With all the buzz around large data analytics, perhaps not enough attention is being provided to data quality and also the identification of models built on the data. Regardless of their deterministic nature, algorithms are simply as good as the data their modelers work with.
Simply defined, algorithms adhere to a series of instructions to address a specific problem based on the input variables in the underlying model.
1 example could be the flash crash which occurred on May 6, 2010. Within a couple of minutes, The Dow Jones Industrial regular dove 1,000 points simply to recover less than 20 minutes later. While the cause was never explained, most market participants concur that even quantitative algorithms have been to blame. With algorithms accountable for up to 75% of trading volume, the capacity for future calamitous events is much likely. Regardless of the efficiencies, the absence of human intervention resulted in a cascade of events that triggered greater trades to the container the market further. Perhaps you have learned nothing against the portfolio insurance of the 1980s that caused the 1987 crash?
On a more individual level, algorithms based on personal data, such as zip codes, payment histories and health records have the capacity to be discriminatory in determining insurance prices and credit scores. Include social data into the combination and the resulting assumptions in models can skew outcomes even further.
Another example could be the revelations about the NSA’s collection and analysis of personal information. Governments have enacted legislation to allow data mining for indirect or non-obvious correlations in the name of domestic security. Similar algorithms are being used for profiling by municipal police departments. A modeling mistake may have devastating effects on each day citizens. And the possible breach of personal privacy leaves a gaping hole in governance.
Modeling in fields with controlled environments and dependable data inputs, such as medication discovery or predicting traffic patterns provide scientists the true luxury of time to support their models. However, in web search, the time horizon might be two seconds and onto a trading ground, milliseconds.
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Focus on model validation
As huge data becomes more pervasive, it becomes even more essential to validate models and the integrity of data. A correlation between two variables does not necessarily signify this one causes the other. As such, this also distorts the analysis of the residuals. Models for spatial and temporal data would just appear to complicate validation further.
Data management tools possess improved to significantly increase the reliability of the data inputs. Until eventually machines devise the models, focus on the veracity of the data might increase model validation and reduce, not eliminate inherent bias. Additionally, it would also yield valuable data.
Ways to enhance data quality
Bad data is not only an IT problem. Missing data misfielded attributes and duplicate records are among the causes of flawed data models. These, in turn, undermine the organization’s capability to execute online strategy, maximize revenue and cost opportunities and adhere to governance, regulatory and compliance (GRC) mandates. Organizations need to enact regulations, policies, and processes to identify root cause and assure improved data integrity.
Following Are Some antidotes for common data quality problems:
Create enterprise-wide metadata with clear definitions and rules. This reduces errors for what data users can enter into a particular field, such as customer name, address, SSN, vendor, serial number or section number. This metadata should be used for integration with all applications, including those behind the firewall and in the cloud.
Use data quality tools for real-time analysis of relevant information. The data quality solution needs to flexibly deploy with application servers, cloud environments or in an enterprise service bus (ESB). Mechanisms must exist for internal and external users to double-check the accuracy in their data entrances.
Establish policies and standards for data handling. Departments must be prevented from using unsanctioned applications or data stores regularly create bogus data or variants which can be incompatible or not properly backed up. These have to be endorsed by senior management to assure adherence and facilitate enforcement because of it.
Profile data from the outset. That is to be certain that data converts smoothly from source application to target. This includes custom code and special processes beneath the data to know the exact shape and syntax in the source.
Deploy performance management tools. This includes schema checks in job streams to test this data remains complete and correctly formatted, as nicely as real-time monitoring to assure end user data experience.
Inventory the full infrastructure and application environment, including external cloud/SaaS applications.
Document all of IT initiatives, including data quality standards, responsibilities and timelines. This helps define what is happening in databases and how various processes are interrelated.
Make data governance an ongoing effort. This is to make sure that as data usage and the data itself changes, the data handling rules and policies adjust accordingly.
Every update of Panda and Penguin in recent years has attracted delight to some SEOs and sorrow to others.
So, what probably issues may arise as a result of faster algorithm upgrades?
Troubleshooting algorithmic penalties
With an algorithmic penalty, they may possibly not even take note that the problem is present.
The easiest way to determine if your website has endured from an algorithmic penalty is to match with a drop in your traffic with the dates of known algorithm updates (pursuing a tool like Penguin).
Another easy way to determine whether there is an algorithmic penalty is to see whether your site rankings high in Maps but poorly for organic for distinct phrases.
Unfortunately, without having the dates when updates occurred, SEOs will need to take a look at far much more data — and it will be difficult to diagnose algorithmic penalties.
Misdiagnosis and confusion
So if you’re keeping a detailed a timeline of website changes or actions, these cannot match up with when a penalty occurs. There could possibly be other issues with the server or website changes which you might not know of which could cause a good deal of misdiagnosis of penalties.
A few SEO companies will charge to check into or “remove” penalties which don’t actually exist. A number of the disavow files which these companies submit will likely do a lot more damage than good.
Google could also roll out some number of other algorithmic changes which could affect ranking, and SEOs and business owners will automatically think they will have been punished (because in their minds, any unwanted change is a penalty). Google Search Console actually needs to inform website owners of algorithmic penalties, however, that I see very little chance of the happening, particularly because it would be giving away more information about what the search engines are looking for in the way of negative factors.
There is going to probably be big money in spamming companies with bad links, then showing companies these links and charging to remove them.
The best/worst section is that this model is more sustainable forever. Just spam far more links and continue charging to remove. Small business owners are going to think it’s a competition company or simply their old SEO company out to get them. Who’d suspect the company trying to help them combat this right?
There’s going to become far more black fur testing to see exactly what you can get away with. Sites will probably be punished faster, and lots of the churn-and-burn strategy may go away, but then there is going to probably be new dangers.