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Finding the Patterns in Big Data

It's natural to compare your business and yourself to industry competitors and colleagues in your department. One of the most powerful ways to defend against competitors and shine amongst your peers is to discover that one gem of data that drives action. In technical terms, it’s the art and science of mining your data to find key patterns.



When business leaders hear about data analysis being used by other organizations in their field, they want to know how they can benefit as well. Members of the team attend Zoom meetings and question how they may stand out from the crowd by presenting key trends that will help them develop better strategies.


You're not alone if you're having trouble figuring out how to extract the most important information from millions of data records. We're all searching for the easy way out, whether we're data analysts, executives, product managers, or marketers. Use these techniques to uncover insights and patterns so you may be the one to deliver an actionable data point that leads to the upcoming big-ticket item in your strategic plan.


#1 Data Collection

Whether you're running a small business or a large corporation, you'll want to collect data from various sources. Every data source claims to have useful insights and elements which is why it’s crucial to hunt for data from sales, marketing, customer service, billing, and other departments within your company. You might even decide to aggregate third-party data in order to capture data points that you don't have yet, such as propensity-to-buy models or demographics data. The world is your oyster in the Information Age. Knowledge confers power, but it also adds to the complexity of interpretation. That's where the second step comes in.


#2 Clean Up Your Data

The quality of your data analysis is only as good as the data you've collected. If your data comes from a variety of sources, as is common, the formatting may be inconsistent. To begin, the data must undergo a cleansing procedure. Because each data set differs, data records must be consistent. It can be tough to make sense of it all at times, so here's one example:


A company has collected data from its marketing and sales departments. The data is different since it originated from two distinct databases. That is, the formulas and naming conventions are distinct. It demonstrates

​Database

Prospect

Called In

Annual Rev

Lead Priority

Sales

​ABC Toys

No

$10 Million

​High​

Sales

Demi’s Doll Co.

No

$10 Million

High

Marketing

Christmas Galore

Yes

$2 Million

High

Marketing

Bob’s Cars

Yes

$2 Million

High

The disparity stems from how each department defines "High" as a "Lead Priority." Marketing defines a lead as “High” if they have called into the company. They do not factor in the prospect’s annual revenue. Sales, on the other hand, considers a lead to be "High" if its annual revenue is $10 million or more. They don't take into account whether or not the prospect has called in. When analyzing data, you need to understand its nuances.


There are instances when data needs to be cleaned up before it can be used effectively. This is standard practice because each department collects data for various purposes. Consider the following scenario:


A company is building a direct mail campaign. They're combining data from the customer service and accounting departments. As you may expect, the accounting department's records are in immaculate order, whereas the customer service department looks like this:

Prospect

Address

City

State

Zip Code

ABC Toys

Park Avenue

New York

New York

Demi’s Doll Co.

Hollow Road

Montgomery

Alabama

53780

Christmas Galore

Carlton

Oregon

Bob’s Cars

Alpine Drive

CA

There are key data elements that are missing. To prepare for the direct mail campaign, the addresses must be cleaned up. It is impossible to manually update thousands, if not millions, of records. There are a number of third-party resources that will do a "data append" in order to edit or update these addresses accordingly.


#3 Make Us of Embedded Analytics

There is no shortage of tools to help you derive insight better and quicker. The difficult part is deciding which one to use. Your end-users and clients will demand more as soon as you start improving your analytics. 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


Find a partner who understands advanced analytics and can provide you with the assistance you require. With embedded analytics, you can take your data analysis to a whole new level. Reach out to the tried and true and highly innovative. They will teach you about the capabilities and features listed above, as well as much more.