Most marketers rely too heavily on intuition rather than data-driven decision making, with only 11% of decisions based on data. While a small number embrace data too aggressively, most marketers struggle with basic statistical concepts. The top performers focus on higher-order goals and apply only the most relevant data and insights, while avoiding distractions. Marketing leaders need to constantly reinforce goals, emphasize data-driven decision making, and address common errors to help marketers better utilize data.
What is big data? What is analytics? What is marketing? Are all managers data savy? How does statistics affect strategic decision making? Watch this ppt to understand this and much more
This is a presentation for the HBC analysis on the article "Marketers flunk the Big Data" during the internship under the guidance of Prof.Sameer Mathur {Ph.D,Carnige Mellon} ,IIM-LUCKNOW
What is big data? What is analytics? What is marketing? Are all managers data savy? How does statistics affect strategic decision making? Watch this ppt to understand this and much more
This is a presentation for the HBC analysis on the article "Marketers flunk the Big Data" during the internship under the guidance of Prof.Sameer Mathur {Ph.D,Carnige Mellon} ,IIM-LUCKNOW
I have made this presentation for my task on my internship training on Data Analytics with Managerial Relevance based on the article(https://docs.google.com/document/d/1nLu6PMKh-FwfFawCZsHq5nzAKok9Bbe988-kDkcnnHM/edit#).This presentation gives a glimpse about the obstacles due to marketers in data analysis and the improvement steps as a manager.
Pharma is under pressure to optimize operations and maximize performance. The industry is searching for ways to use, combine and integrate technology across every department to analyse data, build knowledge, and improve communications, internally and externally.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. The big-data explosion is driving a shift away from gut-
based decision making.
Marketing in particular is feeling the pressure to
embrace new data-driven customer intelligence
capabilities.
3.
4. A recent CEB study of nearly 800 marketers at Fortune
1000 companies found the vast majority of marketers
still rely too much on intuition — while the few who do
use data aggressively for the most part do it badly.
INTUITION NOT ON INTUITION
6. Insight 1 : Most rely too much on gut
On average, marketers depend on data for just 11% of
all customer-related decisions.
In fact, they said that more than half of the information
came from their previous experience or their intuition
about customers
7. A majority struggle with statistics
When we tested marketers’ statistical aptitude with
five questions ranging from basic to intermediate,
almost half (44%) got four or more questions wrong
and a mere 6% got all five right.
So it didn’t surprise us that just 5% of marketers own a
statistics text book.
9. Some are dangerously distracted by data
While most marketers underuse data, a small fraction
(11% in this study) just can’t get enough.
The problem is, they don’t have the statistical aptitude
or judgment required to use data effectively.
10. In management positions, these people can wreak
havoc by creating endless fire drills and preventing
anyone from sticking with projects long enough to
achieve the best results.
Worse, many marketing disciplines (especially direct,
digital and loyalty marketing) unwittingly encourage
these behaviors and end up magnifying the problem.
11. Insight 2: The best focus on goals and filter out noise
Today’s top performing marketers as rated by the
managers (a profile we call “Focusers“) have three key
qualities: comfort with ambiguity, ability to ask
strategic questions based on data, and narrow focus on
higher-order goals.
Together, these traits help them filter out noise and
apply only the insights or data points that truly matter
for long-term success.
12.
13. The bad news for marketing leaders is that ability to
filter out noise is rare (only about 10% of marketers
excel here) and hard to teach.
The good news is that a well-guided team environment
can protect noise chasers from themselves — by
providing blinkers that keep “bright shiny objects” out
of view.
Managerial relevance
14.
15. To drive effective data use, the best marketing leaders
reiterate critical business goals constantly (to keep them
front-of-mind despite distractions), teach marketers to put
data front and center in their decision making, and sensitize
marketers to common data interpretation mistakes.
This enables even the most distractible data lovers to
overachieve.