Are you drowning in data? Do you feel overwhelmed -- or underwhelmed -- with the myriad of options available to deal with your data problems? (especially in the area of pricing?)

It's the era of big data, and many retailers are discovering that hope is found through new technologies like event stream processing or Hadoop.

However, as I head off to The Big Show - NRF 2016, I'd like to expound upon step one of the four steps to a successful pricing journey - data readiness.  Not only does this step require courage, but it serves as a testimony to the prudence of building the best possible foundational layer.

Data readiness is all about preparation. This preparation will help you find meaning and purpose in the heart of your data like never before. Like the completion of a weekend project (and like weekend projects -- it never really ends), you'll soon discover that hard work and patience does pay greater dividends.

When you consider the data readiness step in your pricing journey, think about these four opportunities:

1. Collection
2. Profiling
3. Exploration
4. Harmonization

Why these four? These are the steps to managing your data layers to produce data-driven insights, shape strategic decisions, prepare you for analytics and improve overall organizational adoption.  Let's discuss further:

Data collection
The most tedious and laborious of all the steps revolves around the collection of your pricing history. What are you collecting? How often? For how long? Richness in pricing history sounds like a no-brainer, but can often times be cumbersome to collect, store, and retrieve when you are ready to start your pricing journey.

Most will tell you that you need at least two years of history to ensure accurate time-series and econometric forecasts to better understand trend, seasonality and life cycle effects. While that's true, what's even more important is the ability to provide promotional history, product attribution, competitive pricing, customer scoring, and much more. Far too often these reside in MS Excel spreadsheets on individual laptops across the organization.

I understand I sound like Captain Obvious here, but the richness of your data collection creates a much easier road ahead. Data insufficiency is the leading cause to failure for pricing projects, simply because the data is not "readily" available. When performing regression modeling to understand pricing effects, it's this data richness that will explain price sensitivity, serve as variables for lift effects and be the causals to understanding why a model fits better than others. Companies that heartily embrace embrace this step benefit from building enterprise data models that provide data access and readiness for more than just pricing -- because the entire organization can benefit from this data.

Data profiling
Once collected, the investigation begins. What is your enterprise data model telling you? Are their competitive gaps? Are your customers responding in a new way? Where are the data gaps that prevent building our bridge to success? The investigation that is data profiling ensures that the enrichment process can occur. When you quickly see the 80/20 rule apply to your data, the costs of storing it becomes staggering. I recently saw the shock and horror of a company that embraced this step only to discover that over 50 percent of their third party data capture was in error. They knew they had a problem, they had no idea the problem was so great -- and, by the way, they were paying for those records too! So, let your investigation begin and get your pricing data house in order!

Data exploration
Once you know the facts from your investigation, you're now ready to explore your pricing data. The ability to see, understand, and measure changes in pricing variance, for example, is critical in this step. Does enough variance exist to better understand a demand signal or see elasticity effects?

Exploration exposes new insights like never before. In this step, models are improved because you're discovering key attributes that drove pricing decisions that intuition couldn't explain. The use of histograms, the ability to plot trends for sales/inventory against pricing over time and the use of data mining techniques to understand unstructured data brings newer insights. This phase creates the beginning of your data-driven insights journal that quickly becomes your data library. They say knowledge is power, so go explore and find new power supported by your data.

Data harmonization
Collecting, profiling and exploring your data gives you the ability to now create a standardized and harmonized process. This ability to create a repeatable and sustainable data model ensures you are now able to aggregate data at the right levels. You can add the right data layers to unfold better strategic pricing decisions and you can build new analytics hierarchies with embedded with ample attribution -- and you can ensure new data stores can easily be added to the process.

Omni-channel engagement demands this type of sustainable data model as your data deluge flows from your customers, manufacturers, multiple channels and more. Harmonization brings the data together in such a way that it's useable, insightful and purposefully meaningful to the end users. Whether it be a data scientist, a buyer, a pricing analyst or a C-suite executive, harmonization finalizes the data model that will lead you to the next step in your pricing journey: Pricing strategy!

Join us as we discuss more about data readiness and pricing insights in more detail on Tuesday, January 26, 2016 in a webinar entitled The Price is Right - Omnichannel Data Readiness for Pricing Analytics.  I look forward to seeing some of you at NRF 2016 -- cheers!