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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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
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.
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.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Diagnostic test
1. Diagnostic Test
1. Choose the correct answer.
1.Where _____ your sister work?
a. is b. does c. has
2. I _____ often go to the theatre.
a. don’t b. hasn’t c. haven’t
3.She _____ at the moment.
a. studies b. ‘s studying c. studied
4. I’m excited _____ my exam.
a. from b. about c. for
5. She’s the _____ aunt in the world!
a. cool b. fantastic c. best
6. What _____ going to eat?
a. she b. she’s c. is she
7. The train _____ at seven o’clock.
a. leaving b. ‘s leaving c. going to leave
8. They are _________ to Japan next month.
a. going fly b. flying c. fly
9. ‘Was Grace visiting her mum this morning?’ ‘Yes, she _____.
a. is b. wasn’t c. was
10. How _____ is this cap?’ ‘£5.’
a. much b. many c. a lot of
11. Where did you _____ that ?
a. saw b. see c. seen
12. Where’s Laura? I don’t know. I _____.
a. don’t see b. agree c. have no idea
13. They _______ at the hotel, last week.
2. a. didn’t see her b. can’t see her c. weren’t see her
14. In ten years’ time, I’ll _____ married.
a. are b. be c. being
15. I _____ think I’ll have a sister.
a. won’t b. don’t c. sure
16. You should _____ different trousers.
a. wearing b. to wear c. wear
14. Have you ever ____ to Albania?
a. been b. gone c. went
16. You _____ be quiet. It’s a library.
a. needn’t b. don’t have to c. should
17. We stayed ______ a really nice hotel next to the beach.
a. at b. on c. by
18. They’re talking _______the weather forecast.
a. from b. for c. about
19. They are really looking forward ______ our meeting us.
a. for b. from c. to
20. _____ in my family likes jazz music.
a. Anyone b. No one c. Someone
21. My parents bought a new car last weekend. I really like my ______ car.
a. parents’ b. parents c. parent’s
22. This food is _____ unhealthy.
a. much b. enough c. too
23. Would you like _____ a birthday party?
a. having b. to have c. have
24. I _____ go to school today! It’s Sunday.
a. must b. shouldn’t c. don’t have to
3. 2) Choose the correct answer
1. What is this? It's ...
a. a blackboard eraser b. a pencil case
c. a school bag d. a desk
2. What is this? It's ...
a. a desk b. a pencil case
c. a pen d. a sharpener
3. What is this? It's ...
a. a pencil sharpener b. a chair
c. a desk d. a ruler
Name: The Worm Test
Date:
1) Answer the questions
A) What colour is D) What colour is an orange?
an apple?
It is ______________ ___________________________
B) What colour is
a
banana? E) What colour is a kiwi?
_______________________________
It is ______________
C) What colour is
a
coconut?
___________________________
-
4. What is this? It's ...
a. a desk b. a folder
c. a chair d. a ruler
