Data Management: Data Culture (Part V: Quid nunc? [What now?])

Data Management Series

Despite the detailed planning, the concentrated and well-directed effort with which the various aspects of data culture are addressed, things don't necessarily turn into what we want them to be. There's seldom only one cause but a mix of various factors that create a network of cause and effect relationships that tend to diminish or increase the effect of certain events or decisions, and it can be just a butterfly's flutter that stirs a set of chained reactions. The butterfly effect is usually an exaggeration until the proper conditions for the chaotic behavior appear. 

The butterfly effect is made possible by the exponential divergence of two paths. Conversely, success needs probably multiple trajectories to converge toward a final point or intermediary points or areas from which things move on the "right" path. Success doesn't necessarily mean reaching a point but reaching a favorable zone for future behavior to follow a positive trend. For example, a sink or a cone-like structure allow water to accumulate and flow toward an area. A similar structure is needed for success to converge, and the structure results from what is built in the process. 

Data culture needs a similar structure for the various points of interest to converge. Things don't happen by themselves unless the force of the overall structure is so strong that allows things to move toward the intended path(s). Even then the paths can be far from optimal, but they can be favorable. Probably, that's what the general effort must do - bring the various aspects in the zone for allowing things to unfold. It might still be a long road, though the basis is there. 

A consequence of this metaphor is that one must identify the important aspects, respectively factors that influence an organization's culture and drive them in the right direction(s) – the paths that converge toward the defined goal(s). (Depending on the area of focus one can consider that there are successions of more refined goals.)

The structure that allows things to converge is based on the alignment of the various paths and implicitly forces. Misalignment can make a force move in other direction with all the consequences deriving from this behavior. If its force is weak, probably will not have an impact over the overall structure, though that's relative and can change in time. 

One may ask for what's needed all this construct, even if it doesn’t reflect the reality. Sometimes, even a not entirely correct model can allow us to navigate the unknown. Model's intent is to depict what's needed for a initiative to be successful. Moreover, success doesn’t mean to shoot bulls eye but to be first in the zone until one's skillset enables performance.

Conversely, it's important to understand that things don't happen by themselves. At least this seems to be the feeling some initiatives let. One needs to build and pull the whole structure in the right direction and the alignment of the various forces can reduce the overall effort and increase the chances for success. Attempting to build something just because it’s written in documentation without understanding the whole picture (or something close to it) can easily lead to failure.

This doesn’t mean that all attempts that don’t follow a set of patterns are doomed to failure, but that the road will be more challenging and will probably take longer. Conversely, maybe these deviations from the optimal paths are what an organization needs to grow, to solidify the foundation on which something else can be built. The whole path is an exploration that doesn’t necessarily match what is written in books, respectively the expectations!

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Data Management: Data Culture (Part IV: Quo vadis? [Where are you going?])

Data Management Series

The people working for many years in the fields of BI/Data Analytics, Data and Process Management probably met many reactions that at the first sight seem funny, though they reflect bigger issues existing in organizations: people don’t always understand the data they work with, how data are brought together as part of the processes they support, respectively how data can be used to manage and optimize the respective processes. Moreover, occasionally people torture the data until it confesses something that doesn’t necessarily reflect the reality. It’s even more deplorable when the conclusions are used for decision-making, managing or optimizing the process. In extremis, the result is an iterative process that creates more and bigger issues than whose it was supposed to solve. 

Behind each blunder there are probably bigger understanding issues that need to be addressed. Many of the issues revolve around understanding how data are created, how are brought together, how the processes work and what data they need, use and generate. Moreover, few business and IT people look at the full lifecycle of data and try to optimize it, or they optimize it in the wrong direction. Data Management is supposed to help, and it does this occasionally, though a methodology, its processes and practices are as good as people’s understanding about data and its use! No matter how good a data methodology is, it’s as weak as the weakest link in its use, and typically the issues revolving around data and data understanding are the weakest link. 

Besides technical people, few businesspeople understand the full extent of managing data and its lifecycle. Unfortunately, even if some of the topics are treated in the books, they are too dry, need hands on experience and some thought in corroborating practices with theories. Without this, people will do things mechanically, processes being as good as the people using them, their value becoming suboptimal and hinder the business. That’s why training on Data Management is not enough without some hands-on experience!

The most important impact is however in BI/Data Analytics areas - how the various artifacts are created and used as support in decision-making, process optimization and other activities rooted in data. Ideally, some KPIs and other metrics should be enough for managing and directing a business, however just basing the decisions on a set of KPIs without understanding the bigger picture, without having a feeling of the data and their quality, the whole architecture, no matter how splendid, can breakdown as sandcastle on a shore meeting the first powerful wave!

Sometimes it feels like organizations do things from inertia, driven by the forces of the moment, initiatives and business issues for which temporary and later permanent solutions are needed. The best chance for solving many of the issues would have been a long time ago, when the issues were still small to create any powerful waves within the organizations. Therefore, a lot of effort is sometimes spent in solving the consequences of decisions not made at the right time, and that can be painful and costly!

For building a good business one needs also a solid foundation. In the past it was enough to have a good set of products that are profitable. However, during the past decade(s) the rules of the game changed driven by the acerb competition across geographies, inefficiencies, especially in the data and process areas, costing organizations on the short and long term. Data Management in general and Data Quality in particular, even if they’re challenging to quantify, have the power to address by design many of the issues existing in organizations, if given the right chance.

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