Matt Assay recently posted on how the concepts of open source and SaaS have been blurring into one. He notes that the confusion might be in part because both open source and SaaS have increasingly turned to “freemium” models. No wonder, clients are looking into lowering their spendings. SaaS and open source both offer that decrease - either on licensing, hardware, or human resources.

Speaking of open source, the benefits don’t stop with the costs. There is always a community of developers ready to help and enhance the program’s code any time. There’s a multi-platform support and a higher level of flexibility, as clients don’t depend on a single vendor anymore. There’s easy deployment and a high level of customization. With all the benefits open source has to offer, no wonder emerging SaaS vendors choose to go open source.

It’s no news that companies of all sizes today strive to deliver their products and services faster and to manage more complicated marketing programs, while the budget for that only decreases. In terms of that aspiration, having the right data integration and data quality model proves critical.

My collegues at Apatar have recently released a white paper aiming to help organizations reduce ETL (Extract, Transform, and Load) and data integration costs by 80%. The white paper sets out a sound approach to data integration and ETL projects that can save companies a considerable amount of time, money, and efforts.

They discuss the Total Cost of Ownership of integration projects, break down the cost structure, and determine ways to cut each cost down. The paper also includes best practices and examples of how major companies saved on open source and data integration.

Don’t waste time and resources on creating what’s already there.
Extracting and normalizing customer data from multiple sources is the biggest challenge with client data management, according to the Aberdeen Group. OK, true, a lot of companies consider linking and migrating customer information between databases, files and applications a sticky, if not risky, process to deal with. Gartner says corporate developers spend approximately 65 percent of their effort building bridges between applications. That much! No wonder they risk losing lots and lots of data, not even mentioning the time and efforts this may involve. Why spend time on creating what’s already there?

Do
Find an integration provider that suits you. There are plenty of vendors. Of course, there isn’t a universal integrator that would suit everyone, as each tries to cover a a certain area and solve a particular problem. So, you just need to spend a bit of time looking for the right vendor.

Don’t let expenses frighten you.
In today’s enterprises, most data integration projects never get built. Why? Because most companies consider the ROI (Return on Investment) on the small projects simply too low to justify bringing in expensive middleware. Yeah, so you have your customer data in two sources and want to integrate (or synchronize). But then you think “Hey, it costs too much, I might as well leave everything as it is. It worked up till now, it’ll work just as well in the future.” Then after a while you find yourself lost between the systems, the data they contain, trying to figure which information is more up-to-date and accurate? Guess what? You’re losing again.

Do
If ROI is an issue, consider open source software. With open source data integration tools you could have your pie and eat it too. Open source can offer a cost-effective visual data integration solutions to the companies that previously found proprietary data integration, ETL, and EAI tools expensive and complicated.

Not having to pay license fees for BI and data integration software should make companies previously scared of insufficient ROI return to the data integration market.

Moving contacts from one CRM to another? PowerObjects Microsoft CRM blog has tips on how this process should be planned and handled so as not to lose any data somewhere in the middle.

The blog suggests that you first take a look at your current system and determine whether it’s really worth moving your data to a new system. If after thorough consideration you still think of migrating, move to the next step. That is, define the relationships and determine exactly what information needs to be migrated. After getting a hang of what needs to be done and identifying the efforts and costs associated with migration, you are ready to reherse your migration.

I’d also add that, as usual when dealing with customer data, migration from one CRM to another is a great chance to improve your data quality by checking its accuracy and consistency before sending it to your new system.

Data quality problems cost U.S. businesses more than $600 billion a year, according to The Data Warehousing Institute. (14 Dec 2007)

How does that happen? How come companies are losing this much money and not even realizing there’s a way to save up?

Again, the major problem usually lies in the source the data is received from and the way it is processed (if processed at all) before it enters the database/warehouse. Then there’s also the currency of data, its accuracy, completeness, relevance and consistency.

When dealing with leads, a few of these problems can be avoided by using Online Lead Generation (OLG) techniques instead of buying from a big data provider with huge outdated databases. A recent Christopher Petix’s article titled Marketers Should Avoid ‘Dirty Data’ discusses the major benefits of OLG as opposed to buying from data providers. Here are a few I thought really make OLG technique stand out:

- collecting new data for every campaign
- ability to set parameters for data collection
- consumers fill out information related to their demographics AND specify their interest in a specific product or service
- strict data cleansing processes, double-filtering

All of the above ensure a hugher degree of relevance, and, as a result, really help you save the money you’d waste otherwise on reaching out irrelevant leads.

So called “dirty data” not only wastes contact center agents’ time, it also wastes a marketer’s budget–and optimizing budgets is crucial in the current market conditions.

That’s where you realize that data quality could be taken care of in advance, but you’re stuck with your dirty data and need to deal with it (and consequently spend more).

Data quality has the ability to save marketers a considerable amount of money and is a completely transparent process.

What could be simpler?

With the speed on-demand solutions appear and gain popularity and compete with the old-school desktop systems, you’d naturally think customer data integration solutions have long become a usual thing. That’s why it’s a bit shocking to see the recent Forrester Research report only 2% of companies surveyed manage to achieve an integrated view of a customer data, while 92% claim this approach to be either “critical” or “very important”.

Turns out, building bridges between Web and desktop is still a problem, and we’re only left guessing whether it’s the loss of data, resources, or time that the companies are scared of.

But then there are enough pre-built solutions out there that won’t take months or weeks of your time, and that you can use and be sure that nothing gets lost. Everything is simple, couldn’t get more simple, in fact. Then why make it sound so compicated and scarry?

Just know what you need out of your CDI solution, and find one that would fit your requirements. Also, it helps to actually know what customer data integration is and enlightening your staff as to why the company need it.  In these terms, Jill Dyché’s seven tips to get companies ready for successful customer data integration implementation might be useful.

I keep wondering how come data quality check still exists as a procedure performed once in a while, rather than as a part of the front-end process? How come most companies start worrying about the quality of your data only when it’s already dirty and in use? How come it doesn’t occur to them that the quality of data needs to be thought through before it’s actually captured? Even at the early stages of data capturing, data quality aleady plays an important role in the future of the company. It is the early stages that make a difference in how your data turns out and if it will pay off later on.

A recent Forrester paper titled It’s Time To Invest In Upstream Data Quality suggests that when companies realize short-term data cleanup ROI immediately, it’s hard to justify front-end investments that may take years.

At the same time, Forrester says, IT budget planning committees tend to avoid the existing data quality (DQ) products that allow integrating downstream data hygiene rules into front-end processes, justifying this by solutions’ cost and complexity.

The result? I&KM (Information and Knowledge Management) pros quickly reach diminishing return on data quality investments, requiring even more investments later on to catch up with missed opportunities like verifying customer contact information, standardizing product data, and eliminating duplicate records.

Read the paper to find out how to break this cycle and identify the optimal DQ solution downstream and audit source systems that cause the most significant data issues upstream.