During the last 50 years, we have witnessed a tectonic shift related to the nature of the world economy. The entire planet is rapidly transforming from an industrial society into an information civilization, where manufacturing and distributing of goods becomes a derivative of the ability to process and react to the collected information. Such a change makes know-how and intellectual resources more valuable assets in comparison to conventional material equipment and physical infrastructure. And the core of this virtual capital is data of various kinds that is typically stored in an electronic data warehouse (EDW). 

For all kinds of enterprises, efficient handling of information from their business data warehouse is increasingly turning into a vital competitive differentiator that is highly instrumental in outselling their rivals and conquering new markets. Using it, companies shape their business strategies and map out development plans that pave the way to a financial bonanza. Yet, this goal can be attained only in case the quality of data is up to the mark.  

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Poor data quality: A scourge of our time

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In the volatile world of the early third millennium, everything and everyone seems to be in constant motion. People move to other locations, change their jobs and phone numbers, old companies go extinct and new startups crop up almost every week. Naturally, the accuracy of dossiers on them kept in a data warehouse can hardly be an invariable value, with between 30% and 43% of it getting obsolete during a year.  

For businesses, gaps in data veracity spell sheer losses. Companies mail letters to wrong addresses, fail to identify customers who call them or visit their website, or totally lose contact with them. As a result, a large portion of the clientele is frustrated or alienated. The loyal customer base shrinks. Such a development threatens the scope of sales and referrals and adversely impacts revenue prospects. On average, according to some estimates, using bad data costs commercial organizations over $30,000 per sales rep.  

Our experience in cooperation with commercial actors shows that nigh on half of them overestimate the quality of the information in their enterprise warehouse and are largely unaware of (or blind to) negative effects its poor quality can usher. What are the most typical of these setbacks? 

  • Lead generation. If the data in customers’ profiles is incorrect (outdated job titles, mistakes in data entries, inconsistent records, missing fields, etc.), the prospects of converting leads are rather vague. 
  •  Sales performance. Having incorrect client data at their disposal, sales reps can’t hope to build a reliable and lasting rapport with them. Moreover, they may turn out to contact the wrong person who is a poor fit for the company’s product or services. 
  • Marketing campaigns. Planning and implementing successful campaigns is next to impossible when invalid electronic addresses or inaccurate performance metrics stand in the way of seeing the endeavor through. 
  • Resources. Of course, financial losses are the most grievous consequences inflicted by bad data employment. But the gruesome cost of time and effort wasted in futile attempts to provide efficient functioning of the organization can be no less pernicious, exhausting the staff and undermining the team spirit. 
  • Company’s reputation. Incorrectly titled messages produce a negative impression on clients, who may conclude that the company doesn’t value them or has unqualified people on its staff unable to do the job properly. As a result, customer satisfaction plummets, bringing down the organization’s reputation in its wake. 

The nature of the poor quality of data is quite versatile.  

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6 data inadequacy problems scrutinized 

Data quality can be measured along such dimensions: 

  • Completeness. Is all related information available or something is missing? 
  • Accuracy and timeliness. Does data correctly represent the current real-world picture? 
  • Consistency. Do different occurrences of identical data conflict or agree with each other? 
  • Conformity. Are data values in sync with specified formats? 
  • Integrity. Are there essential linkages between data entries that reflect their relationships? 
  • Understandability. Can data values be easily comprehended and interpreted? 
  • Relevance. Is data important for the organization’s purposes? 

In case any of these dimensions is compromised, data becomes inadequate. Why does it happen? 

1. Manual data entry defects solved with EDW

Errare humanum est – and this is only too true when it goes about entering data. Employees responsible for this task may put it into the wrong field, skip an entry, make all kinds of typos and spelling mistakes, or just miss some symbols in the code or e-mail address. 

Sometimes, the quality of data suffers through communication failures between personnel. For instance, an administrator may forget or neglect to notify managers of the changes (s)he introduced to the system (like adding a new value or field), ruining the integrity of the database. 

Problems caused by the human factor increase exponentially when organizations allow clients to enter their personal information directly into the corporate system. In this way, unrecognizable words, nicknames, and unconventional acronyms appear in a company’s database. 

2. EDW can solve AI-engendered problems 

Machines also make mistakes. Such errors typically occur when companies digitalize slews of printed sources relying on Optical Character Recognition (OCR) software. Being extremely helpful when it is necessary to scan numerous lists of names and addresses within a short time to turn them into the digital format, this technology isn’t flawless. The AI can recognize numbers for letters and vice versa (zeroes and Os is the most typical case), proper names can be read as common words, lowercase and capital letters aren’t distinguished, etc. 

3. Electronic data warehouse can help you address the ambiguity of data 

Sometimes, the nature of data leaves the personnel unsure as to the way to interpret it. For instance, a phone number may contain more digits than it is typical for a country (like ten digits in the USA), which raises the question of whether these extra digits are just typos or the phone number is an international one. The same refers to unusual names or addresses. And if an employee has to enter huge amounts of data on short notice, they take the responsibility of correcting what they consider wrong at their discretion. 

4. EDW works good in solving issues with data duplication 

When two entries are almost identical, you can never be sure whether the same person was entered twice (and certain mistakes crept in) or these are two different persons (like family members) whose names and addresses are very similar. The time squeeze that most employees work under makes sorting out such issues problematic. 

5. Data warehousing is a good way to solve issues caused by incomplete information 

It may happen that some values for a data entry remain blank. For example, methods applied for dataset compilation were inadequate to determine zip codes so the requisite field isn’t filled out. 

6. EDW to address data conversion errors 

Changes happen not only in customers’ lives. Organizations undergo all kinds of transformations as well. Departments get fragmented into subdivisions or, on the contrary, unite, new software and hardware is installed or the old one is updated, databases migrate from in-house premises to the cloud. Global expansions accompanied by the clashes of languages, customs, and currencies as well as mergers and acquisitions contribute to the data conversion chaos, affecting the quality of data used by companies. 

Integration of several standalone databases into a single one or transition to new software may incur numerous discrepancy issues. Thus, you can encounter mismatched formats (6- vs 4-byte date fields), syntax (last, first, middle name vs first, middle, last name), or codes (male/female vs m/f). Another source of integration mess is the incongruity of interfaces utilized in different architectures that are hard to bring into accord. 

When an organization is divided into more or less independent branches, their databases start to diverge in definitions and values. Sooner or later, one department will use “customer” where another will have “supplier”, and “net sales” of one database will correspond to “gross profits” of another.  

While some of these cases must be treated as collateral damage or even inevitable evil, most bad quality data causes can be mitigated. Organizations adopt a modern approach leveraging an EDW – enterprise data warehouse, meaning to automate the majority of procedures related to data handling.  

What is EDW and how it helps monitor data quality? 

IT specialists define EDW as a virtual depot that structures, organizes, and stores data vital for the company’s successful functioning and development. This universal data storage allows to hoard the entire database of an organization in one place so that all authorized actors could access it at any time. Other enterprise data warehouse benefits include its utmost security, the simplicity of usage, the absence of necessity to manage several disparate storage facilities, and a range of data tracking opportunities. 

How to build EDW? Basically, you choose an EDW cloud provider and transfer your on-premises database, encompassing the whole gamut of files of different nature (texts, videos, images, tables) previously stored in the company’s systems (like ERP) as well as manual recordings. 

The practice of building EDWs that DICEUS has had shows that leveraging EDW software organizations can critically minimize poor quality data issues and improve your data hygiene. It is achieved through employing high-end data cleansing, verification, integration, and conversion tools that a good cloud-based EDW offers. For instance, an email verifier helps to remove invalid addresses from your repository and the best B2B database solutions are equipped with a verification algorithm that checks records before they get into the CRM

Another data quality control mechanism state-of-the-art EDWs provide is the ETL (Extract – Transform – Load) data integration model that enables manipulations with the information before it is stored in the system. 

With all the software tools EDWs provide, it is humans who should take the foremost care that data quality is up to scratch. 

Guidelines to control data quality using EDW

The quality-minded experts of DICEUS recommend introducing a foolproof step-by-step strategy that will ensure a high quality of an organization’s data.  

Be proactive. Don’t wait for mistakes to crop up to start giving thought to how to combat them. You should have a data quality program ready before there is anything to correct. The backbone structure of it must be the Assess-Plan-Implement-Evaluate-Adapt-Educate architecture ideal for this kind of task. Such a program must identify the scope of activity, problem zones, objectives, measures to be taken for data quality control implementation, and metrics to evaluate the progress and performance.  

Appoint personnel responsible for the job. The task should be entrusted to a data-quality team, consisting of a data steward and a network of representatives acting as single points of contact across the organization. In this way, you will facilitate downward and upward quality-related communication. The former is called to make sure all divisions are on the same page as to data quality control and the latter guarantees the steward knows what is going on in each department.  

Analyze current workflow and data architecture. The quality team should review business processes and systems related to collecting, storing, and utilizing data to realize the nature and range of their responsibility 

Audit data quality. The purpose of the procedure is to identify typical data defects and their patterns and develop recommendations as to fixing problems. This is the crucial stage in the entire sequence, consequently, it is the most laborious and time-consuming one. 

Clean the data. At this stage, data cleansing is implemented. The process should prioritize defect detection and elimination that happens as close to the source as possible. In this way, correction and repair cost the company less – both in terms of money and business reputation. To facilitate the procedure, the data quality team should be equipped with the most efficient data quality software. 

Revise business practices. Prevention is the focus of data quality support. The lion’s share of mistakes can be avoided if the company’s workflow is improved, using a case-driven approach. For instance, while loading EDW, many administrators resort to turning off referential integrity. Naturally, it enhances performance but at the cost of possible data defects appearance. Such malpractices can be easily identified and dealt with. 

Maintain data quality. Since data decay is a continuous process, monitoring data quality must never end. Data quality audits should be performed at regular intervals or after important changes to the data storage system have been introduced. Unless it is done, all the previous efforts aimed at providing data quality will eventually be wasted. 

Poor data quality afflicts the health of the organization and the efficiency of its operation. Companies who are aware of such deleterious impact introduce requisite strategies and high-profile software to radically reduce bad quality data and prevent its appearance down the road. The expertise of DICEUS in developing EDW projects allows us to offer you adequate solutions for data quality issues management, that will ensure your most valuable asset is on par with the level of tasks your company faces. 

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