Favorite 2015 Schmarzo Big Data Blogs

A top Big Data influencer lists, outlines, and summarizes his favorite blog posts of 2015. Gain some additional insight into various data science topics with some of these great entries.

It’s that time of year that I review everything that I’ve written over the past year and share my favorite blogs. As many of you know, I travel frequently and because I’ve continuously seen every airline movie, I have plenty of time to write. And according to the commentary, every now and then I have a good one. So here are my Top 10 Blogs from 2015!

#10EMC World Day 1: Big Data Business Model Maturity Index and Analytic Profiles. I believe that this is the first blog where I introduce some very important data science concepts: Analytic Profiles and Scores. I talk a lot about the power of Scores in delivering metrics that are potentially better predictors of performance. Scores are important in supporting the decisions you are trying to make and the actions or outcomes you are trying to predict. And Analytic Profiles are how we bring all the analytics insights to support the organization’s key business initiatives.

For example, let’s look at what Bill Schmarzo’s Analytic Profile might be from the perspective of Starbucks.

  • Demographic Information. This is the basic information about me such as name, home address, work address, age, gender, marital status, income level, value of home, length of time in current home, education level, number of dependents, etc.
  • Behavioral Information: Now it gets interesting, as we want to uncover behavioral insights that are relevant for the business initiatives that Starbucks is trying to support. Depending upon the targeted business initiative (e.g., customer acquisition, customer retention, advocacy development, new product introductions), here is some behavioral information that Starbucks might want to capture about me: favorite drinks in rank order, favorite stores in rank order, most frequent time-of-day to visit a store, most frequent day-of-week to visit a store, recency of store visit, frequency of store visits in past week / month / quarter, monetary value of store visits the past week / month / quarter, how long do I stay at which stores, etc.
  • Classifications. Now we want to try to create some “classifications” about Bill Schmarzo life that might have impact on my key business initiatives such as: Lifestage classifications, Lifestyle classifications, Product preference classifications, Store visit classifications, etc.
  • Rules. We might also want to capture some rules or propensities about Bill’s usage patterns that we can use to support Starbucks’ key business initiatives, including: propensity to buy oatmeal when he buys coffee when traveling in the morning, propensity to buy a cookie/pastry when traveling in the afternoon, propensity to buy product in the channel, propensity to order online, etc.
  • Scores. We also may want to create scores to support decision-making and process optimization. Scores that we might want to create could include Customer Lifetime Value Score, Advocacy Score, Loyalty Score, Product Usage Score, Store Visitation Score, etc.

#9Weaving Data Hay Into Business Gold. Some organizations think that they will just jump data into the data lake and the deploy data scientists to “find needles in the data lake haystack.” But the “find a needle in the haystack” is the wrong analogy for the data lake. “Finding a needle in the haystack” is a data warehouse / Business Intelligence way of thinking about analysis; to slice-and-dice the data haystack trying to find needles.

However, data science with a data lake is more like trying to “weave data hay into business gold.” So instead of thinking about the data lake as this haystack from which you are trying to find needles, think instead about the data lake as the loom for your data where you weave data hay into business gold.

The primary goal of the data lake, from a business perspective, is to think differently; to enable your data science team to not search for random needles in haystacks, but instead think about how they can leverage the data lake to “weave the data hay into business gold.”

#8An Executive Mandate: Think Open Business. This blog discusses the power of thinking about an “open business” model, where we define “open” as creating a platform or ecosystem that allows third-parties (developers, partners, resellers) to provide value (and make money) upon that platform or ecosystem. Attacking the market with a closed business model introduces two significant liabilities:

  • You limit innovation to only the innovation that your company itself can deliver.
  • You force your customers to only have the choice of only the products that the original manufacturer can develop and deliver.

This blog discusses how Apple and Google addressed the innovation challenge with an open business model that encourages app developers to develop new, innovative products on top of their platform; the model allows third-party app developers to make money on top of the Apple and Google smartphone platforms. This adds considerable value to their respective platforms and in the process, Apple and Google are transforming their business models from a product-centric business model to a market-enabling business model; one of the key transformations as an organization seeks to “metamorphosize” their business models.

The blog concludes with an exercise on how my favorite kitchen appliance – the Vitamix could create a more open and creative marketplace for its products.

#7The Mid-market Big Data Call to Action. Small organizations seem to have this inferiority complex when it comes to big data. It would seem that the deck is stacked against the small organizations that lack the technology resources to invest or the data experience upon which to leverage to compete with the large companies in the area of big data. However, I think the opposite is true, that small organizations have a HUGE advantage over many of their larger counterparts with respect to integrating data and analytics into their business models including:

  • Smaller organizations have fewer data silos, so they have a much clearer view of their customers, products, operations and markets.
  • Smaller organizations have a smaller number of HIPPO’s (the Highest Paid Person’s Opinion) with which to deal.
  • Smaller organizations can unlearn faster.
  • Smaller organizations are less fixated on technology.
  • But the most important reason is that it is easier for small organizations to institute the organizational and cultural change necessary to actually act on the analytic insights.