Conceptual Frameworks for Information Science Initiatives

Observe: All figures within the following sections have been created by the writer of this text.

Frequent Framework Varieties

Though conceptual frameworks are available many various sizes and styles, 4 fundamental framework varieties stand out as being particularly widespread in knowledge science initiatives: hierarchies, matrices, course of flows, and relational maps. We are going to briefly go over every of those framework varieties 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 could characterize an overarching idea in a taxonomy or an preliminary binary query in a choice tree. A node’s place within the hierarchy (or tree) offers us priceless details about its relationship to different nodes. Though Determine 1 labels the gadgets within the hierarchy as “ideas,” they are often any type of entity. Entities could also be impartial (e.g., ideas, matters, 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 sometimes drawn explicitly and might be non-directional (easy strains) or directional (downward or upward arrows, relying on the circulation of the connection). Against this, horizontal hyperlinks between entities on the similar stage of a hierarchy are sometimes not proven explicitly. Identical-level entities could also be topic to a pure ordering (e.g., temporal or spatial), which might be proven by putting them accordingly within the framework. For 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’ll be able to nonetheless think about ordering them indirectly (e.g., by stage of significance or precedence) to assist reasoning. Entities on the similar stage in a hierarchy ought to typically 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 vital. A MECE hierarchy might be helpful for breaking down a broad idea into sub-concepts (or parts) to determine key drivers of the entire.

Matrices

A matrix is a tabular knowledge construction consisting of n rows and m columns. Information scientists engaged on tabular use circumstances routinely leverage matrices for storing coaching knowledge 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 might be helpful for analyzing issues and speaking key insights.

The generic two-by-two matrix proven in Determine 2 compares two totally 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 usually 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 research the aggressive panorama in numerous business 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 could characterize steady, ordinal or categorical knowledge varieties. Ideally, these dimensions (or axes) must be vital 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 nicely.

Generally, the MECE precept additionally applies to the selection of dimensions — they need to collectively cowl the vital 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 totally different from taking a look at a person dimension. If analyzing the interplay isn’t vital, a hierarchical framework could also be extra appropriate. Changing between a matrix framework and its hierarchical analog might be simple. For example, to rework the matrix in Determine 2 right into a hierarchy, create a root node that defines the general context, let its little one nodes be Dimensions 1 and a pair of, and let their respective little one nodes be “excessive” and “low.”

Course of Flows

A course of circulation defines a sequence of logically ordered actions that work together to attain an overarching goal. For example, instruments corresponding to Dataiku and KNIME enable customers to assemble knowledge science pipelines as course of flows, going from knowledge 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 might be steps, phases, operations, and so forth. 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 sometimes fed into the method at the beginning and reworked over the sequence of actions to yield an output. Observe that inputs and outputs also can enter and go away at intermediate steps throughout the course of.

As with hierarchies and matrices, the MECE precept might be vital in formulating the totally different actions of the method. If two actions have important conceptual overlap, you may think about both grouping them right into a single exercise or breaking them up right into a extra granular set of distinct actions. For example, the intermediate actions in Determine 9 could have resulted from this form of evaluation; Exercise 2 might be the end result of merging some overlapping actions, whereas Actions 2.1-2.3 might 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 purpose of manufacturing the specified output. If a transition doesn’t change the inputs, then the 2 actions on both aspect 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 target from particular person ideas (or entities) to the relationships between them. Information scientists working with information graphs or box-and-arrow “path diagrams” of causal relationships (as proven in Determine 4) will likely be accustomed to this framework kind.

A relationship can typically be any perform that hyperlinks two totally different ideas collectively. 4 sorts of relationships are particularly widespread:

• Transactional: A relationship can characterize a number of transactions between entities. The transactions could contain the circulation of tangible issues (e.g., merchandise purchased and offered) or intangible issues (e.g., data, cash). Transactional relationships can incorporate directionality; a transaction can circulation from A to B, from B to A, or in each instructions, and every of those circumstances has a unique 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 partially — for the prevalence or state of B (or vice versa). The character of the causal relationship could range. The function of A is powerful if its presence is adequate to completely trigger B (though A will not be the one entity that may absolutely trigger B). The function of A can be sturdy whether it is essential to trigger B (though A could not have the ability to do that alone). Furthermore, if A causes B, it doesn’t essentially observe that B causes A; the notion of directionality is clearly vital for specifying causal relationships.
• Similarity-based: Entities could also be associated as a result of they’re related or dissimilar indirectly. For instance, entities A and B might be related as a result of they have a tendency to seem in the identical place or occur on the similar time (and dissimilar if the prevalence of 1 entity tends to preclude the prevalence of the opposite). The notion of correlation is a mathematical formalization typically used to assemble measurable, similarity-based relationships. Observe that, simply because two entities are correlated doesn’t essentially imply that they’re causally associated (though if they’re causally associated, then they’d even be correlated).
• Membership-based: Entities might be linked collectively by being members of the identical group, group, or class. For example, folks might be associated by being in the identical neighborhood, grocery gadgets might 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 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 appropriate constructing blocks (i.e., the framework kind 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 have to 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 mission, formulation of a choice, 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 supposed as a decision-making instrument? Is the framework meant to construction the circulation of an argument in a report or a presentation?

Simply since you want a framework doesn’t imply that you will need to construct one your self. In lots of conditions, current conceptual frameworks might be reused with out important modification. Spending some effort to keep up a strong, 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 principal options, in addition to its strengths and limitations, could also be well-documented and examined in numerous settings. Platforms corresponding to In the direction of Information Science are an ideal supply for maintaining abreast of conceptual frameworks associated to knowledge science initiatives.

Step 2: Determine the Framework Sort and Dimensions

Having clarified the target of the framework, it’s time to suppose extra concretely concerning the development of the framework itself. One of many principal difficulties right here is that conceptual frameworks are inherently not as tangible as bodily ones (like molds in a manufacturing facility). We are likely to intuit the hyperlink between kind and performance — the framework and its objective — extra simply when the framework and its object are tangible. The hallmark of 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 facets that decide the illustration of conceptual frameworks: the kind of the framework and the dimensions of the framework. You’re more likely to discover the framework kind first because it determines how the framework seems as a complete. The earlier sections coated the 4 widespread framework varieties. 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 kind might be reused to generate a variety of various insights. Following are three widespread courses of framework dimensions:

• Categorical: These dimensions include a finite set of discrete classes that absolutely 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 which you could analyze whether or not one thing is “lower than,” “higher 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 kind and dimensions have been recognized, they are often mixed to supply a customized framework. Usually, the identification and mixture steps will not be explicitly separated, because you hardly ever do one with out the opposite. However the framework kind and its dimensions — the fundamental constructing blocks — will not be essentially wedded to one another. Some combos could make extra sense than others, and you may typically combine and match the constructing blocks in some ways, over a number of iterations, till the framework feels proper. Be capable of spot and exploit this combinatorial flexibility is a vital talent that it is best to begin creating 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 corresponding to graphs and tables, or written summaries). Doing so permits us to higher describe and analyze what occurred up to now, however it could not essentially inform us why one thing occurred, or whether or not it should occur once more.
• Diagnostic: Takes descriptive data of previous occasions and goes a step additional to have a look at why one thing occurred. That is finished by drilling down into the information, mining for clues and correlations, and looking for a believable hyperlink between trigger and impact. As with the descriptive pathway, the main target is on the previous.
• Predictive: Differs from the prior two by asking and answering questions concerning the future. The main target is on making an informed guess about what’s going to occur sooner or later by counting on a bunch of sometimes quantitative strategies that vary from the easy (e.g., fundamental chance concept, linear fashions) to the extra advanced (e.g., neural nets).
• Prescriptive: Goes past merely predicting future occasions to recommending methods to cope with them. The main target is on determining tips on how to make one thing occur — or whether or not it ought to occur — sooner or later. The reasoning for the prescription might be quantitative (e.g., based mostly on statistics or simulation modeling) or qualitative (e.g., based mostly on private expertise).

Framework varieties and dimensions can due to this fact be mixed in numerous methods to supply customized frameworks that lend themselves to descriptive, diagnostic, predictive, and prescriptive use circumstances.

High Suggestions

This part offers 5 suggestions for constructing good conceptual frameworks. The ideas are not at all an exhaustive checklist of the factors that it is best to think about, however characterize a fundamental set of issues to remember.

Tip 1: Deal with the Goal and Viewers

The method of constructing frameworks broadly consists of three steps, particularly defining the target, then figuring out and mixing the constructing blocks (framework varieties and dimensions) accordingly. Whereas step one will, by its nature, emphasize the strategic goal and audience of the framework, the main target 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 tougher it may be to keep up visibility of the unique goal. To take care of visibility of the larger image, it will possibly assist to take a step again every so often 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 knowledge turns into obtainable and to hunt common suggestions from colleagues and the audience of your framework the place potential.

Tip 2: Hold It as Easy as Potential

To paraphrase a quote typically attributed to Albert Einstein — some of the achieved builders of conceptual frameworks of the final century — we are able to say {that a} framework must be made so simple as potential, however not easier. For the reason that course of inherently includes making an attempt out totally different combos of framework varieties and dimensions, it will possibly generally be tempting to snap increasingly items collectively. But sacrificing simplicity can probably diminish the broader worth of the framework in apply. Advanced frameworks might be obscure, apply, consider, and construct — chances are you’ll must confirm a number of assumptions and preconditions, and regulate many various levers throughout the framework.

Tip 3: Make It MECE 

Guaranteeing {that a} framework is MECE has some vital benefits. From a theoretical standpoint, being MECE implies that the sub-concepts observe a constant, additive part-whole logic; you anticipate the sub-concepts to “add up” to kind the larger idea. Crucially, this logic lets you substitute the set of sub-concepts for the larger idea (and vice versa) all through your evaluation. The additive logic of MECE additionally helps you to evaluate totally different ideas in a rigorous method; as a substitute of claiming that two ideas are related, you’ll be able to state exactly the extent to which they’re related by figuring out the sub-concepts they share. From a sensible perspective, being MECE means which you could “divide and conquer” huge issues effectively and options to some sub-problems could also be reusable. Typically you’ll be able to even attain the answer of the larger drawback with out fixing all of the sub-problems (e.g., if the larger drawback might 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 chances are you’ll be questioning why flexibility is a crucial side to think about. In apply, there are at the very least two sorts of conditions wherein flexibility is usually a huge assist. Within the first state of affairs, chances are you’ll be coping with an goal that could be a shifting goal, with some elements of the target’s full scope altering (even barely) every so often; responding to such scope modifications is usually a ache if some flexibility isn’t baked into the framework. Within the second state of affairs, your framework could need to endure a number of iterations, wherein totally different framework varieties 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 sometimes related to software program engineering and structure — are additionally related design issues for constructing versatile conceptual frameworks.

Tip 5: Construct It Iteratively 

It will be nice in case you may give you the proper framework in a single go, however it hardly ever works out that means. A number of components could make the primary iteration extra of a primary draft, to be adopted by at the very least a number of extra. The overarching goal — and particularly the operational implications with regards to constructing the framework — will not be absolutely clear at first. Over a few iterations, nevertheless, you’ll most likely start to get the cling of which framework varieties and dimensions work and which don’t. Whereas your output after a given iteration could also be removed from excellent, 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 might be examined (e.g., with precise knowledge 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 admire. This may be particularly vital for knowledge scientists, or so-called “information employees,” whose jobs contain amassing, analyzing, and deriving conclusions from knowledge. If you’re studying this text, you’re most likely a information employee. To paraphrase well-known administration guru Peter Drucker, “It’s knowledge that permits information employees to do their job,” however it’s the capability to meaningfully set up this knowledge that results in a job nicely finished — and that, in a nutshell, is why the correct use of conceptual frameworks can help the profitable design and supply of information science initiatives.