Tips for Better BigData Presentations

Don’t Put Your Audience to Sleep: Why Analytics Presentations Fail and How to Present Analytics that Speak the C-Suite’s Language

Don't Put Your Audience to Sleep: Why Analytics Presentations Fail

How to Present Analytics that Speak the C-Suite's Language

Phil Kemelor is Senior Manager, Enterprise Intelligence Digital Analytics of Ernst & Young. Phil was one of the earliest adopters and advocates for the use of analytics and has 16 years of experience in the field as a practitioner, industry analyst and consultant.

Phil recently wrote a couple of at articles which were published at CMSWire. Both articles were on the broad topic of presentation effectiveness with one focusing in on how not to be boring when presenting analytic information and the other focusing on how to present analytic information to a ‘C’-Level executive. I’ve distilled down his bullet points to give you a 30,000 ft fly-by of his advice. Please read the entire articles (links are embedded in several places) to get the full benefit of Phil’s wisdom.

First, here is a summary of Phil’s article, “Don’t Put Your Audience to Sleep: Why Analytics Presentations Fail”:

[…] I’m going to share six reasons I see presentations fall off the rails and self-destruct. If you think analytics is more art than science, presenting analytics data is even more so. […]

  1. Too Much Data

    […] Your audience wants to hear your recommendations as soon as possible. They trust that you know what you’re doing. Pages of data will put them to sleep — or make them antsy and irritated. […]

  2. Passive Slide Titles

    […] Your audience doesn’t want to have to read through the slide to unearth the key takeaway. They don’t care so much about the data analysis as the perspective and smarts that you bring to the discussion — that’s why you have your job in the first place. […]

  3. Metrics That Don’t Add Up

    […] Your audience doesn’t understand how [what you observed in your analysis] helps them figure out the business problem that they gave you to solve. It might help you figure out the answer, but for the audience, it’s like watching sausage get made. […] Only put the key data points and visuals that will support the headline into the rest of the slide.

  4. Overstuffed Slides

    […] Your audience sees a slide with more text than they can read. It blends together. Their eyes glaze over. They are interested in being told what to do with the data. They want to get to the bottom line. […]

  5. Speaking in a “Foreign Language”

    […] Your audience doesn’t necessarily live in the digital world. Maybe they live in the world of finance, regulation, operations and legal affairs. Digital is new to them and they don’t understand it quite as you do. They want to understand, but they also want the intersection of your world and their world to be clear and easy to grasp. […] Think about all of the terms that you might want to use to explain your data. Remove any of the industry language that you’ve grown accustomed to. […]

  6. Overconfidence in One’s Ability as an Awesome Analyst

    You know the data better than anyone. Your recommendations are freakin’ brilliant. Your insights will make your audience swoon. You’re charming and witty to boot. Naturally your presentation will be a stunning success.

    But do you know this for sure? Have you shown your deck to anyone? Has it been proofed, edited? Have you done a run through? Is your timing going to be razor sharp?

Next is a summary of Phil’s article, “How to Present Analytics that Speak the C-Suite’s Language

[…] I’m going to share four techniques that will help you reach more people, more effectively than you are today with analytics data.

  1. Know the difference between tactical and strategic data

    […] What do you present to executives? I think we’re all on board that you don’t present the same data that you would to the marketing team, the granularity isn’t of interest and pretty much a waste of time to someone who is running an organization, business unit or an entire program.

  2. Understand what drives your organization’s strategy

    [It surprises me that ]…] many analytics managers and analysts do not know their organization’s strategic goals and objectives. If they do, they have not figured out how to tie the data that they collect to these goals. […]

    […] Roll up campaign data into one number on the revenue being driven by all digital campaigns, or […] focus on the success of expansion into new markets by highlighting site registrations from a specific geolocation or visitor segment.

  3. Understand executive concerns

    Figuring out organizational goals and objectives are requisite items to building relevant executive level reports. […] I find three items of high interest to the C-Suite:

    • Competition — Understand how your organization’s performance stacks up to the marketplace and whether you’re ahead or behind.
    • Voice of Customer — Bringing the voice of customer to life in your reporting through surveys or social media commentary adds an element of humanity that executives value […]
    • Risk — Is there data that you are collecting that suggests negative market or product changes? Is negative social media or mass media having an impact on digital channel activity? These are among the risks that keep C-level execs up at night.
  4. Understand the person

    […] You have an audience of one, and the research should reflect that. It’s more personal. Do you know the person you’re presenting to? What type of presentations they like and don’t? Do they want you to provide a point of view and recommendations? Or do they want to come up with the conclusion themselves?

    You also want to know how they are going to use what you present for their own presentations. No matter where you are in the food chain of business, you have to figure out the best way to communicate “up.” […]

Where to go from here

Communicating data is a lot more complex than most organizations would like to think. As a “soft skill” it doesn’t get nearly the same attention as technology or data. If you want to have a successful analytics program, you will need to spend more time on this part of delivery and roll out then you are likely spending today.

Metacademy: Machine Learning and Probabilistic AI Learning Resources

I’ve recently come across a tremendous resource for the discovery of various machine learning and probabilistic artificial intelligence topics and associated educational materials. The site I’m describing is Metacademy.Metacademy Large Cropped Home PageMetacademy is a community-driven, open-source platform to facilitate the collaborative construction of a web of knowledge by domain experts meant to help individuals efficiently learn about any topic of interest (supported by Metacademy and the domain experts). The experts responsible for Metacademy are Roger Grosse and Colorado Reed. In addition to building the site, they organized roughly 350 machine learning and probabilistic artificial intelligence concepts along with related training and learning materials.

While Metacademy is currently focused on machine learning and probabilistic artificial intelligence topics, eventually, it has the goal to cover a much wider breadth of knowledge; e.g. mathematics, engineering, music, medicine, computer science, etc.

The premiss of Metacademy is that a user will search for and click on a concept of interest. Metacademy then produces a “learning plan” which includes the prerequisite concepts which were identified in the web of knowledge previously created by the domain experts. This component of identifying for the student the list of prerequisite concepts is what sets Metacademy apart from other learning sites or course catalogs.

As posted at Metacademy:
… But try learning something of conceptual depth by sifting through Google search results … and you’re in for a lot of agony. Before you learn this concept, you need to learn its prerequisite concepts (sometimes you’re not entirely sure what these are), and the prerequisite concepts may have prerequisites themselves. Pretty soon, you’re deep in dependency hell, switching between twenty different tabs trying to understand the various [pre]prerequisite concepts in order to understand the tutorial article Google returned …

Metacademy’s learning experience revolves around two central components:

  • a “learning plan” in a tabular ‘list view’
    Metacademy Logistic Regression List View
  • a “graph view” of the learning plan which is meant to help explore relationships among concepts
    Metacademy Logistic Regression Graph View

Clicking on the check-mark next to the title of a concept in either the graph or list view marks that [prerequisite] concept as being understood. To not show those concepts which have been marked as being understood, click the “hide” button in the upper right. Note that Metacademy will remember the concepts marked as understood and hidden and will automatically re-apply these selections at future visits.

As Metacademy is a work in progress and limited in scope, please keep an open mind when visiting, but I think that you will find it an interesting, unique and valuable resource, particularly if you are, as I am, actively exploring the world of machine learning.

Improve your Digital Analytics Skills with Google’s … – Analytics Blog

See on Scoop.itEvidence Based Systems

That’s why today we’re excited to announce Analytics Academy — a new hub for you and your colleagues to participate in free, online, community-based video courses about digital analytics and Google Analytics.

mike pluta‘s insight:

Another free resource from which to learn about analytics, statistics, data visualization and big data concepts.

See on