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March 5, 2010

Database Integration: On the Importance of Data Quality Standards

It’s a sad fact, but many organizations realize the poor quality of the data in their databases, only when it comes to database integration. Data quality issues are among the common reasons for data integration failure.

This neglecting attitude to data quality lies in the fact that companies often don’t understand how much data quality impacts business processes. Thus, each data source or database a company uses may have its own rules and standards for data quality. The issues, however, evolve as soon as the database integration started in order to get a unified look at, for example, company’s customers’ data.

Those issues may come out of the difference of data fields, for example, or data formats, so the same contact may be represented differently in different databases. Thus when it comes to database integration, it can’t be performed correctly due to those differences, which may lead to data duplication, and many more data quality issues. In fact, in the result of integrating several databases of poor quality, a company gets one big database of poor quality. This means that database integration was in vain, as it failed to achieve its main goal of providing the company with a general view of business data, while the integration expenses were significant.

Unfortunately, data quality technology does not always allow organizations to fix poor data. So, it’s much wiser to implement company-wide standards for data quality to prevent the appearance of data quality issues associated with integration of data from heterogeneous sources, then to perform data cleansing and other data quality procedures afterward.

January 15, 2010

Customer Data Integration Among the Top Priorities for Companies

Filed under: Data Integration, Data Quality — Tags: , — Olga Belokurskaya @ 8:35 am

According to one of the latest researches by Aberdeen Group, issues with customer data may greatly affect organizations’ sales and marketing efforts. Besides, lack of trusted customer data and inability to target customers through customer data proved to be the top two of those issues, according to the respondents’ opinion.

This reveals the fact, that there’s still a lack of customer data integration and data quality at many organizations.

This seems strange to me, especially taking into account that experts have been talking a lot about the importance of customer data integration across the enterprise to give all the responsible a better view on who are their customers, what do they need and buy.

Even in a rather small company there could be several sources containing customer data, and not obviously those sources contain similar records, so customer data integration is the means to piece together the data puzzle.  Probably, mergers the number of which was significant in 2009 prevented numerous organizations from fulfilling their customer data integration initiatives.

Well, the fact is that more than 60 percent of Aberdeen’s survey respondents named customer data integration and other customer data initiatives among their top priorities for 2010.

December 28, 2009

Customer Data Integration to Be Among Companies’ Concerns in 2010

Filed under: Data Integration — Tags: , — Olga Belokurskaya @ 1:53 am

Well, let’s have some words on customer data integration (CDI). CDI has been one of the main concerns for different organization. Making and keeping customer data clean and convenient to work with, and getting more value from CRM systems has been in the top five of data integration challenges.

As it was mentioned in one of researches by Forrester, many organizations have changed their view on customer data integration and management (and, generally, on MDM), and started looking at it as a multiyear investment, including several phases. Though, again according to Forrester, the right approach to customer data management, and customer data integration as its significant part, still keeps being elusive.

The problem is that many enterprise CRM applications often give quite fragmented view on enterprise’s customer data, due to poor customer data integration. One more issue, the quality of customer data is often poor, so this is one of the reasons of failed customer data integration.

So, as predicts Forrester, the question of customer data integration is not solved yet, and there is a lot to be done in 2010.

December 3, 2009

Choosing ETL That Fits Your Business Requirements

Filed under: Data Integration, ETL — Tags: , , — Olga Belokurskaya @ 3:07 am

In my previous posting, I touched upon the importance of defining business requirements before starting data integration initiative. Without those requirements, the process will comprise just blind gathering all kinds of data available at the enterprise with no clear purpose. In other words, data integration initiative may turn into some kind of monkey business. Okay, that’s clear.

Well, data integration tools selection, including ETL (extract, transform, and load) solutions, is a job that requires efforts but if done right, it’s worth it. What do I mean by this “done right”? The message is simple. When choosing an ETL tool, a company should bear in mind business requirements for data, and make their choice based on whether an ETL solution possesses functionalities that meet those requirements, or whether a vendor may add the needed functionality to their solution.

Look. You’ve defined your data integration strategy, business users have created the list of requirements for the data they would need to work with. So now it is clear what data should gather the future ETL tool, and what operations it should perform over that data. Now, having all the necessary criteria, you won’t be wandering blindly among multiple vendors, but will concentrate on those whose ETL solutions meet your criteria.

December 2, 2009

Data Integration: Business Needs are Important

Filed under: Data Integration, Data Quality — Tags: , , — Olga Belokurskaya @ 8:18 pm

Have you ever thought that one of the serious issues that makes data integration initiative complex is the lack of well -defined user needs for data? No, let’s start differently. Why do companies need data integration and spent so much efforts and resources on this initiative? To get the full view and better understanding of company’s data. And this information, in its turn, is needed for business decision makers to make right decisions.

Back again to user needs, or better say, requirements for data. This may come as a surprise, but the «lack of well defined user needs” has been named on the third place among the reasons for the failed data integration initiative to deliver business important data to decision makers, according to a survey by Aberdeen Group.

So, why user requirements are so important for successful data integration? In fact, the goal of data integration is not simply to gather all the data from systems and applications used within a company in a single place, but to get the data that is important for business. They‘re business representatives who are the end users of data integration, because they make the decisions based on the data received. So to ensure the process of data integration was correct, specific business focus should be placed on data standards and requirements.

These business requirements should be taken into consideration and thoroughly defined before data integration is started. In other words, there should be clear definition of what data is critical for business.

June 30, 2009

Customer Data Integration vs Data Warehouse: the Difference

Filed under: Data Integration, Data Warehousing — Tags: , — Olga Belokurskaya @ 11:51 pm

The extensive world of enterprise data is quite tricky; however, it’s very important to clearly distinguish all the solutions used when dealing with data.

As customers are very important for any company, customer data is among the greatest company’s assets. In fact, customer data integration (CDI) solutions are to deliver the fullest information about customers.

However, as CDI is a software solution, and, in fact, is a clearinghouse for data synchronization and deployment, it is inevitably compared to data warehouses. There are several reasons of this confusion:

  • the aim of both solutions is to accommodate clean, meaningful information to the enterprise
  • both solutions are undoubtedly beneficial for business
  • both demand a solid partnership of business and IT

This confusion is risky, for these solutions differ in term of their positioning as well as in term of their usage. According to Jill Dyché one of the most acknowledged BI experts:

Data warehouses are designed and built to support business intelligence, and are meant for use by business people. Best practice data warehouses are those that have been planned around a set of business requirements that inform a series of applications—we call this the BI Portfolio—that are deployed incrementally to the business over time.

CDI, however, is purpose-built for operational data integration. The CDI hub is the ultimate home of customer master data that has been matched, reconciled, and certified, and is available to a series of business applications and systems (not end users). Unlike the data warehouse, which usually stores historical detail, summarized data, and time-variant information, the CDI hub stores or points to certified master data about a customer.

March 30, 2009

Some More Words About Customer Data Quality

Filed under: Data Integration, Data Quality — Tags: — Olga Belokurskaya @ 4:05 am

According to Ted Friedman, an analyst from Gartner, though many organizations have at last acknowledged the importance of customer data quality, only few of them have actually implemented data quality tools or any data quality initiatives throughout the enterprise. Moreover, even those organizations that actually use the tools still do not use them enterprise-wide.

Among the reasons some analysts name the fact that that most companies collect and store customer data in numerous data sources spread throughout the organization with no way to connect them. Organizations fail to apply data quality tools enterprise-wide by reason of lacking a single view of the customer through a master data management (MDM) system or customer data integration (CDI) initiative. Some may have up to 10 different customer databases with no single schema for collecting customer data.

As an effect, poor or siloed data lead to missed cross-sell and up-sell opportunities and can even deter potential customers who usually expect personalized interactions.

It’s high time for the companies to start thinking broadly about data quality in the context of enterprise information management and MDM/ CDI initiative.

Experts advise companies with simple customer data quality needs to start with tools embedded in their existing applications.  However most organizations will need to invest in more specialized tools for sophisticated tasks like data parsing and standardization.

Though the adoption of deploying customer data quality tools enterprise-wide is still lagging, the experts predict it will shortly gain momentum as more and more companies recognize the advantage it can provide over competitors.

March 26, 2009

Avoiding CDI Implementation Challenges

Filed under: Data Integration, Data Quality — Tags: — Olga Belokurskaya @ 6:12 am

The administration of data within an enterprise or an organization is not an isolated process. Data is not usually confined to an application or an individual within a department; it flows across systems, often multiple times. Mismanaged data is a source of constant headache for an organization. So it’s utterly important that data is been treated as a corporate asset. And it’s especially true when speaking of customer data.

When someone’s planning for implementation of customer data integration system, what are the common CDI problems he could face during deployment?

I’ve found some advice from Jill Dyché, internationally recognized author, speaker, and business consultant, on how to avoid common challenges one can face during customer data integration system implementations

  1. There are always people in a company, who refuse to understand the purpose of CDI implementation if there is already “a kind of thing that’s doing almost the same”. Anticipate such arguments, prepare to explain the key moments and educate your opponents offering them deliberate examples. Position your CDI effort as an ongoing program that can enable different business needs.
  2. Premature involvement of vendors may lead to waste of time and money for you won’t be able to give the vendors what they need to deliver the tools. Data management requires intent focus on functional requirements. So until you have your thoroughly elaborated list of requirements, keep vendors aside.
  3. Many IT environments are accustomed to buying off-the-shelf applications and they simply don’t have enough development skills to configure and maintain an MDM solution. Underestimating the need of required development skills will bring no good. For if your business is complex, data management solutions will be also complex
  4. Sometimes you just don’t understand where to start. Do not disregard asking professionals from a good consulting company which may explain all pros and cons, help define the right product from the wide range vendors offer, it also may  recommend tactics for moving forward.