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Copy of SM: Strategic Analytics
1
Strategic
Analytics
Joe Arun, SJ
1. What is SA?
What is
Strategic Analytics?
Analytics that were used to predict what customers might buy; to close less profitable stores, branches, and product lines; or even to develop new service offerings
Definition
Definition
Strategic Analytics are those that make a company's strategy or business model possible
In a survey of nearly 65 Fortune 1000 or industry leading firms, 72% of large, sophisticated companies have not achieved data-driven cultures
65%
have not created a data-driven organization
53%
not yet treating data as a business asset
52%
not competing on data and analytics
Survey
Common elements
Common elements
To truly leverage the value of strategic analytics, companies need to have some common elements in place.
Data and technology
The right talent and skill sets
A data-driven culture
Economist proclaimed that data is now
“the world's most valuable asset”
The 5 elements
A company must perform solid work on five components, each reasonably aligned with the other four. Missing any of these elements compromises the total effort
- Quality data
- Means to monetize that data
- Organizational capabilities
- Technologies to deliver at scale and low cost
- Defense
5 elements
Quality data
Quality Data
Companies need data that is properly defined, trustworthy, relevant to the tasks at hand, and structured in a way that makes it easy to find and understand
Monetize data
Means to monetize that data
Companies need a business model for putting that data to work profitably
Organizational capabilities
Companies need the right talent—both
technical experts and general data skills up and down the org chart— as well as a structure and culture that allow data to be shared
Technologies
Technologies to deliver at scale and low cost
Companies need basic technologies in place for data storage, processing, and communication, as well as more sophisticated technological tools, to
help scale and deliver on their data efforts
Defense
Defense
Companies must minimize risk by following the law and regulations, focusing on cybersecurity and privacy, maintaining
relationships with customers, and keeping an eye on competitors
2. Predictive Analytics
Predictive Analytics
With today's high demand for data scientists and the high salaries that they command, it's often not practical for companies to keep them on staff.
Instead, many organizations work to ramp up their existing staff's analytics skills, including predictive analytics
Don’t Fall for Buzzwords
—Clarify Your Objective
3 Dont's
- Clarify objectives for what the team needs to learn
- Focus on a specific skill set or role, such as becoming a "predictive analytics manager,” rather than something like “data scientist,” which can be vague
Don’t Lead with Software Selection
—Team Skills Come First
Team skills
- Emphasize skills before looking at software selection. A machine learning tool only serves a small part of what must be a larger organizational process.
- Prepare teams to manage machine-learning integration first, and hold off selecting analytics software until later
Don’t Leap to the Number Crunching
— Strategically Plan the Deployment
Strategically Plan
- Avoid jumping to number crunching
- Each predictive analytics project follows a series of steps that begins first by establishing how it will be deployed and then working backward to see what you need to predict and what data you need to predict it
What data is & isn't good
What data is & isn't good
Data can only do so much. It can tell you what is happening, but it will rarely tell you why
To bring the two together, leaders need to combine the advanced capabilities of big data and analytics with qualitative approaches
What of a problem
Data can determine the “what” of a problem
Individual patterns can show up in data, revealing the “what" of a problem. Upon seeing these patterns, people may make assumptions about the root causes of the behavior.
But these assumptions are only
guesses and are not reliable for determining a solution
Why of a problem
Data rarely reveals the “why”
- To discover the “why,” engage in qualitative research, such as conducting interviews and focus groups and using in-depth observation
- Also consider what other factors that can't be seen in the numbers may be affecting the data
3. AI Driven decision making
Data Driven to AI- Driven
- The term “data-driven” even implies that data is curated by—and summarized for—people to process
- We need to evolve from data-driven to AI-driven work flows.
- Each term reflects different assets, the former focusing on data and the latter processing ability
- Humans and AI are both processors, with very different abilities
Evolution of Decision Making
- Just 50 to 75 years ago human judgment was the central processor of business decision making.
- Professionals relied on their tuned intuitions, developed from years of experience
- Experience and gut instinct were most of what
was available to discern good from bad,
high from low, and risky from safe
Contd.
Evolution of Decision Making
This is the result of hundreds of thousands of years of evolution where, as early hunter-gatherers, we developed a system of reasoning that relies on simple heuristics—shortcuts or rules of thumb that circumvent the high cost of processing a lot of information
Contd.
Evolution of Decision Making
Imagine a group of our hunter-gatherer ancestors huddled around a Camp fire when a nearby bush suddenly rustles.
A decision of the “quick and almost unconscious” type needs to be made: Conclude that the rustling is a dangerous predator and flee, or gather more information to see if it is potential prey—say, a rabbit, which could provide rich nutrients.
Contd.
Evolution of Decision Making
Our more impulsive ancestors—those who decided to flee—survived at a higher rate than their more inquisitive peers
Therefore, the trait for more impulsive decision making and less information processing becomes prevalent in the descendant population
Model 1: Human judgement
Model 1: Decision Making based on Human Judgement
- Survival heuristics become myriad cognitive biases preloaded in our inherited brains. These biases influence our judgment and decision making in ways that depart from rational objectivity
- A bias that for many decades was the central processor of business decision making
- Human intuition is inefficient, capricious, and fallible, and relying solely on it limits the ability of the organization
Model 2: Data Supported
Model 2: Decision Making based on Data- Support
- Humans playing the role of central processor still suffer from several limitations
- We don't leverage all the data. Summarized data can obscure many of the insights, relationships, and patterns contained in the original (big) data set
- Data is not enough to insulate us from cognitive bias
Model 3: AI
Model 3: Decision Making utilizing AI
- AI is less prone to humans cognitive bias
- AI has no problem dealing with thousands or even millions of groupings
- This model, better leverages the information contained in the data and is more consistent and objective in its decisions
- While humans are removed from this model, it's important to note that mere automation is not the goal of an AI-driven model
Model 3: AI & Humans
Model 4: Decision Making combining
AI & Human Judgment
- AI is first to reduce the workload on humans
- Human judgment can provide input for AI processing
- Humans are not interfacing directly with data but rather with the possibilities produced by AI's processing of the data
- By leveraging both AI and humans we can make better decisions than by using either one alone
How a German manufacturing company set up its Analytics Lab
Eg: ZF - Analytics Lab
ZF, a global automotive supplier based in Germany, feared a “Kodak moment,” a fatal disruption that could redefine its business. So it set out to launch a dedicated lab that focused entirely on data challenges
Lab's success ingredients
How a German manufacturing company set up its Analytics Lab
ZF noted four ingredients in the lab's success;
- Focus on the right internal customers:
Narrow your efforts to the departments in which data projects will have the most impact
2
How a German manufacturing company set up its Analytics Lab
Identify high-impact problems:
Select the projects within those departments that have the highest-value outcomes.
Consider three criteria: a clearly defined problem; data that is available, accessible, and good quality; and a motivated team.
Contd.
How a German manufacturing company set up its Analytics Lab
Place a deadline on execution:
Limit the execution phase to three months, and give your team the right to cancel the project if necessary, so they
can free up resources to use toward a better goal
Contd.
How a German manufacturing company set up its Analytics Lab
Consider other success factors:
Executive support, the perspective of an outside authority, and experts to answer domain related questions once the team is engaged with a problem
How Vineyard Vines uses analytics to win over customers
Eg: Vineyard Vines
When brothers Shep and Ian Murray cut their ties with corporate America to start a little company on Martha's Vineyard in 1998, their motivation was clear:
“We're making neckties so we don't have to wear them
Contd.
How Vineyard Vines uses analytics to win over customers
- Today, the company best known for its smiling pink whale logo offers much more than its signature neckwear. It manufactures a full line of “exclusive, yet attainable” clothing and accessories for men, women, and children
- The "little” privately held business has grown tremendously since its launch and currently has more than 90 physical retail locations and a highly successful e-commerce business
Contd.
How Vineyard Vines uses analytics to win over customers
Moving past batch-and-blast messages that sent the same text and images to millions, Vineyard Vines looked to authentic, relevant, and personalized communications through a retail marketing automation platform.
This platform created triggered campaigns based on an AI driven decisioning engine that determined the timing and content delivered for each shopper.
Contd.
How Vineyard Vines uses analytics to win over customers
The company also expanded into “predictive audiences,” which enabled the company to send personalized messages based on the customers’ online behaviors, purchase transactions, and level of personal engagement with the brand
Contd.
How Vineyard Vines uses analytics to win over customers
The Vineyard Vines example illuminates a few additional best practices:
- Provide your customers with the best experiences at every touch point; determine the best channel mix for each customer
- View detailed audience insights to understand customer health and engagement with products