Conceptual Frameworks for Information Science Initiatives

Notice: All figures within the following sections have been created by the creator of this text.

Frequent Framework Varieties

Though conceptual frameworks are available many various sizes and shapes, 4 fundamental framework sorts stand out as being particularly widespread in information science initiatives: hierarchies, matrices, course of flows, and relational maps. We are going to briefly go over every of those framework sorts under.

Hierarchies

Hierarchical frameworks typically take the type of tree diagrams, beginning with a root node and ending with a number of leaf nodes, as proven in Determine 1. For instance, the foundation node might characterize an overarching idea in a taxonomy or an preliminary binary query in a call tree. A node’s place within the hierarchy (or tree) provides us useful details about its relationship to different nodes. Though Determine 1 labels the gadgets within the hierarchy as “ideas,” they are often any form of entity. Entities could also be impartial (e.g., ideas, subjects, segments) or have some constructive or unfavourable valence (e.g., revenues, prices, issues, points). The hierarchical construction can range in depth and breadth.

In visible representations of hierarchies, vertical hyperlinks between two entities are usually drawn explicitly and could be non-directional (easy traces) or directional (downward or upward arrows, relying on the movement of the connection). Against this, horizontal hyperlinks between entities on the similar stage of a hierarchy are usually not proven explicitly. Identical-level entities could also be topic to a pure ordering (e.g., temporal or spatial), which could be proven by inserting them accordingly within the framework. As an example, entities that happen earlier in an ordering must be positioned to the left of entities that happen later. If the entities don’t include a pure ordering, you possibly can nonetheless contemplate ordering them indirectly (e.g., by stage of significance or precedence) to assist reasoning. Entities on the similar stage in a hierarchy ought to usually even be on the similar stage of abstraction.

In lots of conditions, it helps if the nodes of a hierarchy are mutually unique and cumulatively exhaustive, or MECE (pronounced “me-see”), to a big extent. Being mutually unique implies that the ideas represented by particular person nodes don’t have any main overlaps (i.e., no redundancies), whereas being cumulatively exhaustive implies that the framework leaves out nothing essential. A MECE hierarchy could be helpful for breaking down a broad idea into sub-concepts (or elements) to determine key drivers of the entire.

Matrices

A matrix is a tabular information construction consisting of n rows and m columns. Information scientists engaged on tabular use circumstances routinely leverage matrices for storing coaching information and mannequin weights. Coaching machine studying fashions can yield high-dimensional matrices of weights that seize advanced relationships between predictors and targets. Low-dimensional matrices just like the one proven in Determine 2 could be helpful for analyzing issues and speaking key insights.

The generic two-by-two matrix proven in Determine 2 compares two completely different dimensions towards one another. Such a matrix naturally yields 4 quadrants. By conference, the bottom-left quadrant (the place each dimensions are “low”) is often the undesirable area of the matrix, and the top-right quadrant (the place each dimensions are “excessive”) represents the fascinating area. For instance, the market analysis agency Gartner makes use of two-by-two matrices to investigate the aggressive panorama in numerous trade sectors and calls the top-right area of the matrix (the place the market leaders are plotted) the “magic quadrant.”

The size of a matrix might characterize steady, ordinal or categorical information sorts. Ideally, these dimensions (or axes) must be essential to the overarching framework goal indirectly (e.g., key sub-concepts, issues, or drivers in a given context). The interactions between these dimensions must be of explicit curiosity as a supply of perception, since it’s these interactions that matrices can seize properly.

Basically, the MECE precept additionally applies to the selection of dimensions — they need to collectively cowl the essential sub-concepts or drivers of the issue being investigated and keep away from redundancies. In any other case, trying on the interplay will likely be no completely different from a person dimension. If analyzing the interplay is just not essential, a hierarchical framework could also be extra appropriate. Changing between a matrix framework and its hierarchical analog could be easy. As an example, to remodel the matrix in Determine 2 right into a hierarchy, create a root node that defines the general context, let its youngster nodes be Dimensions 1 and a pair of, and let their respective youngster nodes be “excessive” and “low.”

Course of Flows

A course of movement defines a sequence of logically ordered actions that work together to realize an overarching goal. As an example, instruments resembling Dataiku and KNIME enable customers to assemble information science pipelines as course of flows, going from information ingestion all the way in which to modeling and report technology. Determine 3 depicts a generic course of framework.

The entities of the method in Determine 3 are labeled as actions, however these could possibly be steps, phases, operations, and so on. The method begins with an exercise (Exercise 1), ends with an exercise (Exercise 3), and has a number of actions in between (Exercise 2). Some inputs are usually fed into the method in the beginning and reworked over the sequence of actions to yield an output. Notice that inputs and outputs can even enter and go away at intermediate steps throughout the course of.

As with hierarchies and matrices, the MECE precept could be essential in formulating the completely different actions of the method. If two actions have vital conceptual overlap, you might contemplate both grouping them right into a single exercise or breaking them up right into a extra granular set of distinct actions. As an example, the intermediate actions in Determine 9 might have resulted from this form of evaluation; Exercise 2 could possibly be the end result of merging some overlapping actions, whereas Actions 2.1-2.3 could possibly be a granular breakdown of a particular subset of these merged actions. If an exercise or a bigger a part of the method repeats, then it may be represented as a cycle, whereby an exercise transitions to a different exercise that has already occurred earlier than.

The transition from one exercise to a different ought to meaningfully rework the inputs of the method (e.g., by growing, decreasing, combining or in any other case altering the inputs indirectly) with the goal of manufacturing the specified output. If a transition doesn’t change the inputs, then the 2 actions on both facet of the transition are possible redundant and must be merged or break up up otherwise, as mentioned above.

Relational Maps

Relational maps shift the main focus from particular person ideas (or entities) to the relationships between them. Information scientists working with data graphs or box-and-arrow “path diagrams” of causal relationships (as proven in Determine 4) will likely be conversant in this framework sort.

A relationship can usually be any perform that hyperlinks two completely different ideas collectively. 4 kinds of relationships are particularly widespread:

• Transactional: A relationship can characterize a number of transactions between entities. The transactions might contain the movement of tangible issues (e.g., merchandise purchased and offered) or intangible issues (e.g., data, cash). Transactional relationships can incorporate directionality; a transaction can movement from A to B, from B to A, or in each instructions, and every of those circumstances has a special that means for the entities (e.g., they could be receivers, senders, or each).
• Causal: Entities A and B could also be causally associated if A is accountable — at the very least partly — for the incidence or state of B (or vice versa). The character of the causal relationship might range. The function of A is robust if its presence is enough to completely trigger B (though A will not be the one entity that may totally trigger B). The function of A can also be sturdy whether it is essential to trigger B (though A might not be capable to do that alone). Furthermore, if A causes B, it doesn’t essentially comply with that B causes A; the notion of directionality is clearly essential for specifying causal relationships.
• Similarity-based: Entities could also be associated as a result of they’re comparable or dissimilar indirectly. For instance, entities A and B could be comparable as a result of they have an inclination to look in the identical place or occur on the similar time (and dissimilar if the incidence of 1 entity tends to preclude the incidence of the opposite). The notion of correlation is a mathematical formalization typically used to assemble measurable, similarity-based relationships. Notice that, simply because two entities are correlated doesn’t essentially imply that they’re causally associated (though if they’re causally associated, then they might even be correlated).
• Membership-based: Entities could be linked collectively by being members of the identical group, group, or class. As an example, folks could be associated by being in the identical neighborhood, grocery gadgets could be a part of the identical product class, and a set of sub-concepts could also be a part of an overarching idea. Certainly, one may apply a hierarchical framework to drill down into successively deeper ranges of membership inside entities into account.

The right way to Construct Your Personal Frameworks

The next three-step course of can be utilized to construct a customized framework:

1. Outline the framework’s goal.
2. Determine the precise constructing blocks (i.e., the framework sort and dimensions).
3. Put the constructing blocks collectively in an efficient method to reply the framework’s goal.

Step 1: Outline the Goal

In defining the framework’s goal, ask your self: In what context will the framework be used? What ought to the framework accomplish? Can an current framework be reused — maybe with some minor modifications — or does a brand new one must be constructed to suit your particular wants?

The development of the framework must be tied to a better purpose, such because the supply of a challenge, formulation of a call, or creation of some documentation. As soon as the context has been correctly understood, cautious consideration must be given to what the framework ought to accomplish in concrete phrases. Is the framework meant as a decision-making software? Is the framework meant to construction the movement of an argument in a report or a presentation?

Simply since you want a framework doesn’t imply that you should construct one your self. In lots of conditions, current conceptual frameworks could be reused with out vital modification. Spending some effort to keep up a stable, up-to-date overview of related current frameworks avoids downstream prices of “reinventing the wheel.” Reusing current frameworks has advantages past not having to begin from scratch; if the framework has been round for a while, its most important options, in addition to its strengths and limitations, could also be well-documented and examined in numerous settings. Platforms resembling In the direction of Information Science are an important supply for protecting abreast of conceptual frameworks associated to information science initiatives.

Step 2: Determine the Framework Sort and Dimensions

Having clarified the target of the framework, it’s time to assume extra concretely in regards to the development of the framework itself. One of many most important difficulties right here is that conceptual frameworks are inherently not as tangible as bodily ones (like molds in a manufacturing unit). We are inclined to intuit the hyperlink between type and performance — the framework and its goal — extra simply when the framework and its object are tangible. The hallmark of a very good conceptual framework is its capability to show a seemingly intangible argument or resolution into one thing extra tangible, and the important thing to that is illustration.

Broadly talking, there are two elements that decide the illustration of conceptual frameworks: the sort of the framework and the dimensions of the framework. You might be more likely to discover the framework sort first because it determines how the framework seems as an entire. The earlier sections coated the 4 widespread framework sorts. The framework dimensions dictate what the framework can particularly characterize (e.g., when it comes to granularity and ordering). By adjusting the scale, the identical framework sort could be reused to generate a variety of various insights. Following are three widespread courses of framework dimensions:

• Categorical: These dimensions encompass a finite set of discrete classes that totally describe the dimension. The classes needn’t be ordered (e.g., a set of merchandise, buyer segments, gender).
• Ordinal: These dimensions are ordered, which implies that you could analyze whether or not one thing is “lower than,” “larger than,” “equal to,” and so forth, in relation to one thing else (e.g., unfavourable/constructive, low/medium/excessive).
• Steady: Such dimensions can take the notion of ordinal dimensions to a way more granular stage. Being steady implies that the dimension is numerical and may embrace decimals (e.g., 1.23, -2.718, 3.14159).

Step 3: Put It All Collectively

As soon as the framework sort and dimensions have been recognized, they are often mixed to provide a customized framework. Typically, the identification and mixture steps should not explicitly separated, because you hardly ever do one with out the opposite. However the framework sort and its dimensions — the essential constructing blocks — should not essentially wedded to one another. Some combos might make extra sense than others, and you may usually combine and match the constructing blocks in some ways, over a number of iterations, till the framework feels proper. Be capable to spot and exploit this combinatorial flexibility is a vital ability that it’s best to begin growing from the outset of your framework-building journey.

Furthermore, there are broadly 4 “pathways of study” that seize the hyperlink between the framework and its goal:

• Descriptive: Approaches the framework’s goal by gathering and organizing previous data (e.g., utilizing visuals resembling graphs and tables, or written summaries). Doing so permits us to higher describe and analyze what occurred previously, however it might not essentially inform us why one thing occurred, or whether or not it is going to occur once more.
• Diagnostic: Takes descriptive data of previous occasions and goes a step additional to take a look at why one thing occurred. That is executed by drilling down into the info, mining for clues and correlations, and looking for a believable hyperlink between trigger and impact. As with the descriptive pathway, the main focus is on the previous.
• Predictive: Differs from the prior two by asking and answering questions in regards to the future. The main focus is on making an informed guess about what is going to occur sooner or later by counting on a number of usually quantitative methods that vary from the straightforward (e.g., fundamental likelihood idea, linear fashions) to the extra advanced (e.g., neural nets).
• Prescriptive: Goes past merely predicting future occasions to recommending methods to take care of them. The main focus is on determining how one can make one thing occur — or whether or not it ought to occur — sooner or later. The reasoning for the prescription could be quantitative (e.g., based mostly on statistics or simulation modeling) or qualitative (e.g., based mostly on private expertise).

Framework sorts and dimensions can subsequently be mixed in numerous methods to provide customized frameworks that lend themselves to descriptive, diagnostic, predictive, and prescriptive use circumstances.

Prime Suggestions

This part provides 5 suggestions for constructing good conceptual frameworks. The information are in no way an exhaustive checklist of the factors that it’s best to contemplate, however characterize a fundamental set of issues to remember.

Tip 1: Concentrate on the Goal and Viewers

The method of constructing frameworks broadly consists of three steps, specifically defining the target, then figuring out and mixing the constructing blocks (framework sorts and dimensions) accordingly. Whereas step one will, by its nature, emphasize the strategic goal and audience of the framework, the main focus within the latter two steps shifts to the nitty-gritty particulars of the framework’s constructing blocks. The deeper you get into the mechanics of the framework, the more durable it may be to keep up visibility of the unique goal. To keep up visibility of the larger image, it might probably assist to take a step again on occasion throughout the framework-building course of and remind your self of the strategic goal and audience. It could additionally assist to delay a part of the evaluation till the mandatory information turns into obtainable and to hunt common suggestions from colleagues and the audience of your framework the place attainable.

Tip 2: Hold It as Easy as Potential

To paraphrase a quote typically attributed to Albert Einstein — one of the crucial achieved builders of conceptual frameworks of the final century — we will say {that a} framework must be made so simple as attainable, however not less complicated. Because the course of inherently entails attempting out completely different combos of framework sorts and dimensions, it might probably generally be tempting to snap increasingly more items collectively. But sacrificing simplicity can doubtlessly diminish the broader worth of the framework in apply. Complicated frameworks could be obscure, apply, consider, and construct — you could have to confirm a number of assumptions and preconditions, and modify many various levers throughout the framework.

Tip 3: Make It MECE 

Making certain {that a} framework is MECE has some essential benefits. From a theoretical standpoint, being MECE implies that the sub-concepts comply with a constant, additive part-whole logic; you count on the sub-concepts to “add up” to type the larger idea. Crucially, this logic permits you to substitute the set of sub-concepts for the larger idea (and vice versa) all through your evaluation. The additive logic of MECE additionally allows you to evaluate completely different ideas in a rigorous method; as a substitute of claiming that two ideas are comparable, you possibly can state exactly the extent to which they’re comparable by figuring out the sub-concepts they share. From a sensible perspective, being MECE means that you could “divide and conquer” large issues effectively and options to some sub-problems could also be reusable. Typically you possibly can even attain the answer of the larger drawback with out fixing all of the sub-problems (e.g., if the larger drawback could be represented as a disjunction of the sub-problems). Bypassing sub-problems additionally works when you find yourself fixing the larger drawback inductively (e.g., as in mathematical induction).

Tip 4: Make It Versatile 

Basically, a conceptual framework must be designed to satisfy its total goal, so you could be questioning why flexibility is a vital facet to contemplate. In apply, there are at the very least two kinds of conditions by which flexibility generally is a large assist. Within the first state of affairs, you could be coping with an goal that may be a transferring goal, with some components of the target’s full scope altering (even barely) on occasion; responding to such scope adjustments generally is a ache if some flexibility is just not baked into the framework. Within the second state of affairs, your framework might need to bear a number of iterations, by which completely different framework sorts and dimensions are added, modified and eliminated over the course of the framework’s evolution; a versatile design makes it a lot simpler to facilitate such alterations of the framework’s form and content material. Modularity, scalability, robustness, extensibility, and portability — whereas usually related to software program engineering and structure — are additionally related design concerns for constructing versatile conceptual frameworks.

Tip 5: Construct It Iteratively 

It could be nice if you happen to may give you the right framework in a single go, nevertheless it hardly ever works out that method. A number of components could make the primary iteration extra of a primary draft, to be adopted by at the very least a couple of extra. The overarching goal — and particularly the operational implications relating to constructing the framework — will not be totally clear at first. Over a few iterations, nevertheless, you’ll most likely start to get the hold of which framework sorts and dimensions work and which don’t. Whereas your output after a given iteration could also be removed from good, it may nonetheless quantity to a minimal viable product (MVP) if it yields a viable resolution to the overarching goal with minimal effort and complexity. The MVP could be examined (e.g., with precise information and actual customers) to know its strengths and weaknesses. Every successive iteration can produce an improved MVP by including, eradicating or altering options of the earlier iteration.

To shut off, here’s a video that shares some extra good recommendation on constructing and utilizing conceptual frameworks:

The Wrap

Conceptual frameworks assist us flip summary concepts into concrete, tangible merchandise that different folks can see, use, and recognize. This may be particularly essential for information scientists, or so-called “data staff,” whose jobs contain gathering, analyzing, and deriving conclusions from information. If you’re studying this text, you’re most likely a data employee. To paraphrase well-known administration guru Peter Drucker, “It’s information that permits data staff to do their job,” however it’s the capability to meaningfully manage this information that results in a job properly executed — and that, in a nutshell, is why the correct use of conceptual frameworks can support the profitable design and supply of information science initiatives.