Optimize > Analytics Review Process

Why

The collection and analysis of data can be used to make important business decisions based on product performance and users’ behavior. For instance, it can be used to identify market patterns, ways to optimize (including SEO and conversions), and to analyze impacts after business decisions.

However, the most notable advantage of data analytics is the ability to do predictive modeling. Modern algorithms can predict where the market and user patterns are likely to head. This can easily make your organization more proactive in maximizing the return on investments.

How

Consider the following when thinking of building an analytics suite:

Start early in the life cycle

Consider the analytics aspect of data from the very beginning of the product life cycle. Data needs to be modeled very differently to your normal transaction processing application. Consider building a dimension or a Kimball model that is independent yet connected to your main relational data model.

Data-driven API endpoints

Focus on building data-driven APIs rather than functionality driven APIs. For example, engineer your endpoints to return raw data in universally accepted formats (E.g.- OData) and use the client-side to process data. This in contrast to returning processed data makes it easier to build data analytics applications later.

Data visualization

There are many tried and tested off the shelf applications available for data visualization and predictive modeling. If your data is universally acceptable and accessible via the above-mentioned endpoints, you’ll be able to fit these applications right into your product.

Decision making

The real success of this only comes when you include the output of data analytics (reports) in your business decisions. Do the needed alteration in your decision-making process to reflect this information.

Continuously improve

Analytics is based on learning algorithms. The more learning your algorithms have, the better it is at predicting accurate outcomes.

References

Why are Analytics Important? Dimensional Modeling Techniques Why Analytics is Important for Business