Medici Technologies common problems with data analysis

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Medici Technologies provides predictive algorithm development and data analytics
services, designed to enable companies to transform the data they have into the
product they want.
Medici accelerates client progress toward goals using a structured approach that
incorporates project management, parallel algorithmic assessment, optimization,
robustness testing, and validation.
This systematic approach solves difficult, complex multivariate, life science algorithm
problems.

Typical Sequence
In a scenario that is very common,
companies are formed to make new
measurements, improve the performance of
existing measurements, or make the
measurement in a cheaper or more
convenient manner.

Contact: info@MediciTechnologies.com

Data Analysis Group on Critical Path
This process often involves a core technical
staff composed of a hardware engineer,
software engineer and a data analysis
person.
The typical development cycle consists of a
financing event, building or refining the
instrument platform, conducting clinical
studies, and processing of the data to
demonstrate readiness for the next phase.
This final processing step often involves
sophisticated signal processing and/or
predictive algorithms that must convert raw,
noisy, imperfect data into the desired result
and achieve the target performance.


Pressure on Data Analysis Group
Delays in other stages of the development
often put the data analysis group under
enormous time pressure, which is
exacerbated by residual issues in the
prototype instrument performance,
unforeseen errors or biases in the clinical
study, or references problems associated
with the gold standard.
The analysis team is under extreme
pressure to deliver and keep the company
viable.


Mistakes Can Occur
At this point, other employees,
management and the Board of Directors are
looking to the data analysis group to
complete their activities so that the
company can move forward.
Although these data analysts are typically
extraordinarily capable, the enormous
pressure, time constraints, and long hours
of work can lead to mistakes or can stifle
creative problem solving.
Sometimes that means taking shortcuts on
the code and infrastructure development, or
code validation is not rigorously completed.
Mistakes can happen


Herculean Efforts Fail
The data analysis group will put in
superhuman efforts in an attempt to
produce the needed results.

They may start sleeping in the office or
forgo sleep altogether.
Although clearly noble, such practices are
not the best approach.


Upset Investors
Despite the data group’s best efforts, the
data may simply be too complex, the search
space too large and the analysis toolbox too
small to generate the results needed in the
time allowed.
This leads to slippage in timelines and
eventually an angry Board of Directors.
Predictive algorithm development is poorly
understood by most Boards and
communicating systematic progress can be
challenging.
The combination of missed timelines, lack of
tangible progress and a general erosion of
confidence morphs the Board into an angry
mob that is concerned with their
investment.


Options are Limited
At this juncture, the CEO and Board of
Directors will look at the options available
for the company to address their data
analysis bottleneck. There are 4 general
options.
1) The company could close, but this is
likely premature given data that
suggests adequate performance is within
reach.
2) The company could simply continue the
current approach, but continuing to
execute a process that has failed to
generate results is inconsistent with DNA
of most venture investors


Build Internal Team
3) Increase the internal capabilities of the
company. This option can be effective,
but most data analysis/algorithm
development activities are transient in
nature. The company requires an
enormous expertise but only for a finite
period of time.
If the company does elect to build an
internal group, the process is quite
expensive and slow, and upon
completion of the data analysis
activities, the board/management will
look to decrease the size of this group
given the significant salary expense
these individuals command. Such
reductions in staff can be demoralizing
to small companies.
Although building an internal group is
feasible, it is typically not entertained
given these problems.


Hire Consultants
4) Hiring external consultants is the typical
approach employed by most companies.
This approach may look attractive on the
surface, but is incredibly difficult to
implement effectively.


Most Valuable Resource Spread Thin
During this period of crisis, the internal data
analysis group is the company’s most
valuable resource. They understand the
problem, the instrument and nuances that
are critical to solving the problem, but they
likely have a backlog of solutions to
investigate.
Hiring and organizing external consultants
typically requires that the company’s most
valuable resource, the data analysis group,
focus on the operational issues rather than
the solution of the data analysis problems.
The finite resources of the data analysis
group are stretched thin. The stress of the
situation may be compounded by the
internal data analysis group’s inexperience
in hiring and program management.
Additionally, small companies may not have
the infrastructure needed for multiple
people to successfully work on complex
predictive algorithm development.


Many External Consultants Hard to Coordinate
A group of external consultants do not
constitute a team and typically do not work
in a coordinated fashion.

As individuals, they utilize different tools,
different software platforms, and different
data output structures.
They may generate a flurry of activity but,
in the absence of a cohesive, structured
approach, the results are likely to be subpar
and represent a wasteful investment.


Medici Fills Void
Medici Technologies was established to fill
this void in data analysis capabilities.
By providing critical resources, years of
experience in teamwork, and project
coordination, Medici is able to accelerate the
internal data analysis capabilities of your
company.
When hiring Medici, you are obtaining a
structured approach supported by a team of
individuals with diverse expertise who know
how to work together, as well as an
established program management process,
and a modular toolbox for rapid data
processing and algorithm development.


Sequential Investigation is Expensive
The Medici process integrates smoothly with
your analysis team.
Your company has already invested in these
individuals, and their depth of knowledge
and historical experience with the problem
are critical to resolving residual issues.
Your internal team most likely has a
multitude of ideas for solving the problem,
but the solutions are typically pursued in a
sequential fashion.
Sequential development can be a
remarkably time-consuming process due to
code development and review, and
computer processing resources may be
limited.


Accelerated Investigation
Medici Technologies accelerates
development by enabling your company to
pursue multiple ideas in parallel and directly
compare results.
Medici’s Rapid Assessment System is a
modular framework for predictive algorithm
development that enables rapid assessment
of multiple combinations of processing
methodologies and the associated
parameter spaces.
New methods can be easily plugged into the
framework and implemented in the toolbox.
This capability, when combined with your
company’s internal resources, creates an
optimal combination of expertise and
modular tools for solving the data analysis
or modeling problem.


Medici is a well-oiled analytical machine
composed of program management,
algorithm development, and sub-modeling
capabilities.
Program Management
Predictive algorithm development tends to
degenerate into a chaotic or nondeterministic research process.
Medici Technologies has developed the tools
and techniques to effectively manage and
track the progress of predictive algorithm
development.


Defined Goals
This process starts with absolute clarity on
the objectives and the domain space within
which a solution may exist.

It is critical that all individuals, from the
board to the data analysis group, have
complete clarity and agreement on the
objectives of the algorithm development
activity.


Understood Risks
Pragmatic assessment of all risks associated
with the project.
These risk should be ranked and to the
extent possible pulled to the beginning of
the timeline and afforded appropriate
resources.


Work Breakdown Structure
Clarity on objectives and the a solid risk
assessment with a general understanding of
the project status allow the completion of
the work breakdown structure
The work break down structure is a key
element in developing an effective project
timeline.


Communication and Status Reporting
Communication and visibility are essential
components of the Medici approach.
Progress dashboards and performance
leaderboards create clarity on objectives
and reinforce their importance.
Dashboards play a critical role in
communicating overall progress to the
board, management, and other employees.

During this period of stress, it’s remarkably
important to communicate to all interested
parties about both the plan and overall
progress.


Project Timeline Management
These foundational inputs enable the
development of a pragmatic and informed
project timeline.

Many times, project timelines are developed
and then placed in a drawer and not used.
Medici Technologies believes that project
timelines are in fact the best tool for project
management and should be examined,
updated, and challenged on a daily basis.


Predictive Algorithm Development
The ability to examine, process, and
understand complex analytical problems is
facilitated by having modular tools, parallel
capabilities, and reporting mechanisms that
enable a detailed examination of each
algorithm approach investigated.


Rapid Evaluation
Medici’s Rapid Assessment System is
composed of a large code base, a
computing cluster, algorithm status
messaging system, and results databases


Structure and Systematic Process
The Rapid Assessment System can quickly
create a large number of “processing
recipes” that contain varying combinations
of preprocessing techniques, feature
selection methods, noise filtering,
regression methods, and sub-modeling.
These recipes are all evaluated in
“experiments” that can involve hundreds to
thousands of different processing recipes
within a controlled framework.


Design of Experiments Investigation
The use of such controlled data analysis
experiments enables Medici to effectively
search for the best processing methods and
to identify subtleties within the data as well
as identify interactions that may be
unobserved when changing one variable at
a time.


Finding Areas of Highest Performance
The resulting statistical analysis of the
results enables Medici Technologies to
identify the most effective methods, their
optimal parameters, and any hidden
interactions.
These results are used to further optimize
performance by predicting superior
processing recipes that have not yet been
evaluated.


Testing and Selection of the Best Method
Results are further examined for stability,
robustness, and predictive performance.


Selection of the Final Prediction Process
The weeding out of inefficient algorithms
continues until a final predictive algorithm
with improved performance is selected.

The entire algorithm development process is
highly structured, modular, efficient, and
very effective.


Sub-modeling or Physiological
Segmentation
The last element of the Medici analytical
toolbox, is a very powerful and
differentiated element—a sub-modeling or
physiological segmentation for improved
algorithm performance.


The Power of Segmentation
Population segmentation, for the purposes
of product placement or advertising, has
been used very successfully by Google,
Netflix, Amazon, and Target, among others.
These companies have divided the
population based on buying characteristics
so that advertising can be targeted to a
given demographic.


Corrections Based on Physiology
Medici Technologies has developed a toolbox
for population segmentation based on
physiological differences that allows for
improved measurement results.
The value of the approach is easily
appreciated if you consider eye
prescriptions, for example. If everyone were
required to use the same prescription, it
would work for some people but not for
others.

The process of determining the correct
prescription starts with population
segmentation based on the focal
characteristics and size of the eye. Using
this information, the ophthalmologist is able
to prescribe the correct prescription based
on those vision characteristics.
Ophthalmology prescriptions are not specific
for just one person, but rather for a general
segment of the population that has similar
vision characteristics


Algorithms Tuned for Physiological Differences
The ability to segment the population for
the use of the correct measurement
algorithm based upon measurable
physiological parameters is extremely
powerful.
Medici has developed a methodology for
determining sub-populations with similar
characteristics for the realization of
improved performance results


Medici Provides Smaller Companies Unique Capabilities
Medici allows smaller life science companies
the ability to access predictive tools
previously only available to much larger
companies.
If you’re looking for an optimized, effective,
low-risk approach to reach your analytical
goals, call us.
Medici Technologies has the organizational
acumen, processing capabilities and the
teamwork skills to help.


Contact Medici
Contact Medici Technologies

Ries Robinson
Info@MediciTechnologies.com
(505)-264-3122


Medici Technologies common problems with data analysis

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