5 Customer Data Integration Best Practices
When working towards effective customer data integration, it is essential that you have an end goal in mind and a series of planned steps to help you get there.
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For the last few years, you have heard the terms "data integration" and "data management" dozens of times. Your business may already invest in these practices, but are you benefitting from this data gathering?
Too often, companies hire specialists, collect data from many sources and analyze it for no clear purpose. And without a clear purpose, all your efforts are in vain. You can take in more customer information than all your competitors and still fail to make practical use of it.
Actual data integration solutions combine data from multiple sources into one location, such as a data warehouse. Integration occurs after collection and involves cleansing and transforming the data. Once you take these necessary steps, analytics tools can guide you to the concrete steps you need to take to achieve full functionality.
To make customer data integration (or CDI) pay off, you need to embrace the best practices — the steps that turn mountains of data into guides for practical and effective actions. Before taking a deep data dive, put the following five practices into place.
5 Practices to Put in Place
1. Consider Long-Term Goals
Your data integration efforts will fail if you only consider short-term benefits. You may get a quick ROI, but those figures won’t hold up in the future unless you choose a platform and process that is adaptable to the ever-changing needs of data integration.
You should use an approach that takes into account all these changes so that your investment stays valuable for years instead of weeks or months. Your data integration software needs the capacity to incorporate these changes so that your efforts do not slow down or become irrelevant.
2. Choose the Right Customer Data Platform
Business users now gather an almost endless amount of data about your customers. You can get information from their website visits, social media interactions, etc. But having this data does not mean you learn much unless you have the proper tools. Often, this data is stored in different places, isn’t compatible with your needs, or is flawed in some way.
A customer data platform lets you collect different types of data from many channels, devices, and platforms and place it in one location. This method makes the information much more accessible. These platforms sort and categorize information. They also scrub the data that is incomplete, wrongly formatted, or repeated so that you get a “clean" record. That means everyone in your organization gets the same data when they access the record. These business processes let you optimize your operations.
Note: A data platform is not the same as customer relationship management (CRM) software. Forbes describes data platforms as CRMs on steroids since they don’t merely track the customer but allow you to engage them in new and more meaningful ways. Some of the best-known data platforms are Microsoft, Oracle, Amazon S3, and Salesforce.
3. State a Clear and Reasonable Data Integration Goal
Digging deeper into the data can be satisfying and informative. However, it wastes time if these projects have no hope of enhancing your company’s business goals or product strategies. You will flounder if you do not set a clear and reasonable goal for your data consolidation, integration, and analysis.
You are not investing time and effort to gather knowledge for knowledge’s sake. You may note fascinating patterns and intriguing numbers, but you cannot afford to be mesmerized by these factors. So set reasonable goals such as centralizing your data, improving data security, or creating more remote work positions. Then refuse to wander off your set path.
4. Create a Strategic Data Management Plan
Data integration is ineffective without a clear plan that spells out what you hope to accomplish and how you will measure the results.
The best way to improve is to establish ongoing data management and analysis processes in your company. Doing so will ensure that no data project is given the go-ahead without a goal and a method of reaching that goal. Just as science experiments need a hypothesis to guide them, so too does your customer data integration work.
All departments need to be involved in planning, and should avoid departmental “silos.” When you limit planning to individual departments, you risk losing a 360-degree view of your data integration projects. The marketing department is likely to collect a mass of customer data that only reveals part of the story. Your research department may do the same. All departments need to work together to paint the complete picture of your customers and their needs.
Your company must know exactly what data you are tracking and each department's goal and methods. If you do not have this understanding, you risk duplicating collection and management efforts and wasting time and money.
5. Create an Efficient Way to Integrate Big Data Sources
Effectively integrating your data sources means embracing a reliable data integration strategy. You will need to integrate data from various application environments and transfer data from a source to your target. ETL (extract, transform and load) technologies have been especially effective for traditional data warehouse environments. They are now evolving to meet the needs of more recent data management environments.
You will need data integration tools that can handle batch integration processes with multi-source real-time integration and federation. In that way, you can blend data you store in a Master Data Management (MDM) system with big data sources that inform your goal. Basically, you can integrate your stored data and legacy systems, even those in different formats, with new sources to get a better picture of your customers.
There you have it, the five best practices to make customer data integration (or CDI) worth your while.
If you follow each step above during your next big data-gathering effort, the terms "data integration" and "data management" won't be just terms. Instead, you'll begin to reap the rewards that come from mining the mounds of data effectively.
Published at DZone with permission of Abe Dearmer. See the original article here.
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