How to Achieve Data Management Maturity
8 September 2020
A Q&A with Melanie A. Mecca, Chief Executive Officer, DataWise
The scope of information that the UN defines, produces and uses is vast and complex. In June, the UN published the Secretary-General’s "Data Strategy for Action by Everyone, Everywhere" – a roadmap for data-driven change in the Organization.
In this Q&A, Melanie A. Mecca, CEO of DataWise, explores fundamental best practices for designing and implementing a mature data ecosystem that can advance strategic data and information goals. Throughout the article, the term Enterprise Data Management (EDM) is used to refer to the essential program foundations for success.
Should organizations care about data management?
Since data is essential to every business, organizations depend on access to the right data, available at the right time, and in a satisfactory condition for fact-based analysis and conclusions. There are many disciplines, processes and practices  required to accelerate and sustain maximum value from data assets. The core pillars of EDM, however, can be summarized as:
- Data Architecture – what you build or buy to capture, store and deliver data, and how the data is organized, shared and provisioned
- Data Governance – how you define, control, and make collaborative decisions about shared data
- Data Quality – how you define, evaluate, and improve the condition of data according to dimensions (measurements), such as completeness, accuracy, timeliness, etc.
To meet the UN’s full data potential, the Data Strategy roadmap includes data actions and workstreams to build these essential pillars. We’ll return to them in the context of decision-making, but let’s set a marker – no organization that I’ve encountered currently excels in all three areas, because the concept of ‘data as a strategic asset’ is relatively recent.
In “Business at the Speed of Thought” Bill Gates stated: “How you gather, manage and use information determines whether you win or lose.” I think we can all agree that the UN must win. However, for many years, data was an afterthought. This is counter-intuitive; data is the POINT – a critical asset.
With the advent of data warehousing for better reporting, master data management to integrate highly shared data, new technologies emphasizing user empowerment, and the explosion of data volumes in the last decade, today organizations realize that data management is a vital and permanent function. Therefore, much like the management of other core business areas such as Finance and Human Resources, organizations need to manage data, practically speaking, forever.
Since most organizations are evolving their technologies, staff education, and culture to manage data well, we can examine three successive broad accomplishments crucial for success – Capability, Maturity, and Deployment.
Capability consists of doing the right things. To build capabilities, an organization should first assess the current state of data management processes – identify major gaps in capabilities, starting with persistent problems. For example, a major gap may be the need for consistent business terms for shared data across an organization in a manner that can be easily aggregated (for instance, this capability is frequently required for Master Data Management solutions).
Many important capabilities and strengths have already been defined and established - some Member States and organizations have implemented proven, effective data management programs, polices, processes and education over the years. Successful approaches and corresponding work products can be leveraged through the Centers of Excellence envisioned in the UN Data Strategy to benefit the UN family and assist the UN to standardize core data management processes, over time, for all Member States and organizations. Implementing pilot projects can also be an efficient way to establish best practices, and high priority data use case actions are excellent starting points for improving data management processes. If approached strategically, gains will be cumulative, and work products can be baselined and reused for each successive data action.
Maturity consists of formalizing and embedding improvements in capabilities, such that they become 'business as usual'. Formalization consists of mandating a well-defined set of capabilities and processes, promulgating them through policies, supporting them with staff resources, and implementing a compliance program to ensure stability, resiliency, and implementation across the enterprise. Maturity practices are commonly employed elsewhere in an organization, such as in planning, resourcing, leading, standardizing, etc. – they should also be applied to data management processes. With increased maturity, the need for compliance processes is recognized (especially for highly shared data), and that is dependent upon robust, operationalized data governance.
Maturity practices ensure that the capabilities are followed, even during stressful conditions. An organization can evolve through a ‘minimum viable set’ approach, by determining what capabilities are most critical right now for high priority data actions and then extending the scope over time.
Deployment measures the implementation extent of capability and maturity practices across the organization, according to a planned sequence. For instance, the UN COVID-19 Data Hub initiative has developed ready-to-use templates, a standardization mechanism for capturing and reporting COVID-19 data, to accelerate the time to identify insights for national statistical offices. This approach and its corresponding work products can be leveraged for other medical data collection and reporting important for UN Member States and organizations. As capabilities are defined and become mature, they can eventually be implemented across all relevant entities of an organization.
What is the relationship between data maturity and improved decision-making within an organization?
Better data, better decisions – it’s as simple as that.
Let’s consider the worldwide COVID-19 pandemic. The crisis has spotlighted the criticality of timely and accurate data that conforms to agreed-upon standards for an array of metrics—for case counts to the percentage of survivors who experience lasting health challenges. If Member States do not agree on key concepts, such as whether death of a COVID-infected individual is recorded as a heart attack or caused by the virus, a full picture of the pandemic’s spread and effects is not possible, decisions will be less trustworthy, and guidance issued to combat the disease and protect life may be late or insufficiently informed.
As the Data Strategy notes, one of the UN’s top priorities is the enablement of expanded analytics capabilities, and user empowerment for self-service statistical analysis, data visualization, and cutting-edge technologies such as artificial intelligence and machine learning. The diagram below is illustrative of the obstacles that organizations must overcome to leverage advanced capabilities and solutions, based on a Forbes 2016 survey of data scientists.
Note that 'cleaning and organizing data' accounts for 60% of the time analytics teams spend preparing for their modeling and mining activities. If you add the 19% spent 'collecting data sets,' only 16% of their time is left for mining data, refining algorithm, and building training sets. These time consuming tasks need to be minimized to empower the skilled, creative and timely analysis that an organization counts on.
Therefore, in addition to implementing enabling technologies, hiring and training analysts and data scientists, and educating users to ask the right questions and employ self-service analytics tools, the organization must manage its data assets well to accelerate the speed of insight and empower informed decisions.
What are three best practices or tips for achieving data maturity?
To achieve data maturity, organizations should consider recognizing, establishing, and growing two permanent functions – centralized data management and federated data governance, illustrated in the diagram below: