🗄️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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🗄️Data Management: Data Culture (Part III: A Tale of Two Cities)

One of the curious things is that as part of their change of culture organizations try to adopt a new language, to give new names to things, try to make distinction between the "AS IS" and "TO BE" states, insisting how the new image will replace the previous one. Occasionally, they even stress how bad things were in the past and how great will be in the future, trying to depict the future in vivid images. 

Even if this might work occasionally, it tends to confuse people and this not necessarily because of the language and the metaphors used, or the fact that same people were in the same positions, but the lack of belief or conviction, respectively half-hearted enthusiasm personified by the parties. To "convert" people to new philosophies one needs to believe in them or mimic that in similar terms. The lack of conviction can easily have a false effect that spreads within the organization. 

Dissociation from the past, from what an organization was, tends to increase the resistance against the new because two different images are involved. On one side there’s the attachment to the past, and even if there were mistakes made, or things didn’t go optimally, the experiences and decisions made are part of the organization, of the people who made them. People as individuals and as an organization should embrace their mistakes and good deeds altogether, learn from them, improve what is to improve and move forward. Conversely, there’s the resistance to the new, to the change, words they don’t believe in yet, the bigger picture is still fuzzy in their minds, and there can be many other reasons that don’t agree with one’s understanding. 

There are images, memories, views, decisions, objectives of the past and people need to recognize the road from what it was to what should be. One can hypothesize that embracing one’s mistake and understanding, the chain of reasoning from then and from now will help an organization transition towards the new. Awareness of one’s situation most probably will help in the transition process. Unfortunately, leaders and technology gurus tend to depict the past as negative, creating thus more negative emotions, respectively reactions in the process. The past is still part of the people, of the organization and will continue to be.

Conversely, the disassociation from the past can create more resistance to the new, and probably more unnecessary barriers. Probably, it’s easier for the gurus to build the new if the past weren’t there! Forgetting the past would be an error because there are many lessons that can be still useful. All the experience needs to be redirected in new directions. It’s more important to help people see the vision of the future, understand their missions, the paths to be followed and the challenges ahead, . 

It sounds more of a rambling from a psychology course, though organizations do have an image they want to change, to bring forth to cope with the various challenges, an image they want to reflect when needed. There are also organizations that want to change but keep their image intact, which leads to deeper conflicts. Unfortunately, changes of image involve conflicts that can become complex from what they bring forth.  

A data culture should increase people’s awareness of the present, respectively of the future, of what it takes to bridge the gap, the challenges ahead, how to embrace change, how to keep a realistic perspective, how to do a reality check, etc. Methodologies can increase people’s awareness and provide the theoretical basis, though walking the path will be a different story for everyone. 

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🧭Business Intelligence: Perspectives (Part X: The Top 5 Pains of a BI/Analytics Manager)

Business Intelligence Series

1) Business Strategy

A business strategy is supposed to define an organization's mission, vision, values, direction, purpose, goals, objectives, respectively the roadmap, alternatives, capabilities considered to achieve them. All this information is needed by the BI manager to sketch the BI strategy needed to support the business strategy. 

Without them, the BI manager must extrapolate, and one thing is to base one's decisions on a clearly stated and communicated business strategy, and another thing to work with vague declarations full of uncertainty. In the latter sense, it's like attempting to build castles into thin air and expecting to have a solid foundation. It may work as many BI requirements are common across organizations, but it can also become a disaster. 

2) BI/Data Strategy

Organizations usually differentiate between the BI and the data Strategy because different driving forces and needs are involved, even if there are common goals, needs and opportunities that must be considered from both perspectives. When there's no data strategy available, the BI manager is either forced to address thus many data-related topics (e.g. data culture, data quality, metadata management, data governance), or ignore them with all consequences deriving from this. 

A BI strategy is an extension of the business, data and IT strategies into the BI knowledge areas. Unfortunately, few organizations give it the required attention. Besides the fact that the BI strategy breaks down the business strategy from its perspective, it also adds its own goals and objectives which are ideally aligned with the ones from the other strategies. 

3) Data Culture

Data culture is "the collective beliefs, values, behaviors, and practices of an organization’s employees in harnessing the value of data for decision-making, operations, or insight". Therefore, data culture is an enabler which, when the many aspects are addressed adequately, can have a multiplier effect for the BI strategy and its execution. Conversely, when basic data culture assumptions and requirements aren't addressed, the interrelated issues resulting from this can prove to be a barrier for the BI projects, operations and strategy. 

As mentioned before, an organization’s (data) culture is created, managed, nourished, and destroyed through leadership. If the other leaders aren't playing along, each challenge related to data culture and BI will become a concern for the BI manager.

4) Managing Expectations 

A business has great expectations from the investment in its BI infrastructure, especially when the vendors promise competitive advantage, real-time access to data and insights, self-service capabilities, etc. Even if these promises are achievable, they represent a potential that needs to be harnessed and there are several premises that need to be addressed continuously. 

Some BI strategies and/or projects address these expectations from the beginning, though there are many organizations that ignore or don't give them the required importance. Unfortunately, these expectations (re)surface when people start using the infrastructure and this can easily become an acceptance issue. It's the BI manager's responsibility to ensure expectations are managed accordingly.

5) Building the Right BI Architecture

For the BI architecture the main driving forces are the shifts in technologies from single servers to distributed environments, from relational tables and data warehouses to delta tables and delta lakes built with the data mesh's principles and product-orientation in mind, which increase the overall complexity considerably. Vendors and data professionals' vision of how the architectures of the future will look like still has major milestones and challenges to surpass. 

Therefore, organizations are forced to explore the new architectures and the opportunities they bring, however this involves a considerable effort, skilled resources, and more iterations. Conversely, ignoring these trends might prove to be an opportunity lost and eventually duplicated effort on the long term.

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🧭Business Intelligence: Data Culture (Part II: Leadership, Necessary but not Sufficient)

Business Intelligence Series

Continuing the idea from the previous post on Brent Dykes’ article on data culture and Generative AI [1], it’s worth discussing about the relationship between data culture and leadership. Leadership belongs to a list of select words everybody knows about but fails to define them precisely, especially when many traits are associated with leadership, respectively when most of the issues existing in organizations ca be associated with it directly or indirectly.

Take for example McKinsey’s definition: "Leadership is a set of behaviors used to help people align their collective direction, to execute strategic plans, and to continually renew an organization." [2] It gives an idea of what leadership is about, though it lacks precision, which frankly is difficult to accomplish. Using modifiers like strong or weak with the word leadership doesn’t increase the precision of its usage. Several words stand out though: direction, strategy, behavior, alignment, renewal.

Leadership is about identifying and challenging the status quo, defining how the future will or could look like for the organization in terms of a vision, a mission and a destination, translating them into a set of goals and objectives. Then, it’s about defining a set of strategies, focusing on transformation and what it takes to execute it, adjusting the strategic bridge between goals and objectives, or, reading between the lines, identifying and doing the right things, being able to introduce a new order of things, reinventing the organization, adapting the organization to circumstances.

Aligning resumes in aligning the various strategies, aligning people with the vision and mission, while renewal is about changing course in response to new information or business context, identifying and transforming weaknesses into strengths, risks into opportunities, respectively opportunities into certitudes, seeing possibilities and multiplying them.

Leadership is also about working on the system, addressing the systemic failure, addressing structural and organizational issues, making sure that the preconditions and enablers for organizational change are in place, that no barriers exist or other factors impact negatively the change, that the positive aspects of complex systems like emergence or exponential growth do happen in time.

And leadership is about much more - interpersonal influence, inspiring people, Inspiring change, changing mindsets, assisting, motivating, mobilizing, connecting, knocking people out of their comfort zones, conviction, consistency, authority, competence, wisdom, etc. Leadership seems to be an idealistic concept where too many traits are considered, traits that ideally should apply to the average knowledge worker as well.

An organization’s culture is created, managed, nourished, and destroyed through leadership, and that’s a strong statement and constraint. By extension this statement applies to the data culture as well. It’s about leading by example and not by words or preaching, and many love to preach, even when no quire is around. It’s about demanding the same from the managers as managers demand from their subalterns, it’s about pushing the edges of culture. As Dykes mentions, it should be about participating in the data culture initiatives, making expectations explicit, and sharing mental models.

Leadership is a condition necessary but not sufficient for an organizations culture to mature. Financial and other type of resources are needed, though once a set of behaviors is seeded, they have the potential to grow and multiply when the proper conditions are met. Growth occurs also by being aware of what needs to be done and doing it day by day consciously, through self-mastery. Nowadays there are so many ways to learn and search for support, one just needs a bit of curiosity and drive to learn anything. Blaming in general the lack of leadership is just a way of passing the blame one level above on the command chain.

Resources:
[1] Forbes (2024) Why AI Isn’t Going To Solve All Your Data Culture Problems, by Brent Dykes (link)
[2] McKinsey (2022) What is leadership? (link)

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🧭Business Intelligence: Data Culture (Part I: Generative AI - No Silver Bullet)

Business Intelligence Series

Talking about holy grails in Data Analytics, another topic of major importance for an organization’s "infrastructure" is data culture, that can be defined as the collective beliefs, values, behaviors, and practices of an organization’s employees in harnessing the value of data for decision-making, operations, or insight. Rooted in data literacy, data culture is an extension of an organization’s culture in respect to data that acts as enabler in harnessing the value of data. It’s about thinking critically about data and how data is used to create value. 

The current topic was suggested by PowerBI.tips’s webcast from today [3] and is based on Brent Dykes’ article from Forbes ‘Why AI Isn’t Going to Solve All Your Data Culture Problems’ [1]. Dykes’ starting point for the discussion is Wavestone's annual data executive survey based on which the number of companies that reported they had "created a data-driven organization" rose sharply from 23.9 percent in 2023 to 48.1 percent in 2024 [2]. The report’s authors concluded that the result is driven by the adoption of Generative AI, the capabilities of OpenAI-like tools to generate context-dependent meaningful text, images, and other content in response to prompts. 

I agree with Dykes that AI technologies can’t be a silver bullet for an organization data culture given that AI either replaces people’s behaviors or augments existing ones, being thus a substitute and not a cure [1]. Even for a disruptive technology like Generative AI, it’s impossible to change so much employees’ mindset in a so short period of time. Typically, a data culture matures over years with sustained effort. Therefore, the argument that the increase is due to respondent’s false perception is more than plausible. There’s indeed a big difference between thinking about an organization as being data-driven and being data-driven. 

The three questions-based evaluation considered in the article addresses this difference, thinking vs. being. Changes in data culture don’t occur just because some people or metrics say so, but when people change their mental models based on data, when the interpersonal relations change, when the whole dynamics within the organization changes (positively). If people continue the same behavior and practices, then there are high chances that no change occurred besides the Brownian movement in a confined space of employees, that’s just chaotic motion.  

Indeed, a data culture should encourage the discovery, exploration, collaboration, discussions [1] respectively knowledge sharing and make people more receptive and responsive about environmental or circumstance changes. However, just involving leadership and having things prioritized and funded is not enough, no matter how powerful the drive. These can act as enablers, though more important is to awaken and guide people’s interest, working on people’s motivation and supporting the learning process through mentoring. No amount of brute force can make a mind move and evolve freely unless the mind is driven by an inborn curiosity!

Driving a self-driving car doesn’t make one a better driver. Technology should challenge people and expand their understanding of how data can be used in different contexts rather than give solutions based on a mass of texts available as input. This is how people grow meaningfully and how an organization’s culture expands. Readily available answers make people become dull and dependent on technology, which in the long-term can create more problems. Technology can solve problems when used creatively, when problems and their context are properly understood, and the solutions customized accordingly.

Unfortunately, for many organizations data culture will be just a topic to philosophy about. Data culture implies a change of mindset, perception, mental models, behavior, and practices based on data and not only consulting the data to confirm one’s biases on how the business operates!

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Resources:
[1] Forbes (2024) Why AI Isn’t Going To Solve All Your Data Culture Problems, by Brent Dykes (link)
[2] Wavestone (2024) 2024 Data and AI Leadership Executive Survey (link)
[3] Power BI tips (2024) Ep.299: AI & Data Culture Problems (link)

🧭Business Intelligence: A Software Engineer's Perspective I (Houston, we have a Problem!)

Business Intelligence Series

One of the critics addressed to the BI/Data Analytics, Data Engineering and even Data Science fields is their resistance to applying Software Engineering (SE) methods in practice. SE can be regarded as the application of sound methods, methodologies, techniques, principles, and practices to obtain high quality economic software in a reproducible manner. At minimum, should be applied SE techniques and practices proven to work, for example the use of best practices, reference technologies, standardized processes for requirements gathering and management, etc. This doesn't mean that one should apply the full extent of SE but consider a minimum that makes sense to adopt.

Unfortunately, the creation of data artifacts (queries, reports, data models, data pipelines, data visualizations, etc.) as process seem to be done after the principle of least action, though least action means here the minimum interaction to push pieces on a board rather than getting the things done. At high level, the process is as follows: get the requirements, build something, present results, get more requirements, do changes, present the results, and the process is repeated ad infinitum.

Given that data artifact's creation finds itself at the intersection of two or more knowledge areas in which knowledge is exchanged in several iterations between the parties involved until a common ground is achieved, this process is totally inefficient from multiple perspectives. First of all, it takes considerably more time than planned to reach a solution, resources being wasted in the process, multiple forms of waste being involved. Secondly, the exchange and retention of knowledge resulting from the process is minimal, mainly on a need by basis. This might look as an efficient approach on the short term, but is inefficient overall.

BI reflects the general issues from SE - most of the issues can be traced back to requirements - if the requirements are incorrect and there's no magic involved in between, then one can't expect for the solution to be correct. The bigger the difference between the initial and final requirements elicited in the process, the more resources are wasted. The more time passes between the start of the development phase and the time a solution is presented to the customer, the longer it takes to build the final solution. Same impact have the time it takes to establish a common ground and other critical factors for success involved in the process.

One can address these issues through better requirements elicitation, rapid prototyping, the use of agile methodologies and similar approaches, though the general feeling is that even if they bring improvements, they don't address the root causes - lack of data literacy skills, lack of knowledge about the business, lack of maturity in planning and executing tasks, the inexistence of well-designed processes and procedures, respectively the lack of an engineering mindset.

These inefficiencies have low impact when building a report occasionally, though they accumulate and tend to create systemic issues in what concerns the overall BI effort. They are addressed locally by experts and in general through a strategic approach like the elaboration of a BI strategy, though organizations seldom pay attention to them. Some organizations consider that they are automatically addressed as part of the data culture, though data culture focuses in general on data literacy and not on the whole set of assumptions mentioned above.

An experienced data professional sees more likely the inefficiencies, tries to address them locally in his interactions with the various stakeholders, he/she can build a business case for addressing them, though it depends on organizations to recognize that they have a problem, respective address the inefficiencies in a strategic and systemic manner!

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🗄️Data Management: Data Quality (Part I: Information Systems' Perspective)

Data Management Series

One LinkedIn user brought to attention the fact that according to top IT managers the top two reasons why CRM investments fail is: (1) managing resistance within the organization; (2) bad data quality.

The two reasons are common not only to CRM or BI solutions but also to other Information Systems, though from the two data quality has usually the biggest impact. Especially in ERP systems the data quality continues to be a problem and here are a few reasons:

• Processes span different functions and/or roles, each of them maintaining the data they are interested in, without any agreement or coordination on the ownership. The lack of ownership is in general management’s fault.
• Within an enterprise many systems arrive to be integrated, the quality of the data depending on the quality and scope of the integrations, whether they were addressed fully or only superficially. Few integrations are stable and properly designed. If stability can be obtained in time, scope is seldom changed as it involves further investments, and thus the remaining data need to be maintained manually, respectively the issues need to be troubleshooted or let accumulate in the backlog.
• There are systems which are not integrated but use the same data, users needing to duplicate their effort, so they often focus on their immediate needs. Moreover, the lack of mappings between systems makes data analysis and review difficult. 
• The lack of knowledge about the systems used in terms of processes, procedures, best practices, policies, etc. Users usually try to do their best based on the knowledge they have, and despite their best intent, the systems arrive to be misused just to get things done. 
• Basic or inexistent validation for data entry in each important entry point (UI, integration interfaces, bulk upload functionality), system permissiveness (allowing workarounds), stability and reliability (bugs/defects).
• Inexistence of data quality control mechanisms or quality methodologies, respectively a Data and/or Quality Management strategy. If the data quality is not kept under review, it can easily decrease over time. 
• The lack of a data culture and processes that support data quality.
• People lack consistency and/or the self-discipline to follow the processes and update the data as the processes requires it and not only the data to move to the next or final step. Therefore, the gap between reality and the one presented by the system is considerable.
• People are not motivated to improve data quality even if they may recognize the importance of doing that.

Data quality is usually ignored in BI projects, and this is because few are the ones that go and search for the causes, making it easier to blame the BI solution or the technical team than to do something. This is one of the reasons for which users are reticent in using a BI solution, to which add up solution’s flexibility and the degree up to which the solution satisfies users’ needs. On the other side BI solutions are often abused, including also reports which have OLTP characteristics or of providing too much unstructured or inadequate content that needs to be further reworked.

Data quality comes on the managers' agenda, especially during ERP implementations. Unfortunately, as soon as that happens, it also disappears, despite being warned of the consequences poor data quality might have on the implementation and further data use. An ERP implementation is supposed to be an opportunity for improving the data quality, though for many organizations it remains in this state. Once this opportunity passes, organizations need more financial and human resources to reach a fraction from the opportunity missed.

The above topics are complex and need further discussion (see [1], [2]).

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Written: Nov-2008, Last Reviewed: Mar-2024

Resources:
[1] SQL-Troubles (2010) Data Management: Data Quality - An Introduction (link)
[2] SQL-Troubles (2012) Data Migration: Data Quality’s Perspective I - A Bird’s-Eye View (link)