David Crosby, of super group fame with Stills, Nash and Young, once said that you have to write about something that goes on in your life if you want to write something that means something to you.
Data wellness obviously means a lot to me. This article starts with something that is “going on” with me.
Like many people beginning in 2020, I had a new year resolution to get healthier and into better shape. I was well on my way and improvement came quickly and consistently. Until I hit a wall. I had changed behaviors to improve my health. But again, at first improvements came quickly. Then … nothing at all.
I consulted a trainer and a weight-loss program “expert.” They told me that I was burning too much, and not consuming enough, fuel. They told me that I had put my body into “starvation mode” and that my metabolism was slowed to a snail’s pace. Never in a million years did I dream that was the case.
Enough about me. Now let’s talk about data. Is your organization in “Data Starvation” mode? What exactly does that mean? And how does this relate to the phases your data leadership are going through to fend off starvation when it comes to their data and information assets?
Starvation leads to perishing, suffering, and lacking something that potentially and eventually leads to death. Starvation also can be defined as a strong desire or need for something. Many organizations recognize that they may be starving when it comes to data. Starvation can lead to the loss of customers, revenue, and even employees. When your organization starves for data, your people are not happy and that includes everybody from your Senior Leadership to the people that they depend on for efficient and effective sales, marketing, research, and operations (to name a few functions).
Data Starvation can be considered your organization’s inability to define, produce, and use your data for the betterment of the organization. Starvation can indicate that the consumers of your data, including (but not limited to) your staff, your partners and your customers, do not have access to the data they need; or do not have the confidence in the data they can access; or do not know what data exists or understand the data that is available to them; or recognize that improvements in how the data is managed will improve their efficiency and effectiveness – leading to improved performance for both them and the organization.
Using the health care and weight loss example shared above, I will presume that data starvation means that your organization is working (exercising) very hard to get as much value from your data as possible, but the value is not coming as quickly as you’d like— even though you have a good plan. Could that be because your “Data Metabolism” is very low? Let’s investigate further.
According to dictionary.com, metabolism is the sum of the (physical and chemical) processes in an organism by which its material substance is produced, maintained, and destroyed, and by which energy is made available. Consider that the organism is your organization. Consider that the material is data. Consider last that the energy is your organization making the best use and getting the most value from your data.
Allow me to make use of six commonly recognized stages of metabolism (of a drug). The phases discussed briefly here are related to the phases that your company’s leadership may go through when releasing itself from data starvation by increasing its data metabolism:
Consider the data that your organization creates or brings in from other sources. This data is the lifeblood of your organization. In order to maximize the value of this data, the organization must improve how the data is defined to improve understanding, produced in a quality manner, and used – which includes protected, accessed, shared, and analyzed to achieve maximum results.
The intake of data is so important that organizations focus on obtaining data from external sources when the internally produced data only covers a percentage of their needs. The intake of data – as it is being produced internally – becomes the focus of data quality efforts. Production improvements require data quality efforts focused on improvements to the most critical data first. Improvements in production are often dependent on the quality of data definition. Or, the creation of the metadata or data documentation that standardizes people’s understanding of the data, how it must be created and how it can be used.
Absorption focuses on assimilation and incorporation. When it comes to data, organization benefit greatly when their data becomes assimilated (adapted and adopted) and incorporated (united or combined to improve experience). Data absorption can also be improved through metadata and data documentation including the development of business glossaries, data dictionaries, and data catalogs.
The focus on the age-old practice of metadata management is back and stronger than ever. Books on the subject are popular, webinars more popular (my Dataversity webinar in February 2020 on the subject last month attracted close to 1500 registrants), articles are getting viewed (on TDAN.com) and conference sessions on the subject are extremely well attended.
Absorption can also refer to improvements in data leadership’s knowledge and ability to support, sponsorship, and understand why formal governance of data is necessary. In the past, leadership has been able to ignore or put off the emphasis of governing data as (perhaps) the most valuable asset of the organization. There is an increased emphasis in Data Literacy and Data Leadership (check out this link to read what my friend Anthony Algmin says about the importance of Data Leadership “absorbing” why improved data management is no longer a “nice to have”) which is all about absorption. Absorption is a critical phase of Data Leadership that leads to improvements in Data Metabolism.
The distribution of data is key to increasing your data metabolism to fend off data starvation. Distribution of the data to stakeholders is imperative to maximizing the value the organization gets from its data. People at all levels require data is make key decisions and this includes all people at the executive, strategic, tactical and operational levels. It is difficult to argue with anything in this paragraph.
However, distribution – when it comes to boosting data metabolism – is all about the sharing of intellectual-capital across data leadership. In today’s distributed environments, it is not enough to have a single person, or even a single division, that supports the idea of investing in data and information management to overcome data starvation.
The very first best practice around formalizing data governance across an organization is that Senior Leadership, however it is defined by your organization, must support, sponsor and understand what it means to formally govern data and the activities of governing data as a valuable asset. Without support, sponsorship and understanding, formal data governance and data management will always be at risk within your organization. Understanding how to distribute the knowledge and the passion for formal data governance across your leadership is a critical step to metabolism health and staving off data starvation.
Interaction is a major phase in an organization’s attempt to enhance their data metabolism. In physics, interaction is the direct effect that one substance has on another inducing the emission or absorption of one substance by another. In the management of data, interaction addresses the requirement of effective communications across an organization to induce change on people’s behavior when it comes how people define, produce, and use data.
Interactions, when it comes to data, include communications with Senior Leadership to improve their support, sponsorship and understanding of the need and actions to formally govern data. Interaction with people at the strategic level includes the engagement of a team of high-level representatives of all organizational functions similar to the Executives, but includes the intake and prioritization of data improvement opportunities. Interaction with people at the tactical level includes engagement of subject matter experts to address opportunities specific to their areas of knowledge, expertise and authority. At the operational and support levels, interaction includes the appointment of working teams (and everybody) focused on specific initiatives that require involvement of people on the front lines of data issue resolution.
The effect of successful interactions, and the engagement of the appropriate people, includes orientation, onboarding and ongoing communications of the requirements, value and results of improved data management and data governance practices. Interaction between people is a critical component of the successful governance of data as represented in the data governance framework that I share often. Putting somebody formally in charge of defining, developing and delivering thorough and well thought-out communications to improve the interactions associated with improving organizational data competence is a critical success factor.
Breakdown, in terms of a metabolism process, focuses on decomposition and analysis. Breakdown, in terms of electricity, means passing through or passing between something. The term breakdown, in terms of the concepts of this article, means the simplification of the actions that are required by an organization to hold off data starvation and improve its data metabolism.
Simplification can be as modest as breaking the governance and management of data into manageable components— namely the data, the roles, the processes, communication, metrics, and tools required to improve organizational capabilities. These six components are the focus of many organizations when it comes to standing up formal data governance and evaluating the best approach to take to achieve the management of data as a valuable organizational asset.
The term elimination has many meanings but mainly focuses on the removal, omission and eradication of something. In the context of this article, I will focus on the elimination of bad habits. The first step toward the elimination of bad habits is to understanding best practice and to evaluate your present position in comparison to best practice. Many organizations consider conducting a best practice assessment to recognize what can be leveraged in the existing environment and address where there are differences between best practice and how their organization presently performs.
The results of a best practice assessment are often converted into a well-thought out roadmap to eliminate the gaps, reduce the risk, and spell out the steps that are required to achieve improvements in maturity and achieve sustainable long-term success. The elimination of bad habits, for example poor diet selections and the lack of exercise (as mentioned at the beginning of this article), often lead to poor results for people and organizations.
This article began with a story about me and my “get-healthier” initiative. I now understand that to get where I want to be, I must not starve myself because I need to keep up a healthy level of metabolism. And I know that to achieve my goal, that it is not going to happen quickly or without a lot of time and effort. I have already committed to following through with my plan and I am ecstatic to say that it is still working.
The same can be said about all of the organizations I work with. Organizations are taking steps to alleviate their data starvation situations by improving their data metabolism. These terms are not commonly used in the data management and data governance arenas. But maybe they should be!
I am not the first to say this … but “a journey starts with the first step.” The first step to eliminating any level of data starvation is by working to improve your data metabolism. I am very interested in what you think about this incredibly long analogy.
Get healthy people! Get data healthy organizations! You’ve got to eat – umm er, you have got to take action and expect it will take some time to get the results you crave.