As numerous organizations and institutions around the world are investing in collecting data from various sources to meet business objectives, data has become the modern day gold. Data collection today is made easier by the diversity of tools available on the market, making data collection rapid and well-handled. The issue, though, is when data is manipulated to be instantly ready for use, and adaptable to work with different data sources into the same use case.
Here, teams are confronted mainly with data interoperability issues, which means teams have to spend some time to homogenize their data : sharing and applying the same naming conventions for customers, products and partners, and gathering the same information written into the same format for all data sources.
And it is a painful process that teams must perform. All this is called data normalization !
This process preps business data to be ready, accurate, and well-organized for reports, machine learning, analysis, prediction and any post processing.
To have a unified source for your use case, you begin first by collecting the data you need. It can be your internal data : customers, partners or whatever source of data created within your organization.
You can also easily collect today’s external data made available through API like Facebook or Google Analytics data.
In some cases, you have to pre-clean this data to make it easier to manipulate, like deleting irrelevant rows or columns etc.
Once you have this data collected, you need to standardize it to make it homogeneous. This step is usually done manually, like rewriting names, entities, or descriptions in the clear format etc. Usually, you detect visually words written in different manners, and replace them with the standard you have chosen. Most of the time, people use regex expressions to detect variations and correct them, but they spend significant time spotting already all the words having variations.
Once data is homogenized, it is time usually to start creating aggregations or categories to make your data ready-to-use. It is about extracting information from your data and putting it into specific categories that you setup regarding the use case. It can be categories of products or suppliers, or aggregating lines by groups etc.
And this step also is most of the time done manually, line by line, extracting the data from the different columns and pasting it on categories columns.
This step is optional and needed if the data collected is insufficient to do the use case, so you need to gather information from other data sources or from experts. It is a common data quality issue. There are some options available, like scraping data from online websites or purchasing databases. But to match this new data with yours, you have to redo the other 3 steps to make sure this new data matches your current one.
Once those steps are done, now you can do the use case : calculating KPIs, compelling analysis, generating a report, etc.
Actually, everyone who is involved in a data process or a use case involving data. The world “data worker” covers several jobs: data analyst, category manager, account manager, data scientist, product manager and acquisition manager … Every job where we manipulate or use data needs data normalization. And when done manually, it is extremely time-consuming.
For example, category managers or product managers who work for marketplaces have hundreds or thousands of products to promote online every day.
High-quality product catalogues are great challenges, because with clear and structured description and presentation, a product can become better referenced and thus drive more conversion. However, information on products is gathered most of the time from vendors, and it can be heterogeneous and not match marketplace standards. For instance, the color can be labeled as “blue” for an iPhone, while the exact color is “blue cobalt”.
Otherwise, account managers do many data normalization on the names of customer entities, addresses and information to have a structured unified customer database.
However, data scientists perform normalization on data before running their algorithms to have accurate and clean data sets.
To overcome this problem of heterogeneous data sources, organizations tend to launch expensive and painful IT transformation projects aiming to change data from the source. Unfortunately, those heavy projects can take long months and a huge budget to obtain normalized and proper data sources.
Our vision at yzr is to enable effortless data sharing. For this goal, we wanted to offer a flexible and rapid methodology accessed through our platform to enable teams with rapid solutions to normalize their data without passing through IT projects. We want to empower business and data teams with the right tools and intelligence to fasten their use cases and enable them to rapidly share clean and standardized data effortlessly.
So if you have issues with normalization, standardization, data labeling and you (or your teams) are struggling with heterogeneous data, do not hesitate to write to us. We will be pleased to discuss this matter with you and give you a sneak peek on our solution.