A CIO Top Ten Guide to Preparing a Thanksgiving Feast

Thanksgiving Turkey I like to cook and I really look forward to the Thanksgiving Holiday. For the past thirteen years, I've enjoyed locking myself in the kitchen and perfecting the Thanksgiving feast largely from scratch everything from home made cranberry sauce to my evolving sweet potato pie/casserole recipes.

But this year we're going to family and I already miss the prep work that I always start the weekend before. So, without a feast to prepare, I will use this holiday to share some of my words of wisdom on preparing a successful Thanksgiving dinner

... with a twist ... This is from a CIO's viewpoint. It turns out that many of the disciplines needed to run a high performing, innovative IT team have parallels to planning a Thanksgiving dinner. Don't believe me? Here are my key tips to aspiring IT Leaders running the Thanksgiving Feast - 

  1. Plan ahead - You'll be sorry if you visit the grocery Sunday afternoon or Wednesday morning when the crowds are fighting over everything from parking spots to aisle space and the lines at check out look like Thursday's traffic. Go Sunday morning, Tuesday, or really early on Wednesday.

  2. Follow the Data Scientist - You can follow any turkey recipe you want, but I go with the one perfected by kitchen scientist Alton Brown. His Good East Roast Turkey recipe includes a brine that is worth the effort and has the perfect technique for a tasty, moist turkey.

  3. Conserve Key Resources - I pop the turkey in early in the morning. For a 2pm dinner and about three hours of cooking required for a 15lb bird, get your turkey in no later than 9am. Why? Because your oven is your critical path resource. Once you get the bird out, you'll have at least two hours of free oven space to make all your sides. Cover the turkey with foil and it will retain sufficient heat for serving later. 

  4. Build a firewall - Sorry, but I don't like visitors in the kitchen while cooking. It's both dangerous and distracting. Find a clever way to block off the kitchen or ....

  5. Hire helpers - ... get some help on tasks that are outside of the kitchen. Set the table, hang decorations or plan activities for the kids. 

  6. Architect your products - optimizing around really good ingredients such as fresh herbs, organic meats and local vegetables.

  7. Demo early - Most of my innovation during this holiday is with the appetizers. Demo early and fail early if you have to, but this is the best part of the meal to be creative.

  8. Please your customers - My family devours my sweet potato recette de l'annĂ©e - sometimes a pie, or a casserole, or just mashed - but I overload it with crowd pleasing ingredients including butter, maple syrup, brown sugar, ginger and almonds. My related warning is don't over engineer this or other recipes with over the top ingredients like marshmallows. 

  9. Outsource what you can't do well - There are some parts of the meal that are just not worth undertaking on your own. Unless you are a pastry chef or very skilled and practiced at desert, you're most likely better off outsourcing it to the local bakery or to family members that insist on contributing.

  10. Celebrate the wins - Have your beverage of choice with you behind the firewall. Practice your plating and photo your best work. Prepare your menu so that you can walk the floor and enjoy the time with your family.  
Happy Holidays!
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Agile Product Owner, What Does the Data Tell You?

Agile Product Development Leveraging Data
The number one question that I ask agile product owners these days is simple, "What does the data tell you?"

To develop excellent web 2.0 products, it may have been sufficient for product owners to understand workflow needs or to define user pain points that need addressing. A reasonable recipe for product owners often includes identifying core users, developing an application that simplifies workflow for them, designing an intuitive, easy to understand user experience, and ideally introducing content or collaboration capabilities to improve "stickiness". Diving down into agile user stories, these artifacts usually prescribe what the user wants to do and the acceptance criteria and parameters acceptable to the transaction. 

As I recently explained in an interview for PWC's Technology Forecast, today's applications need to adjust to the user, one can no longer develop a single workflow or user interface that is applicable to all user segments. Mobile interfaces need to adjust to context including location, screen size, social network, bandwidth, and what users must do quickly and easily. Collaboration has a lot more significance in today's applications when multiple parties can interact in context and perform transactions.

With so many technology options today, if you're not leveraging data to define and manage the product, you might find that you've developed an interesting but not useful product. Users may download the app from the App Store, but not use it beyond month one. An enterprise might buy a hundred seats to the tool, but only a small percentage of them are using it on a regular basis.

Leveraging Data to Define the Product

So where is the use of data in this articulation of priority, requirement, or acceptance criteria when the product owner is working with his or her development team?

In my experience, the answer varies considerably depending on the product owner's beliefs and skills in leveraging data and values in working with his or her team. If the product owner is proficient working with data and is highly collaborative working with the technology team, it is more likely he or she will present and leverage data in defining user stories. That alone isn't sufficient because to be successful, the technology team needs to partner in understanding the data, developing additional analysis, and leveraging the findings in developing great products. If the overall team is weak in leveraging data, then management should come in to influence the culture.

Teams can start this change with one simple question, "What data do we have?"
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What Data Scientists Can Learn From a Fourth Grade Teacher

Fourth Grade Lesson on Data Science
Last week, I attended my fourth grade son's open house at school and sat through his math class. The teacher walked through the steps to solve a word-math problem. You know the kind - "Sally has $3.85, spends $1.20 on materials for lemonade then sells four cups at $0.65. How much money does she have?"

This was a great experience for me to observe. I am sometimes critical of US schools and generally feel that they need to do a better job preparing students, especially in math and science, in order for them to better compete in a global marketplace for skills and talent.

The teacher was on the right track by showing kids how to think methodically and solve problems. She completed several word-math problems, calling kids out to help her go through each step one at a time. I am certain that more kids "got it" because the teacher helped her students think, repeated by example, and required kids to show their work along with the answer.

It was very interesting to see the teacher discuss "data" with kids. Did I learn about data in fourth grade? I doubt it. I wonder if it's lessons like these that are early preparations for some of them to become the next generation of data scientists. Bravo!

Lessons for Today's Data Scientists

This fourth grade lesson is applicable to today's scientist. My interpretation is below with italics showing the fourth grade approach and bold showing the data scientist methodology.

  1. Slowly read the story - Data scientists have to attentively listen to their customers, colleagues, and others to help get a sense of what types of analytics are valuable, what decisions they will drive, and how discovery can lead to action.
  2. Understand the question - Sometimes, an executive will give the Data Scientist a list of questions, but more often than not, the Data Scientist has to be adept to ask good questions. (Note - see my older post on Asking Smart Big Data Questions.)
  3. Circle the important data - Visualize the most relevant data and apply algorithms to help discover data relationships and correlations. Use the visualizations to develop stories and tools that drive and backup insights.
  4. Check your response - Amazing how many times analysts deliver insights out of context. The data scientist needs to consider multiple approaches to insure the results are conclusive, or check with subject matter experts to insure results are reasonable.
Most important - possibly the subject of another post - Data Scientists need to "show their work". Data driven insights need to be backed up with views of the data, the algorithms, and the assumptions.





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About Isaac Sacolick

Isaac Sacolick is President of StarCIO, a technology leadership company that guides organizations on building digital transformation core competencies. He is the author of Digital Trailblazer and the Amazon bestseller Driving Digital and speaks about agile planning, devops, data science, product management, and other digital transformation best practices. Sacolick is a recognized top social CIO, a digital transformation influencer, and has over 900 articles published at InfoWorld, CIO.com, his blog Social, Agile, and Transformation, and other sites. You can find him sharing new insights @NYIke on Twitter, his Driving Digital Standup YouTube channel, or during the Coffee with Digital Trailblazers.