Making Analytics Work For Your Company

September 2021

You want to use data and analytics to drive your business forward, but you can’t simply input data into a business intelligence tool and expect fantastic results.

Here are four simple steps to achieve success with data analytics in your business, according to Rob Reynolds of analytics provider INxSQL Software. 

1)  Do the research necessary to find a product that is not limiting. A good dashboard analytics program will not be limited to a certain group of graphs or dashboards. Being able to see the data the way your business needs to see it is key. 

2)  Involve the people at your organization who will be using the analytics. If they like it, they will be more likely to use it.

3)  Purchase and invest in training for each person who will use it. This can be group training or individual. “When we understand how to use things, we are more likely to use them,” Reynolds explains.

4)  Perform process reviews periodically to gather how your organization is using the analytics. Diagnose how your team members want and need to use the analytics moving forward.

Whether you’re analyzing customer data to improve your marketing segmentation and capital allocation, gathering data from disparate sources to create predictive models, or sorting through cyber security data to make your infrastructure more secure, following best practices ensures you’re making data-driven business decisions, according to technology blogger Shelby Blitz.

Decide On Key Metrics
It’s important to decide on the metrics that are meaningful to your business. For instance, if you’re analyzing advertising and customer data to uncover your best customer acquisition data, then conversion rates and customer spending data are valuable. Eliminating extraneous data allows executives to focus on relevant metrics to make decisions that may change the course of a division or company. 

Some questions to consider when evaluating which metrics to focus on:
  • What is the goal for the data collected?
  • How will this data help achieve business goals?
  • Am I data mining or conducting predictive analysis?

Plan Your Goals
Take time to plan the goals of your analytics, especially when merging data from many different sources. Ensuring calculated fields are consistent, creating proper dimensional hierarchies that allow users to drill down into the data, and other data modeling best practices will make it far easier to model complex data, according to Blitz. 

Choose A Good Model
To gain actionable insights from all your data, you need a way to model the data correctly. This means avoiding common errors such as ignoring small data sources, failing to account for how calculated fields could affect your model, and implementing poor naming standards. Without taking these potential errors into account, you can end up with data models that are cumbersome and confusing.

Create Dashboards That Work
Visualization is at the core of how we interact with data. Things like leading with the most newsworthy information and gradually working your way to background details, choosing visualizations that display the relationship between the highlighted information and the overall context, and ensuring that key stakeholders can understand the key points with a simple glance will go a long way toward improving your dashboard design, Blitz advises. Proper planning will ensure your visualization is relevant, logically ordered, and simplified so users aren’t overwhelmed.

Choose the Correct Tool
Many tools either focus on visualization, lack the power to handle large datasets pulling from disparate sources, or are unintuitive, placing additional demands on IT staff. Look for a solution that offers single-stack capabilities and is powerful enough to handle even the most complex data while still being intuitive enough for less technical users. Rather than requiring separate tools for data preparation and visualization, the single-stack approach wraps an analytical database, built-in ETL (Extract, Transform, Load), and a robust data analysis and visualization suite all into one, so you won’t need any other business intelligence tool. 

This is Part 2 of a two-part series on data analytics.

Download the PDF here.

Would you like to share your experience? Contact Amy Nijjar [email protected]