Analyst Alert: Data Modeling Is a Required Skill
Flawed Data and Bad Decisions Result from Poor Data Models
Business analysts: you are now required to have a strong understanding of key data modeling concepts.
Visual analytics tools have elevated this skill from “nice to have” to “necessity.” As self-service analytics and data experimentation have become more common, these tools have opened the data floodgates. Business users can introduce any type or quantity of data sources. IT departments, which used to manage that data in complex BI systems, have relinquished control, leaving the responsibility in the hands of line of business. The potential for flawed data is high.
Whether you use Cognos Analytics, Tableau, Power BI or even simple spreadsheets, managing your data architecture is crucial. These tools give you power to either be a data hero…or to be the source for delivering potentially unsound data that can lead to disastrous decisions.
In this webinar recording we share the data modeling fundamentals you need to confidently charge forward. From the simple linking of a single spreadsheet to an existing data package, to a complete dimensional model of dozens of database objects, we show you how proven data modeling techniques and fundamental concepts will guide you toward building a reliable, stable and accurate analytical framework.
Vice President of Learning Solutions
Albert has more than 18 years of experience in business intelligence education and technical training. In addition to founding and running the Senturus training division, Albert also serves in various roles in the company including senior consultant and solutions architect. Before joining Senturus, he was a Senior Education Specialist at Cognos. Albert is an IBM Cognos Certified Trainer and has his lifetime CTT and certification from CompTIA.
Data modeling – the basics
- Entity relationship diagram (ERD)
- Describes the logical model of our data
- What’s in a database?
- A database is made up of a collection of tables and views
- Tables contain rows of data, organized tabularly or in columns – much like an Excel spreadsheet
- What’s in a table?
- Table columns have important attributes
- Inside a database all the actual or physical rows of data are present
- Some tables can contain millions or billions of rows, databases can get huge
- A view is a logical description of data that may exist in one or more actual tables, or expressions (calculations)
- The relational database—relationships are also known as joins
- Breaking complex systems of data into smaller pieces is the most efficient way to store and retrieve data (in most cases)
- Relatively simple systems require entity relationships to be defined; without the relationships we have trouble
- To retrieve information from a database, execute a query—language used in relational systems is structured query language (SQL)
- The where clause tells the query how the independent objects, or tables, are related
- The query returns every row from each table linked to every row from the other table, this is known as a cross-join, or cross-product
- In data modeling, whenever you querying across more than one object (database table, text file, Excel worksheet), you must define at least one logical relationship between them, that does not mean that you need to define all of the possible links between objects
- Over joining objects can also have a negative impact—you only need one join path between all objects
- Joining data sources—other considerations
- Certain tools also offer control of additional join behaviors—from IBM Cognos data modules
- Join behavior is driven by business requirements
- Control how measures, facts or numbers are treated
Data modeling—the risks
- Who’s in control?
- Which model is correct?
- Are we doing it right?
- What are the best practices?
- What is the impact on our business if we get it wrong?
- Data modeling vs. data prep
- These concepts intersect, but data prep usually refers to the following activities
- Data cleansing, transformations, binning/grouping, enhancement and data integration
- Data modeling: why now?
- BI is ubiquitous
- Role of IT has changed
- More data