By the Numbers: Making the Case for Reuse Based on Facts with Joan Lasselle and Amber Swope
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Content reuse is one of the key reasons for converting to a single source. But how do you know what the reuse potential really is? Because converting content formats is a major investment, you need to base your business case on real data. You can find the data with these steps: • Observation • Comparison • Analysis This session gives you a methodology to learn how to determine the true opportunity for content reuse and quantify it for your business case. This presentation was given at Information Development World on October 1, 2015.
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By the Numbers: Making the Case for Reuse Based on Facts with Joan Lasselle and Amber Swope
- 1. By the Numbers: Making the Case for Reuse Joan Lasselle and Amber Swope
- 2. Introductions • Joan Lasselle is the President of Lasselle-Ramsay, Inc. • Amber Swope is a DITA Specialist at DITA Strategies, Inc.
- 3. What success looks like
- 5. Address confusion • Inconsistency • Lack of appropriate access • Readers can’t find the information they need
- 6. Quantifying the reuse opportunity Observation Comparison Analysis
- 7. Observation
- 10. How we do it This is the first example string This is the fifth example string • 3 characters would have to change to make them the same • 32 characters in longest string • Matching score = (39-3)/39 = 91% match
- 11. Example • Text strings are then assigned to clusters based on the matching score. • The higher the matching score, the more likely to obtain reuse, and the greater business impact
- 12. Sample data
- 13. Lots of data
- 14. Matching content
- 16. Types of reuse ① Programmatic reuse ② Template reuse ③ Variable reuse ④ Content repurposing
- 17. What do the numbers say? • For you/your team • Initial baseline • Measurement tool • Play what if? (different analytical views) • Run it over and over again
- 18. Example
- 19. Building your business case • Content • Process • Tools
- 20. Summary Q&A • Automated comparison with analysis is the key to knowing your reuse potential • Get the numbers BEFORE you start your project
- 21. Questions Joan Lasselle Lasselle-Ramsay joan.lasselle@LR.com Amber Swope DITA Strategies amber@ditastrategies.com
Editor's Notes
- Joan: The theme of this conference…. <make the connection with theme> Amber: Customer, both internal and external, expect to find your content quickly regardless of the device they are using or the type of content. And that content had better be accurate and consistent.
- Amber: Joan: The opposite of the great customer experience is content confusion. Once you have multiple instances the finding becomes harder. Maintenance. Where do I find it???? This is the hunt problem. How do I find every instance? Update replace product names, dates, locations, specs—minor updates. Minor but time-consuming, difficult, error-prone, and not lasting. Major content changes: Content Learning objectives Cert questions Manual = go and find it Labs can change quickly—keep it aligned Let’s say software changes— Lab, where you use software, may need to change Then cert questions may need to change
- Joan: You can address content confusion by: Improving content consistency Providing easy access to the content that each customer should be able to access Making it easy to find the appropriate information at the right time Other session at this conference will cover access and retrievability, but our session focuses on addressing inconsistency via reuse.
- Amber Based on our experience designing, developing, and delivering content, we know that that reuse doesn’t just happen: it takes planning and coordination. While some teams think that they have lots of potential reuse, other teams think that they have very little opportunity to reuse content. Our recommendation is not to just think that you could reuse more content, but quantify the real reuse opportunity. Our proposal is a 3-part strategy: Observation Comparison Analysis
- Amber: Lots of folks start and end their reuse measurement with observation. This means that they usually compile a collection of content samples and then spot-check to see if there are redundant words/phrases in the different deliverables. While this technique does give you a bit of knowledge about a small subset of text, it doesn’t really give a complete picture. It’s more like fishing for reuse… The weaknesses of observation as the only measurement for potential reuse are: It subjective: one person may search for one set text while another person searches for something completely different based upon what they think is important or useful It’s not reproducible: the data depends upon who checks what phrases in which deliverables Joan: It’s incomplete: this manual method does not scale to give you a true count of the number of times a given word or phrase appears It’s on exact matches only: searching for only exact matches doesn’t really show the opportunity for standardization; for many teams, the opportunity is in the near matches that could be standardized If this is your only tool for estimating reuse opportunities, then you can never get real numbers for a business case to actually show the potential value of reuse Instead of being the only step, we propose that observation is the first step and that instead of focusing on what words or phrases were repeated, we propose that the outcome of observation is identifying the content set for comparison and analysis <<fishing—subjective—spot checking in thousands of deliverable—are you getting the right ones>>
- Joan By focusing on identifying the content for comparison and analysis, you’re not longer just fishing for information a huge ocean of text; instead you are defining the scope of your comparison. We recommend that you use this exercise to select content set against a pattern or criteria: Multiple deliverables of the same type across products Multiple deliverables of different types that pertain to the same product Multiple deliverables from different groups that apply to product families ??? Once you’ve identified the content set, you’re ready to do some thing with it.
- Amber The first thing you do is compare the words and phrases that comprise the content set. Not only are you looking for the items that are the exactly the same, but you want to know which of these things is not like the others…then you can analyze why it is different and whether it should be different. The only way to do this type of comparison is with a tool or script. We’re going to show you the methodology behind a tool that we’ve been testing, but this is not a tool sales pitch.
- Joan First, get text in a format that it can be analyzed. Separate the text from the presentation layer. <<is this a transform? What can’t you work with images?>> Then identify the content you want to compare—within a product line, across product lines, across functions? Run the application to identify not only where reuse occurs, but also give the reuse a score: 100%, 90% Benefit is you can run over and over—within product line across functions.
- Joan After you have identified the matches then you assign them into clusters based on their matching score. Once you have run the comparison and identified matches you can analyze the text to see where the reuse occurs and if text that is potential can be changed to be 100% matches.
- Amber After the tool compares the content clusters, it generates a spreadsheet with a lot of information. Column A: lists the unique ID to each cluster. Column B: lists the text in the cluster Column C: lists the word count for the cluster Column D: provides the file from which the cluster was extracted Column E: lists a randomly assigned cluster number for 100% matches Column F: identifies if this cluster is the root/base for the comparison #311 has the root cluster Column G: identifies if this cluster is a compared cluster Column H: identifies the number of words that were duplicates in the comparison between base cluster and the compared cluster Column I-K: provides the same information for 90% matches Column L-P: provides the same information for 80% matches Column Q-T: provides the same information for the 70% matches This means that cluster # 1050 was compared to and exactly matched all the other clusters that have the value of #304.
- Amber This type of comparison is incredibly detailed and generates thousands of comparisons. This data set included 10 fairly small online help systems and generated over 8K comparisons. So, what do you do with all this data? Just having lots numbers doesn’t really help you quantify the opportunities for actual reuse. You need to analyze the data to understand what it really means.
- Joan – can you speak to this one? Do nothing to the slide. The Analyze the data from the comparison Provide context by identifying the content types <<making sense of the data>> <<reverse engineering the data—where did it come from>> pie chart on the left shows the percentage of paragraphs that match at different levels. Paragraphs that have a match of 60 percent or greater are considered to have attainable reuse and can be easily repurposed in the short term with low to medium effort. The pie chart on the right shows the breakdown of the 31 percent attainable reuse by functional group.
- Joan? Even at 70% will only see 26% reuse. Applied report; graphs from Harmonic analysis Analysis helps to quantify the opportunity—what’s the real opportunity Walk through a blend of the comparison/analysis for Applied and Harmonic—comes from Andy too <<screen cap of subset of data maybe with callouts>> Break into 2 Below the line Total reuse % Comparison: here’s what the comparison gives us—but wait there’s more You’re not done yet—what does it mean—swimming in numbers, data doesn’t give you answers This is more Andy
- Amber Now that we have a real understanding of the content that we can standardize, we can consider the types of reuse that may be possible. 100% match: Programmatic reuse: all those clusters that are content labels and other standardized content should be automatically generated based upon the pattern and deliverable Example: Template reuse: the content clusters that are actually consistent patterns, such as table headers Example: Alarm description content, the alarms are listed alphabetically and each “letter page” has a table with the same three column heads: alarm, explanation, recovery tips Variable reuse: usually single words or small phrases that should be standardized Example: products names, such as NMX Content repurposing: reusing the same word or phrase in multiple contexts Example: “Start the Consolidated Alarms Application to view the alarms at the remote site.” this phrase appears 5 times; perhaps it would make sense to create a “library” of standard recovery options Example: “The ECM/ECMs in one or more SCG messages are missing and are not provisioned from NMX.” appears in two help systems. Maybe this is an opportunity to standardize OR NOT? 90% match: You might find some items, such as Click OK versus Click OK button that are standardization opportunities.
- Joan:
- Joan? Break into 2 Below the line Total reuse % Walk through a blend of the comparison/analysis for Applied and Harmonic—comes from Andy too <<screen cap of subset of data maybe with callouts>>
- Amber Many teams use the prospect of reuse as a driver for moving to DITA. However, before you ask for money to fund this initiative, quantify that real reuse opportunity. Can you standardize your content? The comparison provides the data, but the analysis indicates what content can really be reused. In the example we showed today, many of the 90% matches can’t standardized and reuse via content references or other DITA mechanisms because the difference is between input and output. Further analysis by content type provides greater clarity What processes would have to change to reuse content? If you’re trying to share content across teams, then you’ll to evaluate the readiness and feasibility of sharing/reusing content. Can you become more standard simply with some better processes? What tools would help you reuse content? For many teams, reuse is limited until they are able to manage their content in a CCMS Joan: Give an example