Standardizing Consumer Data in Four Phases For Greater Insights

Standardization is the process by which a data management platform (DMP) sorts raw, irregular data and transforms it into a constant, predictable, and unified format. “Collaborative research, large-scale analytics, and sharing of advanced tools and methodology” are all made possible by this standard format (OHDSI). It is simpler, quicker, and more economical to monitor consumer behaviour, identify patterns, target, nurture, and convert with more reliable data. It also supports the provision of more individualised consumer experiences. In truth, just 15% of customers expect businesses to genuinely deliver on the individualised experiences they say are vital to them (68%) (Oracle). These four actions can be taken by marketers to standardise data.

Step 1: Conduct a data source audit

Start by identifying all the data sources in your company. A data source is a point of data supply from which data enters your database. The sales and marketing teams construct, mine, and maintain numerous sources at most businesses, pulling a variety of data types to move prospects through the sales funnel. Finding as many data stakeholders as you can is important since other teams can also own the data sources. Inform your coworkers of the purposes and advantages of data standards.

Standards are only useful if they take into consideration all data realities, therefore do everything you can to become familiar with your company’s data sources. Understanding each type of data source, how frequently each source delivers data, which teams own each source, which teams use each source (or want to use it), and whether the data source is first-, second-, or third-party are all necessary for building legitimate, enforceable standards (step 2). Remember that standardising or making other changes to data from a third-party source might not be possible.

Step 2: Establish standards for data formats

It’s challenging to offer general guidance because each company has different needs, objectives, and funding sources. To data-driven marketers, we should emphasise one thing: attempt to create a balance between your demand for exact standards and the inevitable tangle of “big data.”

The three Vs of big data are volume, velocity, and variety. In other words, it is plentiful, arrives quickly, and comes in a range of forms or sorts. Data standards should be precise and comprehensive so that you don’t leave any data in your business systems behind; they should be able to withstand a deluge of rapidly changing, inconsistent information. Additionally, standards must be futuristic. Big data may not yet exist for a startup.

Step 4: Standardize the format of external data sources

Now is the time to standardise. Start by looking outside of your database at the external sources.

Data is created by customers’ internet behaviour. Email openings, ad clicks, and form submissions are three of the most common ways to engage users. Your marketing automation software is likely to have applied some level of uniformity to actions like email opens and ad clicks before they enter your database. Filling out forms, however, can be more difficult. Even though forms offer crucial information on sales leads, they can cause chaos on a database if they have blank text boxes.

Due to the requirement for format consistency on entry, dropdown menus are frequently used by marketers to standardise data. If our example company wants to keep things on a high level, it could utilise a dropdown menu with options like “Master’s Degree” or “Graduate School.” If it needed more specifics, it could also create separate alternatives for “M.A.” and “M.S.” The last step could be to ask the data team to create a filter that categorises responses into “Arts” and “Science” on the back end.

Step 4: Standardize the database’s current data.

Great data standards that only apply to fresh, incoming data are useless. That only solves half the problem. It’s recommended practice to use the same criteria for data that has already been gathered. In this procedure, filters are important. Users can improve data sets using data filtering to add only the information they require for a given activity or campaign and to remove “information that may be repetitious, useless, or even sensitive” (Techopedia). When standardising data, leave all fields and data in the current database intact while still applying filters.

In the last example, users entered a wide range of information regarding their degrees. Of the 300 respondents who said they had a master’s degree, some listed the type of degree (arts or science), some the institution, and yet others the exact topic of study. The business could ask its data team to create filters that enable it to separate such data as necessary.

Although standardised data requires a significant commitment, the benefits can be enormous. Every team inside the organisation will have access to the same depth and quality of data for their projects and be able to evaluate data in the same way.