Got the Bad Data Blues?

For over 20 years Zilliant has helped B2B companies make use of their valuable data resulting in better pricing and sales decisions. However, time and time again, we hear companies tell us when we first meet that they lack the data they need to generate sufficient insights necessary to drive growth. Or the data they have is in such ‘bad shape’ we won’t be able to ever help them. Often, they blame poor data collection policies or compliance, or other systemic issues including lack of appropriate systems to capture customer interactions and marketing intelligence.

What we perhaps hear most often is, “We aren’t collecting enough data for you to help us.” However, the problem isn’t that companies aren’t collecting enough data. In fact, the problem is quite the opposite. Companies today are suffering from increasing complexity in the business environments in which they operate. They can be bogged down in data that is not being captured, shared and effectively used. 

The ‘Bad Data Blues’ can be especially apparent in very successful, high-growth companies or companies that have grown significantly through acquisition, where businesses have not taken the time and applied the appropriate disciplines to properly evaluate their strategic data needs.

Causes of Data Chaos 

The need to become digitally savvy has accelerated in almost every line of business. As companies have grown teams have become even more dispersed, and technology has been the answer to help teams become more efficient. But this evolution has landed some companies in a chasm of complexity and chaos.  

As companies expand, individuals and teams become more and more siloed, and many unique manual processes emerge. Department-specific technology is acquired without consideration of its impact on the whole ecosystem of the enterprise. Customer touchpoints are ignored or undervalued. Analyses become rigidly concealed. There are so many moving parts and so many sources of data that data models become fragmented, valuable data becomes trapped, and as a result, data is perceived as being a nuisance rather than an asset. 

Because much of the technology that companies implement is specific to a job function and the tools brought in are intended to optimize individual processes, the data acquired and consumed by these systems becomes more fragmented. This results in a hodge-podge of process and technology that devalues enterprise data and ignores potential holistic use cases that could benefit other departments and functions. 

Common Mistakes to Overcome 

“Data” is a Latin word that means “things given, gifts to others,'' but nowhere does it say who acquires the gift or receives it. It’s the handling of the data, the passing of data or lack thereof that is the most common form of grief. Some of the more common missteps that companies make in this regard include: 

• Lack of rigor in data collection 
• Dependence on manual processes 
• Disjointed technology 
• Siloed teams and lack of communication 
• Bad data in - bad data out 

The impact of these common deficiencies can be different for each company. Some of them are easy to spot, such as teams not communicating with one another or data being captured in isolation and not shared effectively.  

When teams evolve over time and become too dependent on tribal knowledge or the entrenched, familiar, institutionalized processes they’ve used for ages - and don’t invest time and resources to improve the way in which they capture and use data - they don’t realize their fullest potential. Not only that, but these companies are at risk of losing talent over time as other potential employers invest in upgraded contemporary technology and tools to make efforts more efficient and therefore jobs more rewarding. 

And then there’s the notion of ‘bad data.’ This idea typically translates to companies not fully capturing the data they need, or the data they do collect lacks hygiene because of the manner in which it’s collected and stored. The common perception that “our data is bad” or “we have a complete lack of data” can be a faulty premise. Often what is missing are just pieces of data. Sometimes, only a minor amount of additional data would add tremendous value to what’s already being collected, however deficient that may be. 

Learning the Truth 

There’s a joke in linguistics circles that translates well to data quality.  When someone asks a multi-linguist if they speak a foreign language a smug response could be: “I don’t know, I’ve never tried.”  

Data quality is the same: if you believe your data is great, it’s probably not as good as it could be. And if you believe your data is so poor it has little to no value, you may also be incorrect. All data has some deficiencies, no data set is perfect...and that’s ok. Fully understanding your data is not the most glamorous or rewarding aspect of its use. However, it’s the most instructive. 

Often a company may not know what data is actually missing until an analyst or data scientist comes on board, or outside expertise is brought in with a valuable use case that requires specific data. If something is not currently captured or is captured but not readily stored or combined with other data, this can reveal the need for change. 

Data that isn’t in the best shape can be the catalyst for change, in terms of data collection practices, storage and use. The worst thing a company can do is believe their data is so hideous they don’t care to look. The best course is to acquire the tools and expertise you need to reshape your data and apply practices that will allow your business to fully exploit the value of this precious asset so you can leverage it to its greatest extent. 

What’s Next? 

A good first step is to engage an expert that has evaluated hundreds of global enterprise data sets. Zilliant employs a diagnostic process for large B2B enterprises to help them better understand the data assets they have and how they can be effectively used for better pricing and sales decisions. You can contact Zilliant today for a no-nonsense assessment of your needs.  

Getting the relevant data out and passing it between disparate systems (data lakes and data warehouses, ERP, CRM, CPQ, eCommerce or marketing automation systems) is made much easier with Zilliant's Real-Time Pricing Engine. This robust REST API service is a non-disruptive data integration technology that can easily connect Zilliant solutions to your data storage platforms and customer-facing systems, while performing complex calculations on demand. This helps leverage your data to facilitate better and faster commercial decisions in the form of pricing and sales guidance with data that you already possess today or could capture in the future. 

If you want to learn more about how Zilliant is helping businesses make better decisions with their data, connect with me on LinkedIn, send me an email or contact our team here.