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Corporate Data

Do You have Millions of Customer Records?

Or Do you have Tens of Thousands of Vendors Records?

Duplicates?

Do you really know your data?
How many records are duplicates?

Errors?

Which records contain inconsistencies?
What can be done?

Introducing Cordasearch

The Corporate Data Search Engine

Cordasearch

The groundwork of turning your data into a reliable asset.

State of the Art

Proprietary algorithms

Fully optimized code

Cordasearch

Proven in most demanding scenarios.

Cordasearch

So that you can really trust your every record.

Cordasearch

Real time results, even with hundreds of millions of records.

Cordasearch

No downtimes in a production system, ever.

Cordasearch

Whole another level of confidence in your data.

Cordasearch

Consider it done.

%

records in large datasets contain relevant inconsistencies

%

up to 7% records may contain some inconsistencies

%

companies do not have a good data quality solution in place

%

improvement with CORDASEARCH

 

Maximize your data quality with CORDASEARCH.

 

What we offer

Our mission is to raise quality of your corporate data, significantly better than achievable with any other solution, maintaining real-time response, at low cost.

Cordasearch is a specialized solution, focused on a single but most painful class of data quality problems. Perfected over 10+ years, it is most successful in what it does.

Cordasearch is easily adopted and most resource-friendly. You do not need to allocate additional people, time or organizational efforts to have your data fixed. We keep it as simple as possible to maximize data improvements.

• No modification of original data takes place, no affect to underlying data sources, non-invasive deployment = no extra costs. Cordasearch is a reliable low maintenance effort system with no downtime in a production system since its initial version deployment in 2006.

• Even if Cordasearch is used at new data entry only (e.g. clerk / operator), data errors are not becoming business errors any longer = no cumulative degradation of data quality.

• Particular record scope benefits: fixed error = avoided misunderstanding, costs are cut, time spared and business confidence improved, in each and every instance.

• General benefits: reliable data make analytics and BI reliable, while precision of projections and planning is taken to another level, no longer impaired by poor data quality.

Our approach has been built based on requirements and practices of large businesses with millions of records, companies which take their data most seriously, as their customers represent their core values.

Traditional approaches are sequential and holistic. There is nothing wrong with it, except that resources are engaged way before useful results are extracted. Even more importantly, it is not known what results could be expected, in the first place.

Our approach is direct and effective. Cordasearch resolves the problem in a non-invasive way, providing not only impressive quick wins to your business, but unparalleled results, as our ultimate priority. This makes Cordasearch an excellent ROI saver and a tool of choice for your business.

  • Real-time error tolerant search and establishing relations between records, tuned for particular business case decisions when and where most needed;
  • Database merger: two databases are linked or merged. This might occur, for instance, because of a corporate merger, a data consolidation, or to prevent duplication of effort by storing information common to multiple databases in a single enterprise-wide database;
  • Data enrichment from same/different data sources: completion of business data from reference data;
  • Data cleaning and normalization, removal of data noise, preparation for any kind of automatic processing;
  • Duplicate record removal or linkage: the same person, business, or organization is present more than once in a database. Duplicate records are removed or linked together;
  • Approximate search: finds records similar to a record entered online. Fraud protection and risk management uses.
  • Typical solutions: only up to 1/3 of correctable data quality issues are found and/or fixed. 33% 33%
  • Cordasearch – after the initial analysis/tuning cycle 45% 45%
  • Cordasearch – after the second analysis/tuning cycle 70% 70%
  • Cordasearch – after the third analysis/tuning cycle 85% 85%
  • Cordasearch – after multiple analysis/tuning cycles 90% 90%

Our Workflow

Data analysis

Data analysis. Creation and setting up data cleansing / enrichment models.

Tuning Search Engine

Setting up Cordasearch clues, predictors and classifiers.

Creating User Profiles

Creating and setup of user profiles, according to particular user application needs, and rules for business decisions.

Frequently Asked Questions

 

Cordasearch is not static and neither is data. It takes several analysis/tuning cycles to get the most out of the data. The analysis/tuning cycles are done by us in order to get top notch results based on past data dynamics. Depending on a use case, new information gathered from the data not only improve or resolve daily data related issues, but pave way to a better and stricter implementation of data governance policies and a whole new level of initiatives.

Our top priority is to concentrate on the most important datasets and get the most out of them first, because we know that most critical data deserve most attention. This approach puts a lot of focus on data related issues and their implications. Reaching the best solution in each business case involves extensive testing and tuning, but the result is more than rewarding. Newly available information presented by Cordasearch elevates data usage to whole another level.

This is recognized and appreciated by our users, and it is also reflected in Cordasearch deployment phases.

What kind of clients do you work with?
With the ones where corporate data quality is most critical, where there are large databases with millions of records, such as:

 

  • banks
  • insurances
  • government
  • telecoms
  • utility companies
  • middle-sized and large enterprises
What are the deployment phases?
When the system is first deployed, the initial tuning has already taken place. We aim for and usually achieve results which greatly exceed any already existing scenario, be it unprocessed or clean and neat data (even coming from an existing data quality solution).

From the point of user satisfaction, the initial deployment is the big step forward, and deployment phases may be perceived as new and improved versions of the software.

What is the dynamics of tuning cycles?
After the initial deployment, the company immediately starts to benefit from the newly available data insights, but we continue with tuning of the system parameters. In an environment of normally expected data churn, the results usually reach their best after 12 months, which is about two or three tuning-deployment cycles.

Further dynamic depends on various factors related to data churn, business requirements and policies. We may propose next steps after a 12 months period, in accordance with data stewardship priorities of a company.

What is the metrics of improvement of data quality?
Apart from the fact that data governance policies can provide broad boundaries for data quality metrics, in terms of daily data usage, there are usually detailed references to bad data quality instances there, waiting for improvement. Since business inteligence reports can be dependent on subtle metrics, we usually go for the bottom-up approach, to identify the most painful data related problems and try to tackle it from the data quality aspect.

Probably the best piece of technology for searching and matching people and companies in the country.

An excellent solution, delivered in time and within the budget.

Flawless, keep up the good work!

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