Tag Archives: drucker

Advanced Analytics, Part III

This post is also about the front-end part, as a conduit for information delivery to the decision maker. Previous two posts are available, it’s recommended to check out the Introduction into the Big Picture and Ruminations on Conveying, Organization and Segmentation of Information for Executives as users.

Big Data? All Data!

It’s time to pay attention to all data available. Personally I see no reasons to limit to big data. All data matters, most recent data matters more, oldest data matters less. It is possible to visualize plenty of data on relatively small space, which is convenient for delivery onto smartphones and wrist-sized gadgets. The rationale is to depict firm details on the most recent/relevant data, the relevancy is determined by the adopted processes. In SDLC it could be a sprint or iteration; in healthcare it could be a period since current admission. The latest measured value matters a lot, hence must be clearly distinguished on top of the other values within the period. The dynamics during the period also matters, hence should be visualized to convey the dynamics.

Previous periods/cycles do matter, especially for comparison and benchmarking to enable better strategic planning. The firm details on dynamics during past cycles are not so valuable, while deviations into both positive and negative directions are very informative. Decision maker knows how to classify the current cycle exceptions, whether something brand new happened or whether business experienced even more severe deviations in the past, and recall how.

Being inspired some time ago by medical patient summaries by Tufte and Powsner I’ve tried to generalize the concept to be applicable to other non-healthcare industries. So far it fits perfectly, allows customization and flexibility, especially for the optimization of the processes, where people usually use control charts on dashboards. Below is a generalized version of the ‘All Data’ chart as a concept.

all_data

Inverted Pyramid

The principle of Inverted pyramid is partially present there, the pyramid is rotated by 90 degrees. Most important information is within the biggest part of the chart, in the center and on the right. It is rather information than data, because id conveys latest value, dynamics during recent cycle, benchmarking against the normal range, indication of deviations (in qualitative way, using only two categories: somewhat and significant). It’s rationale to stay in the range of 10 with the measurements so that they are remember-able relatively easy.

The next narrow part to the left from the sparkline is partially information and partially data. It’s used for comparison and benchmarking, analysis of exceptions, retrospective analysis. It is absolutely logical to fit there 10 times more data, so that if there is a lack of information in the biggest part, the user is able to dig deeper and obtain significantly more facts and reasons, as measurements of the same thing. Hence phase shift means at least 10x growths. With medical patient summaries the ratio was similar: one-two months between admission and discharge vs. one previous year. But 10x is not a hard ratio, it’s more indicative that we need a kind of phase shift to different data, different level of abstraction.

The leftmost narrow part is actually the all and oldest data. It is additional phase shift, relatively to the middle part, hence imagine additional 10x increase and digging to the different level of abstraction again. Only exceptions marked as min/max are comparable between all parts. Everything else constitutes the inverted pyramid of making the information out of raw data.

Cap of the pyramid: Vital Signs

I think the cap of the information pyramid requires special conceptualization. ‘All Data’ is attractive tool to deliver project/process vital signs for executives and other managers (decision makers), they could be compressed even more. Furthermore, the top five-seven measurements could be stacked and consumed all together. That increases the value of the information synergistically, because some indicators are naturally perceived together as juxtaposition of what is going on.

Specific vital signs for business performance and SDLC process optimization were listed in details in my previous post Advanced Analytics, Part II. Here I will only mention them for your convenience: productivity, predictability, value and value-add, innovation in core competency, human factor and emotional intelligence/engagement. Those are ‘the must’ for executives. They could be stacked as vital signs and consumed as integral big picture.

vistal_signs

Of course we can introduce normal range there, ticks for the time tracking, highlight min/max… The drawing represents the idea of stacking and consumption of executive information of SDLC project/process performance in modern manner. You could critisize or improve it, I’ll be thankful for feedback.

There are two dozens of lower level operational indicators and measurements. Some of them could be naturally conveyed via ‘All Data’ concept, others require other concepts. I am going to address them in next posts. Stay tuned.

Advertisements
Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

Advanced Analytics, Part II

This post will be about delivery and consumption of information, about the front-end. Big picture introduction has been described in previous post Advanced Analytics, Part I.

The Ideal

It would be neither UI of current gadgets nor new gadgets. The ideal would be HCI leveled up to the human-human communication with visual consistent live look, speech, motions and some other aspects of real-life comms. There will be AI built finally, probably by 2030. Whenever it happens, the machines will try to mimic humans and humans will be able to communicate in really natural way with machines. The machines will deliver information. Imagine boss asking his/her assistant how the things are going and she says: “Perfectly!” and then adds portion of summaries and exceptions in few sentences. If the answers are empathic and proactive enough, then there may probably be no next questions like “So what?”

First such humanized comms will be asynchronous messaging and semi-synchronous chats. If the peer on the other end is indistinguishable (human vs. machine) , and the value and quality of information is high, delivered onto mobile & wearable gadgets in real-time, then it’s first good implementation of the front-end for advanced analytics. The interaction interface is writing & reading. Second leveling up is speech. It’s technically more complicated to switch from writing-reading to listening-talking. But as soon as same valuable information is delivered that way, it would mean advanced analytics got a phase shift. Such speaking humanized assistants would be everywhere around us, in business and life. Third leveling up is visual. As soon as we can see and perceive the peer as a human, with look, speech, motion, then we are almost there. Further leveling up is related to touch, smell and other aspects to mimic real-life. That’s Turing test, with shift towards information delivery for business performance and decision making.

What to communicate?

As highlighted in a books on dashboard design and taught by renown professionals, most important are personalized short message, supported with summaries and exceptions. Today we are able to deliver such kind of information in text, chart, table, map, animation, audio, video form onto mobile phone, wristband gadget, glasses, car infotainment unit, TV panel and to the number of other non-humanized devices. With present technologies it’s possible to cover first and partially second levels described in “The Ideal” section earlier. Third – visual – is still premature,  but there are interesting and promising experiments with 3D holograms. As it’s gets cheaper we would be able to project whatever look of business assistant we need.

Most challenging is a personalization of ad-hoc real-time answer to the inquiry. Empathy is important to tune to the biological specifics. Context and continuity according to the previous comms is important to add value, on top of previously delivered information. Interests, current intentions, recent connections and real-time motion could help to shape the context properly. That data could be abstracted into the data and knowledge graphs, for further processing. Some details on those graphs are present in Six Graphs of Big Data.

Summary is an art to fit a big picture into single pager. Somebody still don’t understand why single pager does matter (even UX Magazine guys). Here is a tip – anthropologically we’ve got a body and two arms, and the length of the arms, the distance between the arms, distance between the eyes and what we hold in the arms is predefined. There is simply no way to change those anthropological restrictions. Hence a single page (A4 or Letter size) is a most ergonomic and proven size of the artifact to be used for the hands. Remember, we are talking about the summaries now, hence some space assets are needed to represent them [summaries]. Summaries should be structured into Inverted pyramid information architecture, to optimize the process of information consumption by decision maker.

Exceptions are important to be proactively communicated, because they mean we’ve got issue with predictability and expectations. There could be positive exceptions for sure, but if they were not expected, they must be addressed descriptively, explanatory (reason, root-cause, consequences, repeatability and further expectations). Both summaries and exceptions shall fit into single pager or even smaller space.

What exactly to communicate?

On one hand main message, summaries and exceptions are too generic and high-level guidelines. On the other hand, prescriptive, predictive and descriptive analytics is too technical classification. Let’s add some soul. For software projects we could introduce more understandable categories of classification. “Projects exist only in two states: either too-early-to-tell or too-late-to-change.” It was said by Edward Tufte during discussion of executive dashboards. Other and more detailed recommendations on information organization are listed below, they are based on Edward Tufte and Peter Drucker experience and vision, reused from Tuftes forum.

  • The point of information displays is to assist thinking; therefore, ask first of all: What are the thinking tasks that the displays are supposed to help with?
  • Build in systematic checks of data quality into the display and analysis system. For example, good checks of the data on revenue recognition must be made, given the strong incentives for premature recognition. Beware, in management data, of what statisticians call “sampling to please”.
  • Avoid heavy-breathing metaphors such as the mission control center, the strategic air command, the cockpit, the dashboard, or Star Trek. As Peter Drucker once said, good management is boring. If you want excitement, don’t go to a good management information system. Simple designs showing high-resolution data, well-labelled information in tables and graphics will do just fine. One model might be the medical interface in Visual Explanations (pages 110-111) and the articles by Seth Powsner and me cited there. You could check out research with those medical summaries for iPad and iPhone in my previous posts. Mobile EMR Part I, Part II, Part III, Part IV, Part V.
  • Watch the actual data collection involved in describing the process. Watch the observations being made and recorded; chances are you will learn a lot about the meaning and quality of the numbers and about the actual process itself. Talk to the people making the actual measurements.
  • Measurement itself (and the apparent review of the numbers) can govern a process. No jargon about an Executive Decision Protocol Monitoring Support Dashboard System is needed. In fact, such jargon would be an impediment to thinking.
  • Too many resources were devoted to collecting data. It is worth thinking about why employees are filling out forms for management busybody bureaucrats rather than doing something real, useful, productive…

Closer to the executive information

Everything clear with single-sentence personalized real-time message. Interest Graph, Intention Graph, Mobile Graph, Social Graph might help to compile such message.

Summaries could be presented as Vital Signs. Like we measure medical patient temperature, blood pressure, heart rate and other parameters, the similar way we could measure vital signs of the business: cache flow, liquidity projections, sales, receivables, ratios.

Other indicators of the business performance could be productivity, innovations in core competency, ABC, human factor, value and value-add. Productivity should go together with predictability. There is excellent blog post by Neil Fox, named The Two Agile Programming Metrics that Matter. Activity-based costing (aka ABC) could show where there is a fat that could be cut out. Very often ABC is bound to the human factor. Another interesting relation exists between productivity and human factor too, which is called emotional intelligence or engagement. Hence we’ve got an interdependent graph of measurements. Core competency defines the future of the particular business, hence innovations shall take place within core competency. It’s possible to track and measure innovation rate, but it’s vital to do it for the right competency, not for multiple ones. And finally – value and value-add. In transforming economy we are switching from wealth orientation towards happiness of users/consumers. In the Experience Economy we must measure and project delivery of happiness to every individual. More details are available in my older post Transformation of Consumption.

Finally in this post, we have to distinguish between executive and operational information. They should be designed/architectured differently. More in next posts. It’s recommended to read Peter Drucker’s work “The Essential Drucker” to unlock the wisdom what executives really need, what is absent on the market, and how to design it for the modern perfect storm of technologies and growing business intelligence needs.

Tagged , , , , , , , , , , , , , , , , , , ,