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What is an algorithm anyway?
As per Wikipedia- http://en.wikipedia.org/wiki/Algorithm
an algorithm is a step-by-step procedure for calculations. Algorithms are used for calculation, data processing, and automated reasoning.
An algorithm is an effective method expressed as a finite list of well-defined instructions for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing “output” and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input
Where do I hear the word algorithm being used? Or the wat er cooler version- algols
- Pagerank – how Google calculates search results
- Public key cryptography – keeping credit card data secure
- Correcting errors (in CDs)
- Protecting passwords (cryptographic hash function)
- Perlin noise: generating landscapes in games
But Google NGrams thinks algorithms is flat in books
and Google Trends think the word is actually declining. But India remains a top user of searching for algorithms
But algorithms are increasing in ArXiv articles
and there is a bit of up and down in Algorithms Jobs
What do you think- do you hear the word too much or too little?
The Analytics (or stats) dashboard at WordPress.com continues to disappoint, and is a major reason for people to move out of WordPress.com hosting (since they need better analytics like that by Google Analytics which cant be enabled on the default mode)
Its not really beautiful unlike the rest of WordPress Universe!
It can be made better if people try harder! Analytics matters
Here are some points
1) Bar charts and Histograms are not really the best way to visualize trends across time
4) I cant even export my traffic stats (and forget an api !) so I am stuck with the bad data viz here
Western countries are running out of people to fight their wars. This is even more acute given the traditional and current demographic trends in both armed forces and general populations.
A shift to cyber conflict can help the West maintain parity over Eastern methods of assymetrical warfare (by human attrition /cyber conflict).
Declining resources will lead to converging conflicts of interest and dynamics in balance of power in the 21 st century.
The launch of Sputnik by USSR led to the moon shot rush by the US.1960s
The proposed announcement of StarWars by USA led to unsustainable defence expenditure by USSR.1980s
The threat of cyber conflict and espionage by China (and Russian cyber actions in war with Georgia) has led to increasing budgets for cyber conflict research and defense in USA. -2010s
If we do not learn from history, we are condemned to repeat it.
Declining Populations in the West and Rising Populations in the East in the 21 st century. The difference in military age personnel would be even more severe, due to more rapid aging in the west.
Economic output will be proportional to number of people employed as economies reach similar stages of maturity (Factor-Manufacturing-Services-Innovation)
In part 3 of the series for predictions for 2012, here is Jill Dyche, Baseline Consulting/DataFlux.
Part 2 was Timo Elliot, SAP at http://www.decisionstats.com/timo-elliott-on-2012/ and Part 1 was Jim Kobielus, Forrester at http://www.decisionstats.com/jim-kobielus-on-2012/
Ajay: What are the top trends you saw happening in 2011?
Well, I hate to say I saw them coming, but I did. A lot of managers committed some pretty predictable mistakes in 2011. Here are a few we witnessed in 2011 live and up close:
1. In the spirit of “size matters,” data warehouse teams continued to trumpet the volumes of stored data on their enterprise data warehouses. But a peek under the covers of these warehouses reveals that the data isn’t integrated. Essentially this means a variety of heterogeneous virtual data marts co-located on a single server. Neat. Big. Maybe even worthy of a magazine article about how many petabytes you’ve got. But it’s not efficient, and hardly the example of data standardization and re-use that everyone expects from analytical platforms these days.
2. Development teams still didn’t factor data integration and provisioning into their project plans in 2011. So we saw multiple projects spawn duplicate efforts around data profiling, cleansing, and standardization, not to mention conflicting policies and business rules for the same information. Bummer, since IT managers should know better by now. The problem is that no one owns the problem. Which brings me to the next mistake…
3. No one’s accountable for data governance. Yeah, there’s a council. And they meet. And they talk. Sometimes there’s lunch. And then nothing happens because no one’s really rewarded—or penalized for that matter—on data quality improvements or new policies. And so the reports spewing from the data mart are still fraught and no one trusts the resulting decisions.
But all is not lost since we’re seeing some encouraging signs already in 2012. And yes, I’d classify some of them as bona-fide trends.
Ajay: What are some of those trends?
Job descriptions for data stewards, data architects, Chief Data Officers, and other information-enabling roles are becoming crisper, and the KPIs for these roles are becoming more specific. Data management organizations are being divorced from specific lines of business and from IT, becoming specialty organizations—okay, COEs if you must—in their own rights. The value proposition for master data management now includes not just the reconciliation of heterogeneous data elements but the support of key business strategies. And C-level executives are holding the data people accountable for improving speed to market and driving down costs—not just delivering cleaner data. In short, data is becoming a business enabler. Which, I have to just say editorially, is better late than never!
Ajay: Anything surprise you, Jill?
I have to say that Obama mentioning data management in his State of the Union speech was an unexpected but pretty powerful endorsement of the importance of information in both the private and public sector.
I’m also sort of surprised that data governance isn’t being driven more frequently by the need for internal and external privacy policies. Our clients are constantly asking us about how to tightly-couple privacy policies into their applications and data sources. The need to protect PCI data and other highly-sensitive data elements has made executives twitchy. But they’re still not linking that need to data governance.
I should also mention that I’ve been impressed with the people who call me who’ve had their “aha!” moment and realize that data transcends analytic systems. It’s operational, it’s pervasive, and it’s dynamic. I figured this epiphany would happen in a few years once data quality tools became a commodity (they’re far from it). But it’s happening now. And that’s good for all types of businesses.
Jill Dyché has written three books and numerous articles on the business value of information technology. She advises clients and executive teams on leveraging technology and information to enable strategic business initiatives. Last year her company Baseline Consulting was acquired by DataFlux Corporation, where she is currently Vice President of Thought Leadership. Find her blog posts on www.dataroundtable.com.
Business Metrics (a partial extract from my upcoming book “R for Business Analytics”
Business Metrics are important variables that are collected on a periodic basis to assess the health and sustainability of a business. They should have the following properties-
1) What is a Business Metric-The absence of collection of regular update of the business metric could cause business disruption by incorrect and incomplete decision making.
2) Cost of Business Metrics- The costs of collection, storage and updating of the business metric is less than the opportunity costs of wrong decision making cause by lack of information of that business metric.
3) Continuity in your Business Metrics- The business metrics are continuous in comparing across time periods and business units- if necessary the assumptions for smoothing the comparisons should be listed in the business metric presentation itself.
4) Simplify your Business Metrics- Business metrics can be derived as well from other business metrics. If necessary and to avoid clutter only the most important business metrics should be presented, or the metrics with the biggest deviation from past trends should be mentioned.
5) Normalize your Business Metrics- Scale of the business metric units should be comparable to other business metrics as well as significant to emphasize the difference in numbers.
6) Standardize your Business Metrics- Dimension of business metrics should be increased to enhance comparison and contrasts without enhancing complexity. This means adding an extra dimension for analysis rather than a 2 by 2 comparison, to add time /geography/ employee/business owner as a dimension .