3 Easy Steps to Finding Patterns in Big Data
It’s normal to compare your company and yourself against competitors in the industry and colleagues in your department. One of the most powerful ways to defend against competitors and shine amongst your peers is to find that one golden nugget of data that drives action. In technical terms, it’s the art and science of mining your data to find key patterns.
Business leaders hear about the data analysis that other companies in their industry are doing, and they want to know how they too can reap the benefits. Team members sit in on Zoom meetings and wonder how they can shine amongst the group by presenting key trends that drive better strategies.
If you are challenged by how exactly to go about extracting the key takeaways amidst millions of records worth of data, you’re not alone. Whether a data analyst, executive, product manager, or in the marketing world, we’re all looking for the easy path. Use these steps to find insights and patterns, so you can be the one that shares an actionable data point that leads to the upcoming big-ticket item in your strategic plan.
Step #1: Data collection
Whether you are part of a small company or leading an enterprise, you will want to collect data from multiple sources. Every data source claims important elements and insight. You will look within your organization for data from sales, marketing, customer relations, billing, and more. You may even choose to aggregate third-party data in order to capture data points that you don’t currently have, be it propensity-to-buy models or demographics data. In the Age of Information, the world is your oyster. Knowledge brings with it power, but it adds to the complexities in the interpretation as well. That’s where step #2 comes into play.
Step #2: Get Organized and Clean Up Your Data
Data analysis is only as good as the data that you have collected. If your data is sourced from various places, which is typical, the formatting may be disparate. For starters, the data needs to go through a cleansing process. Data records need to be uniform as there are nuances in each data set. Sometimes it’s difficult to make sense of it all, so let’s look at an example:
A company has collected data from both its marketing department and sales department. These came from two databases, so the data looks different. Meaning, that the formulas and naming conventions are distinct. It shows:
| Database | Prospect | Called In | Annual Rey | Lead Priority |
| Marketing | Bob’s Toys | Yes | $1 Million | High |
| Marketing | ABC Trucks | Yes | $1 Million | High |
| Sales | Christmas Every Day | No | $15 Million | High |
| Sales | Delia’s Doll Co. | No | $15 Million | High |
The disparity lies in how each department defines a “Lead Priority” as “High.” Marketing defines a lead as “High” if they have called into the company. They do not factor in the prospect’s annual revenue. In contrast, Sales defines a lead as “High” if their annual revenue is $15 Million or greater. They do not factor in whether the prospect has called in. When analyzing data, you need to understand its nuances.
There are also examples where data truly needs to be cleaned up in order to be put to good use. This is commonplace as each department compiles its data for different purposes. Check out this scenario:
A company is building a direct mail campaign. They are merging data from the customer service department and accounting. As you can imagine, the accounting department’s records are perfectly in sync, while the customer service department appears as follows:
| Prospect | Address | City | State | Zip |
| Bob’s Toys | Main Street | Las Vegas | Nevada | |
| ABC Trucks | 156 Clover Ave. | |||
| Christmas Every Day | 888 Stewart Pkway | Greensboro | GA | 30642 |
| Delia’s Doll Co. | 500 Peachtree Rd. | Atlanta |
There are key data elements that are missing. The addressed need to be cleaned up to be ready for use for the direct mail campaign. With thousands if not millions of records, it is not possible to update these manually. There are various third-party resources that will conduct what is called a “data append” in order to modify/update these addresses accordingly.
Step #3 Make Analysis Easier with Embedded Analytics
There is no shortage of tools to help you derive insight better and quicker. The tricky part is determining which one. As soon as you start leveling up your analytics, your end-users and clients will want more. It’s best to present them with everything they need from the get-go, like:
- Real-time Data
- Dashboards
- Self-Reporting
- Automation
- Security Benefits
- Drilldown/Up Capabilities
- Business Intelligence
- Advanced Reporting
Get the guidance you need by finding a partner who knows the ins and outs of advanced analytics. Take your data analysis to a whole new level with embedded analytics. Reach out to the tried and true and highly innovative. They will educate you on the above capabilities and features and so much more. Insightsoftware makes it their business to better yours. With their support, once you find that data nugget that changes the way you do business, your ROI will be proven many times over.
Tackling Today’s Challenges in Real-Time with Analytics
Watch Now