Big data analytics and statistical modeling are some of the most important—and challenging—aspects of enterprise business analytics today. With massive amounts of data and increasingly complex business systems, it’s more important than ever to focus on making smarter, real-time decisions. Plus, with the general adoption of a data-driven mindset, we’re seeing an industry shift from BI to AI or Advanced Analytics.
What is statistical modeling?
Statistical modeling is a mathematical way to make predictions based on existing data. Per Wikipedia:
A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data. A statistical model represents, often in considerably idealized form, the data-generating process.
Where are the types of statistical data modeling?
There are many types of statistical data modeling. However, in general terms, all statistical models are either parametric and non-parametric. With parametric data modeling, there are assumptions made about the fixed number of parameters for the data set. With non-parametric modeling, there are no assumptions made regarding parameters.
What is the objective of big data statistical modeling?
With big data statistical modeling, a wide range of sources rapidly deliver a massive amount of information. Big data modeling aims to describe and predict information—ultimately providing grounded recommendations for informed business insights, decision making, and improved processes.
What are examples of data modeling techniques?
A conventional data modeling technique practiced in the financial industry is logistic regression. This data modeling technique uses a logistic curve to explore the relationship between variables. An example of this is when you apply for a personal loan. The loan officer uses a logistic regression model that considers a range of variables such as credit history, income, and loan-to-deposit ratio, which add up to your creditworthiness and predict whether or not you are likely to default on your loan.
Another example of a data modeling technique is the classic decision tree. Decision trees are versatile non-parametric algorithms that split data into subsets, helping inform options and outcomes. When paired with machine learning, decision trees are immensely scalable, highly accurate, and easily understood.
Do I need a SQL data warehouse to generate statistical modeling?
Top SQL data warehouses, such as Microsoft Azure Synapse (previously Azure SQL data warehouse) and Snowflake, offer features that enable you to perform statistical modeling. However, for big data analytics, many organizations opt for non-relational databases like NoSQL, which offer performance, flexibility, and scalability ideal for big data statistical modeling.
What are some data modeling considerations for big data?
The primary consideration for data modeling with big data is the type of statistical modeling and modeling tools you employ. Because big data has a high number of input sources with a high volume and velocity of information, the statistical model must be elastic enough to accommodate the unpredictable nature of big data. Leading BI tools are Tableau, Qlikview, and Looker — while big data modeling tools like Hadoop are among the best suited for big data applications.
How do I begin or improve my statistical modeling?
Improving your statistical modeling starts with finding the right partner who has a solid understanding of your business needs with advanced analytics and statistical modeling expertise. Having a strategic big data modeling resource such as NextPhase will help you take full advantage of your organization’s existing data and help future-proof your business intelligence and data analytics.
Do I need a data modeling consultant near me?
Having a data modeling consultant near you is beneficial for seamless integration with your analytics team and on-premise data center. However, it may not always be possible to have a consultant on site. When selecting a remote data modeling consultant, we recommend verifying that your preferred provider has an established virtual workflow with a focus on excellent communication from start to finish. At NextPhase.ai, we work with local and global clients efficiently, and the key is a well-established process and communication plan.
Have questions? Email anytime for a free consultation.
Operating globally, NextPhase.ai has 60+ years of combined consulting experience. NextPhase.ai delivers analytics and data science solutions, which unlock the value of people, process, and technology investments. Leveraging deep knowledge in BFSI, retail analytics, CPG, manufacturing, technology and logistics, we create long lasting value for top companies in the Bay Area and Northern California.