Big Data Driven Marginal Gains

British Cycling and Big Data

How do you take a team of average Olympic Cyclists and turn them in to 10 gold medals and

3 Tour de France’s?

The answer lies in a concept known as “Marginal” or “Aggregated” gains. The basis of the

concept is that small modifications to the periphery of an operation; when aggregated; will

deliver considerable change to the operation without major cost or disruption.

The problem with applying the concept to business is that it took the cycling team 8 years to

unearth enough opportunities for marginal gains to be able to deliver enough of an

aggregated gain to produce a tangible result. Businesses simply don’t have that amount of

time.

Big data; since it’s conception has been the plaything of computer scientists and

technologists. Whilst the analytical modelling associated with ‘mining’ big data has delivered

a few tangible returns to those businesses that have invested in it (namely improvements in

processing time associated for data analysts), there still represents a major issue in big data

visualisation for business as a whole.

DatMyn uses the concept of Big Data and algorithmic data mining to deliver a cutting edge

analytical tool; which can identify multiple marginal gain opportunities for organisations; to

any level of granularity; in a vastly reduced timescale. The marginal gain opportunities can

be identified across any business unit or function including, customer, employees and

operations.

As a secondary process DatMyn uses analytical modelling to predict the outcome of

implementing the suggested marginal gains.

Techy Part

The first step in delivering the DatMyn marginal gain output is to create a Data Warehouse.

DatMyn use both Microsoft SQL and an open source software programme known as

“Cassandra” when creating Data Warehouses (Cassandra actually began life at Facebook

as the inbox search software). Both represent cutting edge programmes that will suit

different organisations depending upon the focus of their marginal gain requirements.

The Data Warehouse contains 2 fundamental elements:

1) Subjects- These are the different areas of a business about which data will be

collected to populate the warehouse. The higher the level of organisational subject

granulation the more specific and targeted the marginal gains output will be. For

example if an organisation chooses to populate based on subjects that are just

business units (HR, Finance, Sales) etc then any marginal gain outputs will be

suggested as wholesale to specific units. If however the organisation decides to

populate based on subjects that are individual employees then the marginal gain

outputs will be suggested per employee or employee sub-set.

2) Facts- Facts are the responses to questions asked about every single “Subject” in

the Data Warehouse. The only caveat to this data creation is that each subset (Data

Mart) must be asked the same questions. For example the same questions need to

be asked about Machine A as the do Machine B, or Employee A and Employee B. As

with the “Subjects” the more granular an organisation goes (in this case the more

questions asked) the more specific the results will be. Questions can deliver yes/no

answers, be multiple option answers, numerical answers, factual answers, KPI based

data and written opinions. Facts cam be added to the Data Warehouse at any point.

Once this data is collated the Warehouse is complete. An organisation can choose to

segment the data in to different units (often by Territory or business unit) by a process called

DataMart creation. A DataMart is essentially a smaller Data Warehouse, the Mart cannot

exist without the warehouse but not vice versa.

What Good Looks Like/Training Data

The fundamental concept of DatMyn is to determine what minute factors of a particular

individual component of an organisation are the true drivers of operational efficiency.

The concept of “What Good Looks Like” allows an organisation to create their own

benchmarks for success. These may come from the perceived success of an individual

component or be a conceptual idea of success (referred to as training data). If the latter then

there is a requirement to provide conceptual or presumed answers to all questions relevant

to their particular DataMart (subset).

Once an organisation has defined “What Good Looks Like” the process of algorithmic

modelling can commence.

Algorithms used are bespoke to DatMyn but are loosely based around Microsoft SQL

Clustering, Association and Sequential Clustering.

These algorithms generate an output which highlights common responses to the “Fact”

answers provided at the data collation stage by those individual components (or training

components) that are classed as “Looking Good”.

A subsequent algorithm then mines the data further to search for individual components

whom are within 1 standard deviation of matching the highlighted responses of an individual

component. This process continues until the subjects in the dataset have been exhausted.

This process an individual component (determined by granularity) breakdown of areas for

potential improvement. These areas are tied to the bespoke questions asked during the data

collation stage and thus form the basis of the Marginal Gains action plan.

Fast Moving Customer Data

In today’s world the nature of a customer or prospect or product can change in a heartbeat,

they can go from “Looking Good” to being a standard deviation or more away and vice versa

in an instant.

With this in mind DatMyn offer an on-demand Marginal Gain Output Service specifically for

customer and product data.

We load “Fact” data directly in to your DataWarehouse via EDI (Ideally from a CRM or ERP

system) which is essentially frozen for an hour. If in that hour you wish to view which clients

or products “Look Good” and how you need to specifically target those who don’t you can

request the data up to 3 standard deviations from “Looking Good” and receive results

instantly.

What Marginal Gains Look Like

Whilst specific marginal gains are bespoke to any organisation and their component parts;

some generalised outputs are:

Adjustment of recruitment techniques for sales team so that prospective candidates

better match the attributes of those who “Look Good”.

Re-working of lunchtime breaks to correspond with the output of those employees

who “Look Good”.

Relocation of tea and coffee facilities to closer to a specific department.

Understanding of happiness values and engineering individual solutions based on

personality.

Re-modelling of premises to ensure that teams with a higher demographic of

smokers were closer to exits.

Free fruit in the workplace.

“Lazy day” implementation.

Geographical targeting of prospects.

Website function change for first level “linked products”.

Change of shelving location for specific product.

Brand change of hand soap in customer facing areas.

Allowance of demographic based options to a specific business team.

Re-allocation of shift patterns to ensure more efficient use of machinery (energy).

Re-allocation of procurement projects to procurement team members based on

mapping of personal facts versus facts on the seller business.

Implementing a slide to migrate parcels across warehouse.

Increase late night marketing emails to certain demographics of prospects.

Increase office temperature in areas where the average age of the business unit

exceeds 40.

Implementation of staff canteen following suggestion that productivity increases when

an employee does not leave site for a meal.

Free transport provision.

Corporate clothing provision.

Creation of bespoke interest marketing based on click rate of individual users.

High Page click users targeted with essentials offers.

Reward systems introduced based on individual preference.

Increased leg room for those employees over 6ft.

Increase in quality of company vehicle to bring in line with those taking a car

allowance option.

Encouraging stretching time in the afternoon.

Colour changes in specific areas of touch point marketing.

Implement target spots in urinals to reduce cleaning fluid used by cleaners.

Identifying “future stars” and supporting personal growth.

Specific colour painting in an office area.

Specific personality recruitment to change the output of a business unit.

Changing to single distributed paper towels.

Changing H&S regulations to allow for idle shutdown of machinery.

Change of POS material in storefront to correspond to local geography.

Site level recruitment decisions made based on how a “Good Looking” team appears.

The key to delivering a highly successful and bespoke Marginal Gains action plan is in the

level of granularity of subjects and in the detail of the “Facts”. Extensive delivery of both of

these areas will give a highly bespoke and targeted plan for deploying small changes that

when aggregated deliver big operational efficiencies.

Open Access

DatMyn can provide open access and modelling algorithms to users within any organisation.

Unfortunately we cannot allow access to make Data Warehouse changes due to the unique

coding mechanisms required to allow for algorithmic modelling.