In today’s e-commerce ecosystem, work involving customer analytics and data science are in high demand. Data integration & Analysis became Essential to any business around the world.
In 2012, Harvard Business Review rated Data Scientist as the sexiest job of the 21st century.
While organizations invest in advanced analytics tools and the talent to operate these tools, the one component that makes or breaks the effectiveness of any organization’s analytical efforts is data integration. Data integration (DI) is a set of techniques to pull in data from different sources, merge them into one holistic view, and preserve that integration in the future.
There are a number of approaches to doing this effectively, but the most popular approaches include:
- Extract, transform, and load (ETL): data is extracted from different sources, transformed into a unified, usable format for analysis, and loaded into a database, such as a data warehouse;
- Data federation: where vast amounts of data make unification via ETL costly and inefficient, databases can be federated, allowing them to run independently but still have their data accessed holistically through federation software;
- Database replication: to ensure that data is preserved and protected, the central database (master) can be designed to push out updates to copies (slaves) when its own contents are copied;
- Data synchronization: to harmonize data between a variety of instances, such as files accessed remotely via cloud services; and
- Changed data capture: to provide meaningful metrics on how the data in the database has transformed, such as timestamps on changes and identifying the entity behind the change (for example, a particular, authorized user)
Don’t get lost working with data. Professional data integration avoids headaches!
All these techniques require support for a wide range of interfaces, to allow the resulting DI solution access to databases, applications, and files to extract or load data. Solutions based on these techniques may be crafted in-house by the DI provider, based on a third-party’s tool, or a mixture of both.
Why is data integration so important?
Without the ability to integrate data across various customer touchpoints (for example, sales information, number of phone call inquiries made about the product), analytics yields little value, while wasting countless time and effort. This makes attribution modelling extremely difficult; if you can’t look at your data and easily understand how your consumers interact with your business and your marketing efforts, then it’s a huge hassle to determine when and how your customers became your customers.
Avoid the confusion, and gain a sense of how your customers really connect with you.
If your business is sitting on a lot of data, chances are it’s in dire need of clean-up and organization. In fact, the majority of time data analysts and data scientists spend in their workday cleaning and organizing data. A 2017 survey of about 200 data scientists conducted by data company CrowdFlower revealed that data scientists spend 51% of their time cleaning, organizing, and labeling data.
Why do data scientists spend so much time doing this?
- The existing data platforms they work with do not allow for meaningful data extraction, nor connection to other platforms;
- Incompatible formatting of data, which render joining one database to another an extremely time-consuming, if not impossible, effort; and
- Inaccuracies in the data that go unnoticed.
Cleaning and sorting data is among the least favorite activities of data scientists. Why not get them focused on the more powerful data integration transformations – what they’re really good at – by following these simple steps:
When crafting a new database, don’t dump everything in it at once! Even one gigabyte of information can be overwhelming to sort at the start. Begin with a small sample of the dataset, just enough for developing a system and debugging it. Start with just one file, or even one WHERE clause for a relational database. Using too much data at this point only lengthens development time. Then, once you have confirmed that your dataflow works correctly, you can develop your data integration solution gradually, part by part, adding the rest of the dataset a little bit at a time. Keep in mind we are working with a small sample of the data, so check the output after each intersection and make sure that the results are correct.
Create multi-departmental checks and balances.
Ensuring seamless data integration requires the contribution of multiple teams, not just from your data scientists leading development. Stakeholders from various departments should be encouraged to test the data frequently and share with the development team any inefficiencies and inaccuracies.
Organizations that rely on data from external sources need to ensure compatibility and accuracy with their internal data warehouse. Indeed, a master data governance system needs to be created where all newly-developed data must have their accuracy and compatibility validated against the master database before being integrated with other systems.
An organization’s data integration hub will need to evolve to meet the new corporate goals. This can only happen if the data development team maintains open communication with other teams and business stakeholders, to plan for changes in corporate goals well ahead of their implementation. This may mean changing the format in which data is tracked and recorded, investing in newer tools to provide the data needed, or adding new categories of information to track.
Retain a history of changes.
Many old, legacy database systems do not record a timestamp for changes made. As a result, erroneous assumptions about dataflow can be made. Systems must timestamp every change made in each system.
Newer systems do record the dates and times of changes and the content of the change itself. This can assist in effective data delivery. There is increasing legal pressure in jurisdictions around the world to maintain a history of all transactions, yet in the interests of efficiency a day-to-day operational system may not be the best place to store these. Instead, an effective data integration system should incorporate data archiving solutions to preserve transactional history.
Invest in your own data integration platform.
To thrive in today’s data-rich environment, it is worth considering outsourcing data integration. Getting outside expertise is particularly helpful if your organization moves quickly and has an opportunity to avoid the common pitfalls noted above. With Windsor.ai’s data integration tool, you can onboard data from any marketing, e-commerce or CRM source, build segments and act on them in real time, with windsor.ai’s technology that consist of an attribution model which follows the customer journey from first to last touch point, and can help improve your efforts and optimize your marketing spend. Contact us for a demo of what our data integration services can do for your organization.
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