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.

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.

Choosing CRM software is an important step for many companies. The variety of options as well as variable pricing makes the decision-making difficult, especially in today’s economy. The uncertain economic situation made many companies delay or even abandon their implementation of a CRM software platform which could provide a benefit tomorrow for the sake of survival today. As companies tighten their budgets, the need for an inexpensive CRM software solution becomes even more important.

Open source CRM can be the answer to many companies’ needs in today’s trying economy. A question arises then, whether open source CRM software a viable and effective alternative to proprietary solutions?

A lot of the fundamentals about implementing CRM technology hold true regardless of whether you are implementing open source CRM or conventional commercial CRM software. However there are some areas asking for more attention when installing open source CRM. Let’s have a look at some open source CRM pros and cons.

It goes without saying that implementing open source CRM requires minimal investments because of many free options available. In other words, for little or no money you get functionality and features that allow companies to improve upon inefficient operations.

However, the range of functions open source CRM may provide is not as extensive as the one available through commercial packages, and some of the features may simply be not included. Moreover limited support is available to open source CRM software which can be a problem for some companies.

That means that a company should be careful defining its needs and choose open source CRM software that meets those needs. But, if a company can adjust to these limitations, the return for its business can be significant.

Implementing data quality management programs is an important step for any organization. Due to the size and complexity of an organization data quality management can be quite complicated and, sometimes, may turn (and very often it does) into a nightmare. There are three most common mistakes that companies make when starting data quality management project:

1. Hope for a “magic tool” – meaning that too often organizations believe that a packaged solution can “fix” noncompliant data though in the first place they should care about eliminating the introduction of bad data. Although data quality tools are critical components of a data quality program, one must first question the motivation for purchasing a tool, then the process itself, and consider the improvement potential in terms of contributing to the effectiveness of the program.
2. Lack of the right expertise – which means that the success in developing a data quality management program depends on having both business and technical expertise but, in fact, lots of organizations disregard this crucial moment, being sure that as soon as a data quality program is initiated within an organization, there should be some visible improvement to the data.  This is definitely wrong. Moreover, the data quality manager is often viewed as having responsibility for some data quality improvement action without necessarily having either the knowledge or authority to make it happen, and the team has no idea where to begin. This is the result of not bringing in the proper expertise to help get the program off the ground.
3. Not accounting for organizational culture changes – no technology in the world will eliminate data quality problems, without understanding of how people’s behavior allows the introduction of information flaws in the first place. Without the cooperation of upstream systems owners, data warehousing managers are often helpless to control the quality of incoming data. Stricter data quality needs at the data warehouse demand resource allocation by upstream managers.

Here are the solutions on how to avoid data quality management mistakes, provided by David Loshin, an expert in information management:

Exploit the advisory role of data quality teams and use internal procedures to attach responsibility and accountability for data quality improvement to the existing information management authority.

Don’t forget training in the use of policies and procedures — especially in the use of acquired tools.

Hire professionals with experience in managing data quality projects and programs from the start. These individuals will be able to identify opportunities for tactical successes that together contribute to the strategic success of the program.

Engage external experts to help jump-start the improvement process. This will reassure your team that your problems are not unique and will allow you to learn from others’ best practices.

According to Dion Hinchcliffe, the year 2009 will be a rebuilding period for most organizations, however there will be not many fast growth businesses in the major categories. Still there will be opportunities for new products in industry sectors and classes of data haven’t seen wide penetration online yet. The downfall of several online industries left s a large vacuum that must be filled by something.

Here is a short overview of his predictions.

The adoption of low-cost Web 2.0 and cloud/SaaS solutions, will  help the smaller Enterprise Web 2.0 companies to get away from large vendors.

2.0 arrives to business. Online community and 2.0 technologies are becoming mainstream and many organizations could make them their priority. Finally, CRM and customer service become connected with online communities of users.

Cloud computing industry as a whole will be growing and flourishing as organizations will be seeking to cut costs and shorten time to market.

Internal penetration of social networking and Enterprise 2.0 in organizations will continue in 2009 but the weak story continues to be the successful creation of online 2.0 products for the broader marketplace.

Hinchcliffe also predicts mobile platforms and devices becoming highly strategic in 2009, the survival of SOA, however not in its present form but transformed. He expects mashup technologies to be front and center with this transformation. And he foresees that massive changes in the business landscape will create new 2.0 business opportunities.

In my previous post i reviewd some points that could help to increase the quality of your CRM database. Now I’d like to touch upon quick tips on what to do if you plan your CRM data migration.

Data migration from one CRM system to another can be quite irksome. Although adding new records to the new configured and ready to use CMR is pretty plain, it’s quite troublesome to shift your previous data into new CRM. Often before the data in your existing format is ready to upload into the new system, it requires a big amount of formatting, enrichment and cleansing. It’s “an inevitable evil” that comes with the migration process.

What you should keep in mind while migrating your CRM data:

Make sure you have an exact back up of all your previous data and the new CRM so that you could roll back to where you were if anything goes wrong.
Check which additional data fields are compulsory in the new CRM and identify them with the fields you have in your current CRM.

Add any additional data items that are missing, remove those that are not required and make sure you have complete records which are ready to be migrated to the new CRM.

As soon as the data moved to the new system, categorize and label it. Do it systematically to avoid the mess and have the retrieval easier.

The whole process, of course, requires a lot of effort and quite dull work, but if it done well, it will be worth every spent minute.

What is one of any organizations most valuable assets? CRM data is probably one of them. Companies are likely to protect and secure their CRM data, but what about its quality? Data quality management is very often one of the most neglected areas of CRM management and one of the major pain areas for administrators and managers.

So trying to learn more about the problem, I came across some practices that could help maintain and enhance the value of the data.

• Do not ignore bad data until it starts affecting your work. Keep an eye on your data and monitor any changes in its quality.
• It’s a good practice to manage, normalize, format, qualify and filter out your leads outside your CRM and then have it uploaded so that what is not valuable or quality data does not get added.
• Periodical data append is important although it involves a lot of manual effort and may seem time consuming.
• Duplication is possibly one of the most common problems and creates redundancy as well as inaccurate reports. So it has to be kept in check
• The same thing may be said about expired data that simply junks your CRM. The more regularly you check for expired data, the healthier your CRM is.
• And, finally, if data cleansing is what helps you maintain your database quality then data enrichment is what will help you enhance your data quality and make it more valuable to the end users.

And here we go with the conclusion: good data management practices, constant cleansing and enrichment process – that’s when your CRM data really becomes an asset.

According to a recent Gartner report, many companies are throwing away tens of thousands of dollars on data integration tools or services that do not match their organizational needs. Though they could save up to $100,000 if they learn to be savvy and accurate while negotiating or renegotiating data integration software and services licenses.

It appears to be that many companies don’t understand the terms of their data integration licenses, having never taken the time to study them in detail. And they seem not to clearly understand what their needs are and what particular software and services they could use.

Gartner insists that learning and understanding the terms of their data integration licenses is a must for wise companies. It also recommends to negotiate with multiple vendors which is essential to realizing maximum cost savings. Companies should keep negotiations open with multiple vendors until a contract with one of them is finalized - something too many companies fail to do. And, finally, companies must know their needs and and craft licenses to include only those tools and services that they will actually use. This step becomes increasingly important as proprietary data integration vendors continue to push expensive, comprehensive software suites.

I’ve recently come accross some interesting figures.

According to Gartner, over 60% of organizations state they have a BI strategy, but despite the many years experience most organizations have with BI, they aren’t doing better in addressing the fundamental challenges of BI.

Gartner estimates that no more than 20% of business users actually use BI proactively. This means that BI is not being widely used to manage performance. And advises that IT must look for means to align BI initiatives with corporate and business objectives. Saying about BI strategy it underlines that it doesn’t mean just choosing which megavendors you will work with. You must keep in mind that the speed of technology change is increasing. There are lots of thechnologies concidered as trends, which have the potential to disrupt the BI marketspace significantly. They are Web 2.0, interactive visualization, in-memory analytics, SaaS, and BI integrated search.

All these topics were discussed at the recent Gartner Business Intelligence Summit 2009 during March 9-11, by the way.