming an analytics-driven leader,” or even “empowering data democratization and literacy.” Make no mistake; whatever you want to call it, it’s a big aspiration to transform a company culture from being focused on their expertise in delivering industry products and services to becoming a flexible, data and analytics-driven company.
But, as with all lofty goals, it can start with planting the right seeds and building upon incremental value-adding successes. In this blog, let’s break down some essential concepts and then plant some seeds.
Why Now: Becoming a data-enabled organization starts with understanding why it’s essential to do so and then conveying a sense of urgency to get it done. In the past several years, many companies have begun a digital transformation – often including the adoption of advanced analytics, like machine learning and AI – with an eye toward future business models. However, with ongoing global disruptive forces, companies realized the vital need for having trusted data to be agile and resilient in an ever-changing business environment. Business teams rely on the latest data to explore, understand, and predict what’s going on and make critical decisions for business continuity and customer relationships. Organizations now see the imperative of business teams being hands-on with their data to reveal urgent insights, and, as a result, analytics has come to the forefront within data-enabled organizations.
The New Way: Organizations realized their dependence on well-understood and trusted data as the foundation for making faster decisions with more confidence, and business-enabled data mastering and curating tools gained adoption as a result. While IT and data management professionals have relied on a traditional master data management approach to program business rules for critical data, their business outcome was to improve operational efficiency in business processes through data consistency among operational systems. Yet this approach falls short of the needs of analytics; mastering data sets for business decision-making and analytics is different from getting operational systems to agree on a common data element among applications. Furthermore, when the primary users are business subject matter experts and not developers, a much different approach and user experience are required. Intelligent data hubs that recognize and account for these needs and concepts provide a significant accelerator for organizations taking steps towards being data-enabled and analytics-driven.
“Becoming a data-enabled organization starts with understanding why it’s essential to do so and then conveying a sense of urgency.”
Getting Started: Radiant Advisors counsels organizations to focus initially on understanding their particular needs when taking a business-led, decision-making-oriented approach to mastering data. This will allow business teams to be more agile and set proper expectations for how mastered data will align with the context and correct usage. Through the implementation of platforms such as Semarchy xDM, organizations have a holistic view of their data across all applications and datasets, increasing their data enablement and business insights.
Specifically, we recommend:
- Be clear about the desired business outcome or analytics capability. The mastering data process is faster and more accurate with a clear understanding of how people will use the data. Quality data will begin with a proper definition that ensures uniqueness and then defines relationships to other entities or their position in a hierarchy. Related data elements can then be added to support the intended outcome.
- Be clear about your data needs and prioritize them. Many organizations quickly lose control of their data scope because all business data in systems can seem related. When you begin with defining the data needed for decision-making within a particular business area, it is easier to identify the initial data needed and to make it available more quickly.
- Be clear in defining levels of quality that are acceptable and achievable. Decisions and analytics can work with varying levels of data quality in completeness, accuracy, precision, and timeliness. However, when companies fail to state clear acceptance parameters or criteria, any data with a defect can be considered untrustworthy – therefore undermining the perceived quality of the whole data set in use and resulting in analytics output. Clearly stating the acceptable levels beforehand will establish confidence the data is fit for use.
Takeaway: Data-enabled organizations will relentlessly focus on the data mindset they need everyone to have to achieve business outcomes, and that’s a good thing. By focusing on desired outcomes, prioritized data needs, and quality expectations, these three foundational “seeds” will produce a healthy environment for confident decision-making with data and analytics.