Collecting data about customers and their preferences was once a time-consuming and expensive process, but most modern businesses take it for granted. However, there is no inherent value to collecting data; it needs sorting, processing and maintaining to impart true value, reveal patterns and yield insights.

Data frequently enters a business from a number of sources and in a wide variety of formats. Feeding these inconsistent raw numbers into business intelligence software or analytics systems will produce unreliable outputs. Instead, it must first be organised. The process of aggregating data from disparate sources and converting it into common units is called data standardisation. Once data is standardised into a statistically comparable format, it can be used as the foundation for your data-driven marketing strategy.

How does data standardisation work?

The data supply chain describes the process by which data is turned from numerical input to useful, actionable output. The first step of this process is collection. Data likely comes from a number of sources, each using its own format, schedule and repository. The next step is data standardisation, and this is followed by transformation or enrichment, integration, and finally, activation. Data standardisation ensures that data moving on to the later steps in the data supply chain is consistent and comparable.

In the data standardisation process, the complex data sets gathered during the collection phase are run through predetermined fields to produce a homogeneous dataset. The result of this processing is stored in a unified repository, rather than returning it to its various sources.

How does a business set up a standardisation system?

The decisions made during the design of a data standardisation system have a significant impact on its function. The business must begin by accounting for all of its data sources, understanding their various utilities, and considering the end-goals of collecting the data. The process of designing a standardisation system can only proceed once a diverse range of stakeholders from across the business have weighed in on these considerations.

Once the needs of the various data users and stakeholders have been accounted for, the business must establish rules for standardising the data to ensure that the outputs of the standardisation process meet the requirements of the stakeholders. This may be as simple as renaming the variables, but in other cases such as open-ended text input boxes in forms, it will require more creativity. The final step before putting the system into action on new data is to run it retroactively to ensure that the business”; existing data is indexed in the same consistent format.

Defeating data fragmentation

Data fragmentation -; the problem of data being dispersed among multiple repositories -; is one of the most significant obstacles businesses face when implementing a data standardisation system. When data is fragmented, it can be extremely time consuming and expensive to access and process, which makes it harder for businesses to make the right decisions.

In many cases, data fragmentation is the result of overzealous data collection. Collecting data should always be a process that begins with the question “;why?”; and where every piece of data gathered can be traced to a desired business outcome. Especially in the modern age of data security with regulations like the GDPR, it is critical that businesses avoid collecting extraneous data. In some cases, businesses may also have to contend with “;dark data”;, information which is stored in inaccessible systems or offline. This data can”;t be collected for processing and is essentially worthless, and businesses should avoid collecting it.

The right tools for the job

Overcoming data fragmentation requires a method of controlling inputs and organising data, and it would be a full-time job for a person. Fortunately, there are powerful, purpose-built automated tools available for businesses. For instance, Tealium”;s Universal Data Hub is a suite which includes tools such as EventStream that collect, control, and deliver server-side event data in real time. The data collected by EventStream is then passed through the data layer, where it is sorted according to predetermined rules. The output, a consistent dataset, is then easy to share with stakeholders throughout the business using another Tealium tool: DataAccess. This uniform, accurate, and up to date data is the perfect prerequisite to driving customer engagement.

While the broad feature set offered by the Universal Data Hub may seem imposing at first, it”;s surprisingly easy and cost effective to set up and run. Smart data standardisation can let a business cut costs, acquire more customers, and increase competitiveness, and with powerful automated options available, there”;s no excuse for businesses to continue to struggle with siloed data and outdated processes.

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