Understanding Data Modeling

Modern business intelligence (BI) and data analytics platforms aim to put data insights directly into the hands of, well, anyone who can use these accessible data insights to make decisions. Enterprises are phasing out legacy systems that silo data in disparate repositories and require specialists to query data and create reports. In other words, they are breaking down the barriers that once made data analysis the domain of specialists rather than everyday employees.

Industry experts herald the benefits of data democratization. Listed among them is providing a user the ability to access data through “plug-and-play interfaces”, rather than requiring data science degrees to understand the process. This can drive faster and more widespread data-driven decision-making at every level of an organization, which in turn can drive outcomes aligned specifically with company goals.

Employees utilizing self-service data analytics tools encounter a user-friendly interface akin to using an online search engine. With a basic query like “sales revenue monthly by store,” a retail executive can compare the earning performance of different stores across the country at a glance. In other words, users are able to ask direct questions in natural language to get quick insights.

But what has to happen behind the scenes to facilitate a smooth and intuitive self-service analytics user experience? Data modeling is one of the processes laying the foundation for effective search analytics.

Here’s an overview to help you better understand data modeling.

The Basics of Data Modeling

Through the process of data modeling, enterprises can define metadata and other aspects of data, as ThoughtSpot outlines. Two key ways of doing so are by naming data columns according to how users will be most likely to search for them and predefining how columns can be searched/aggregated by users down the line.

While many modern analytics systems automatically provide metadata, specialists are further able to customize metadata and define relationships according to enterprise needs — either by individual table or by editing all the tables at once using a single file.

In other words, during data modeling, specialists take the time to understand and anticipate the requirements non-specialized users will have while harnessing analytics tools — and structure accordingly so the search tools are as user-friendly and beneficial as possible.

Understanding Analytics Requirements

The primary goal of effective data modeling for data analytics is boosting searchability for business users, helping them get the most relevant search results possible using natural, intuitive search terms. For this reason, a huge component of effective modeling is understanding what people are going to search and why — then adjusting the metadata as needed.

As SearchData Management notes, this makes “gathering requirements from business stakeholders” a central tenet ineffective data modeling. Data scientists need to consider: Who will be analyzing said data, what will they be asking and how will they be asking it?

One challenge enterprises face in optimizing data analytics execution is bridging the gap between back-end experts with specialized training and front-end users with little or no specialized training. Sometimes it feels like speaking two different languages — with data specialists viewing data in terms of complex analytics and stakeholders viewing it in terms of business challenges/goals.

Data modeling works best when it’s undertaken with a clear idea of how users will interact with analytics interfaces and what business questions they will be trying to address — as well as how they will naturally phrase such search queries.

The convenient thing about data modeling today is that much of it is automatic, which gives enterprises the option to customize as much as they want without requiring them to manually undertake the tedious process.

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