📊Graphical Representation: Graphics We Live By I (The Analytics Marathon)

Graphical Representation Series

In a diagram adapted from an older article [1], Brent Dykes, the author of "Effective Data Storytelling" [2], makes a parallel between Data Analytics and marathon running, considering that an organization must pass through the depicted milestones, the percentages representing how many organizations reach the respective milestones:

It's a nice visualization and the metaphor makes sense given that running a marathon requires a long-term strategy to address the gaps between the current and targeted physical/mental form and skillset required to run a marathon, respectively for approaching a set of marathons and each course individually. Similarly, implementing a Data Analytics initiative requires a Data Strategy supposed to address the gaps existing between current and targeted state of art, respectively the many projects run to reach organization's goals. 

It makes sense, isn't it? On the other side the devil lies in details and frankly the diagram raises several questions when is compared with practices and processes existing in organizations. This doesn't mean that the diagram is wrong, just that it doesn't seem to reflect entirely the reality. 

The percentages represent author's perception of how many organizations reach the respective milestones, probably in an repeatable manner (as there are several projects). Thus, only 10% have a data strategy, 100% collect data, 80% of them prepare the data, while at the opposite side only 15% communicate insight, respectively 5% act on information.

Considering only the milestones the diagram looks like a funnel and a capability maturity model (CMM). Typically, the CMMs are more complex than this, evolving with technologies' capabilities. All the mentioned milestones have a set of capabilities that increase in complexity and that usually help differentiated organization's maturity. Therefore, the model seems too simple for an actual categorization.  

Typically, data collection has a specific scope resuming to surveys, interviews and/or research. However, the definition can be extended to the storage of data within organizations. Thus, data collection as the gathering of raw data is mainly done as part of their value supporting processes, and given the degree of digitization of data, one can suppose that most organizations gather data for the different purposes, even if only a small part are maybe digitized.

Even if many organizations build data warehouses, marts, lakehouses, mashes or whatever architecture might be en-vogue these days, an important percentage of the reporting needs are covered by standard reports or reporting tools that access directly the source systems without data preparation or even data visualization. The first important question is what is understood by data analytics? Is it only the use of machine learning and statistical analysis? Does it resume only to pattern and insight finding or does it includes also what is typically considered under the Business Intelligence umbrella? 

Pragmatically thinking, Data Analytics should consider BI capabilities as well as its an extension of the current infrastructure to consider analytic capabilities. On the other side Data Warehousing and BI are considered together by DAMA as part of their Data Management methodology. Moreover, organizations may have a Data Strategy and a BI strategy, respectively a Data Analytics strategy as they might have different goals, challenges and bodies to support them. To make it even more complicated, an organization might even consider all these important topics as part of the Data or even Information Governance, or consider BI or Analytics without Data Management. 

So, a Data Strategy might or might not address Data Analytics at all. It's a matter of management philosophy, organizational structure, politics and other factors. Probably, having a strayegy related to data should count. Even if a written and communicated data-related strategy is recommended for all medium to big organizations, only a small percentage of them have one, while small organizations might ignore the topic completely.

At least in the past, data analysis and its various subcomponents was performed before preparing and visualizing the data, or at least in parallel with data visualization. Frankly, it's a strange succession of steps. Or does it refers to exploratory data analysis (EDA) from a statistical perspective, which requires statistical experience to model and interpret the facts? Moreover, data exploration and discovery happen usually in the early stages.

The most puzzling step is the last one - what does the author intended with it? Ideally, data should be actionable, at least that's what one says about KPIs, OKRs and other metrics. Does it make sense to extend Data Analytics into the decision-making process? Where does a data professional's responsibilities end and which are those boundaries? Or does it refer to the actions that need to be performed by data professionals? 

The natural step after communicating insight is for the management to take action and provide feedback. Furthermore, the decisions taken have impact on the artifacts built and a reevaluation of the business problem, assumptions and further components is needed. The many steps of analytics projects are iterative, some iterations affecting the Data Strategy as well. The diagram shows the process as linear, which is not the case.

For sure there's an interface between Data Analytics and Decision-Making and the processes associated with them, however there should be clear boundaries. E.g., it's a data professional's responsibility to make sure that the data/information is actionable and eventually advise upon it, though whether the entitled people act on it is a management topic. Not acting upon an information is also a decision. Overstepping boundaries can put the data professional into a strange situation in which he becomes responsible and eventually accountable for an action not taken, which is utopic.

The final question - is the last mile representative for the analytical process? The challenge is not the analysis and communication of data but of making sure that the feedback processes work and the changes are addressed correspondingly, that value is created continuously from the data analytics infrastructure, that data-related risks and opportunities are addressed as soon they are recognized. 

As any model, a diagram doesn't need to be correct to be useful and might not be even wrong in the right context and argumentation. A data analytics CMM might allow better estimates and comparison between organizations, though it can easily become more complex to use. Between the two models lies probably a better solution for modeling the data analytics process.

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Resources:
[1] Brent Dykes (2022) "Data Analytics Marathon: Why Your Organization Must Focus On The Finish", Forbes (link)
[2] Brent Dykes (2019) Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals (link)

𖣯Strategic Management: Strategy Definition (Part II: Defining the Strategy)

In a previous post an organization’s strategy was defined as a set of coordinated and sustainable actions following a set of well-defined goals, actions devised into a plan and designed to create value and overcome an organization’s challenges. In what follows are described succinctly the components of the strategy.

A strategy’s definition should start with the identification of organization’s vision, where the organization wants to be in the future, its mission statement, a precise description of what an organization does in turning the vision from concept to reality, its values - traits and qualities that are considered as representative, and its principles – the guiding laws and truths for action. All these components have the purpose at defining at high-level the where (the vision), the why (the mission), the what (the core values) and by which means (the principles) of the strategy.

One of the next steps that can be followed in parallel is to take inventory of the available infrastructure: systems, processes, procedures, practices, policies, documentation, resources, roles and their responsibilities, KPIs and other metrics, ongoing projects and initiatives. Another step resumes in identifying the problems (challenges), risks and opportunities existing in the organization as part of a SWOT analysis adjusted to organization’s internal needs. One can extend the analysis to the market and geopolitical conditions and trends to identify further opportunities and risks. Within another step but not necessarily disconnected from the previous steps is devised where the organization could be once the problems, risks, threats and opportunities were addressed.

Then the gathered facts are divided into two perspectives – the “IS” perspective encompasses the problems together with the opportunities and threats existing in organization that define the status quo, while the “TO BE” perspective encompasses the wished state. A capability maturity model can be used to benchmark an organization’s current maturity in respect to industry practices, and, based on the wished capabilities, to identify organization’s future maturity.

Based on these the organization can start formulating its strategic goals – a set of long-range aims for a specific time-frame, from which are derived a (hierarchical) set of objectives, measurable steps an organization takes in order to achieve the goals. Each objective carries with it a rational, why the objective exists, an impact, how will the objective change the organization once achieved, and a target, how much of the objective needs to be achieved. In addition, one can link the objectives to form a set of hypothesis - predictive statements of cause and effect that involve approaches of dealing with the uncertainty. In order to pursue each objective are devised methods and means – the tactics (lines of action) that will be used to approach the various themes. It’s important to prioritize the tactics and differentiate between quick winners and long-term tactics, as well to define alternative lines of actions.

Then the tactics are augmented in a strategy plan (roadmap) that typically covers a minimum of 3 to 5 years with intermediate milestones. Following the financial cycles the strategy is split in yearly units for each objective being assigned intermediate targets. Linked to the plan are estimated the costs, effort and resources needed. Last but not the least are defined the roles, management and competency structures, with their responsibilities, competencies and proper level of authority, needed to support strategy’s implementation. Based on the set objectives are devised the KPIs used to measure the progress (success) and stir the strategy over its lifecycle.

By addressing all these aspects is created thus a first draft of the strategy that will need several iterations to mature, further changes deriving from the contact with the reality.

🧭Business Intelligence: Trivia (Part II: General Issues in Business Intelligence)

Business Intelligence Series

Introduction

BI projects are noble in intent though many managers and data professionals ignore their implications and prerequisites – data quality (incl. availability), cooperation, maturity, infrastructure, adequate tools and knowledge.

Data Quality

The problem with data starts usually at the source - ERP and other information systems (IS). In theory the system should cover all the basic reporting requirements existing in an enterprise, though that's seldom the case. Therefore, basic reporting needs arrive to be covered by ad-hoc developed tools which often include MS Excel/Access solutions, which are difficult to integrate and manage across organization.

Data Quality (DQ) is maybe the most ignored component in the attempt to build flexible, secure and reliable BI solutions. DQ is based on the validation implemented in source systems and the mechanisms used to cleanse the data before being reported, respectively on the efficiency and effectiveness of existing business processes and best practices.

DQ must be guaranteed for accurate decisions. If the quality is not validated and reviewed periodically, users will be reluctant in using the reports! The reports must be validated as part of the UAT process. Aggregated BI reports need detailed reports that can be used for validation, while the logic and data need to be synchronized accordingly.

The quality of decisions is based on the degree to which data were understood and presented to the decisional factors, though that’s not enough; it's need also a complete perspective, and maybe that’s why some business users prefer to prepare and aggregate data by themselves, the process allowing them in theory to get a deeper understanding of what’s happening.

Cooperation

A BI initiative doesn’t depend only on the effort of a department (usually IT), but on the business as a whole. Unfortunately, the so called partnership is more a theoretical term than a fact, while managers’ and business users' involvement is often suboptimal. 

BI implementations are also dependent on consultants’ skills and the degree to which they understood business’ requirements, on team’s cohesion and other project (management) related prerequisites, respectively on knowledge transfer and training. 

Tools

Most of the BI tools available on the market don’t satisfy all business, respectively users’ requirements. Even if they excel in some features, they lack in others. Usually, more than one BI tool is needed to cover (most of) the requirements. When features are not available, or they are not mature enough, or they are difficult to learn, users will prefer to use tools they already know.

Another important consideration is that BI tools rely on data models, often inflexible from the point of the data they provide, lacking integrating additional datasets, algorithms and customizations. The overall requirements need to be considered more recently from the point of cloud computing technologies, which becomes steadily a requirement for nowadays business dynamics. 

Maturity 

Besides the fact that Capability Maturity Models (CMMs) are difficult to implement, organizations lack the knowledge of transforming data into knowledge, respectively in understanding data and evolving it further in wisdom and competitive advantage. 

Most of the fancy words used by salesmen to sell a product don’t become reality overnight. Of course, a BI tool might have the potentiality of fulfilling the various technical and nontechnical goals, though between a theoretical potentiality and harnessing the respective potential is a long road that need to be addressed at strategical, tactical and operational levels.

Infrastructure

Infrastructure refers to human and technical components and the way they interact in getting the job done. It's not only about "breaking habits" and using the best tools, but in aligning people and technologies to the desired level of performance, of retaining and diffusing knowledge. 

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