Why Modeling Languages are the Key to Data-Based Decision Making
If the pandemic has taught us anything, companies in all industries need to leverage data better to stay competitive. Studies show that over the past 12 months, the digitization of customer and supply-chain interactions have accelerated by an average of three to four years. And while everyone is jumping on the data-driven decision making ‘train,’ a small word of caution: historically, digital transformation success rates are very low.
There are several reasons for this: lack of executive sponsorship, disconnection from business priorities, fear of the unknown, and overly ambitious targets. But, increasingly, there’s a more significant reason that gets in the way of transformation – a lack of trusted data. A data initiative that doesn’t correctly connect and analyze disparate data sources, so they are trusted across the enterprise, will fail.
Consider this. Your data has the potential to provide insights into go-to-market strategy, helps identify business leads, can determine who’s ready for promotion, and can help add features your customers will love (and your competitors will envy). It can uncover hidden needs or trends your company can leverage.
But if your sales team only trusts Salesforce.com data, your marketers won’t look beyond Marketo, and your HR team refuses to go outside of Gusto, then you have a problem. To build a trusted, enterprise-wide data mindset, you’ll need to create a solid data analytics foundation you can leverage. Setting this up requires three critical areas of consideration:
The ultimate goal of building a data foundation is to establish an enterprise-wide single source of truth. This structured information model and associated data scheme ensure every data element is mastered in only one place, giving the business logic implicit in SQL queries somewhere to live. Everyone uses the same vocabulary to represent critical KPIs and data, which improves data quality, collaboration, productivity while reducing inconsistencies, which all lead to trust.
With the data model established, business users can answer questions in a self-service way. Getting the modeling layer right is key to letting end users explore data independently, so analysts are free to focus on ensuring the model’s integrity and evolving it based on business needs.
The Legacy Way: New Query, New Model
Business intelligence platforms have become pervasive throughout companies, either embedded in other applications or through a standalone platform’s self-service application. With more and more people wanting to access data sources and gain insights, the pressure for better-defined and organized central processes grows.
Data analysts play a crucial role here. With legacy systems, each time a user wants a report, the analysts build a data model.
