The pace at which data nomenclature is changing can sometimes feel overwhelming. While data governance technology is moving at a pace faster than mere humans can keep up with, it’s not overwhelming when you break it down to its components.
It’s even easier when we ask a simple question; what can data governance do for me?
After all, that question is the heart of every purchasing decision, data governance solution or not.
The solution capabilities have occasionally outstripped the ability of end users and let’s be honest, we all want the same thing. We want the “thing” to work as intended, to be useful. Larger organizations have always implemented teams to collect, analyze and make use of large data sets, sometimes with painstaking and time-consuming manual methods. In the end, data must work for us or it will work against us, and it must solve a problem.
The data you’re working with is often in disparate networks, in different formats and is, for all intents and purposes, unusable. That is, until you implement a data governance solution. Once data is collected, the next part of the solution is cleansing it, then transforming it into a format that everyone in the organization can understand.
Here’s an example. Let’s suppose your organization is trying to figure out if you should expand into another country. Initially, you’d need to define the problem.
Do you need more market share? Do you need more revenue? Are you trying to take advantage of the talent pool in that geographical location?
Next, you would need to gather the valid data to support a correct decision.
Have you tried to penetrate this market before? What are the sales prospects like in that country? Will past or future pandemics matter in this decision?
After analysis, you could make a case for moving into that geographical area to expand the business, or not as the case may be. That’s a pretty obvious use of data in the decision-making process, but what else can data do?
Exploration Of Unknown Market Characteristics
Most profitable businesses are in tune with their audience, their customers. You know what they need, want, and desire. However, there’s one thing that some businesses avoid talking about; not knowing what they don’t know.
It’s easy to get caught up in the “we know our people” side of things, and very easy to forget that there are a whole slew of customers who exist that we probably know nothing about. It’s not a fault in the system, it’s a feature. Just like a car is built for people of average size with average size families, your product or service is most likely built to fit “most” organizations in your target market.
But the margins exist.
And they’re getting bigger.
The margins are organizations, businesses and people who do not fit into the cookie cutter demographics and/or customer avatar models you’ve built. You need to find them and service them; how?
Market characteristics are the holistic overview of who buys, when and where, and the most important of all... why? Hidden deep in this information, you have customers who do not fit your marketing avatars. They’re silent, but they still buy from you.
Data governance, and the collection, analysis and structured reporting of said data can open up entirely new avenues of revenue. That small 4% of customers who do not fit your marketing avatar, most likely count for a larger portion of the wider audience you could market to, sell to, and serve.
Little Risk Product Development
We say little risk, because there’s risk in every decision. However, with greater in-depth knowledge gleaned from data sets, you can minimize that risk. Organizations can’t and in most cases probably shouldn’t, just go ahead and launch a new product or service.
Speed is always of the essence, but not at the risk of financially hurting the business. Or destroying the brand. This is especially true post-pandemic, when businesses have been scrambling to rapidly prototype new solutions and then adopt them, sometimes with no research or quality control. Sure, it can be highly profitable to rapidly prototype a new solution and launch to market before anyone else, but most organizations do not have the intrinsic customer data required to do so.
At least, not on hand. It takes a short while to figure this out, but it’s worth doing. A poorly executed application can damage a brand for the entire lifetime of the brand or product. With strict data governance policies in place, you can fast-track prototyping in a way that ensures the end user is served correctly.
Speed and agility should never come at the expense of strategy. This couldn’t be more obvious when the organization is location based and has multiple states, countries or even languages to consider. A solution implemented in Portland, Oregon, doesn’t necessarily work in Tel Aviv-Yafo.
You can, however, mitigate these faux pas with correct implementation of your most valuable asset, data.
- What’s the demand like for a newer product in each geographical area?
- Who are you targeting (don’t forget what we said about hidden customers)
- How much are they spending or willing to spend on this solution?
- What changes are they asking for if the product already exists?
- What features resonated with which buying personas and did anything miss the mark last time around?
These are just a few of the valuable questions you can answer with data that already most likely exist in your organization.
Most giant corporations and government organizations have an in-house team of marketing and brand experts. The scope of their knowledge is vast and, to some degree, infallible, especially when the organization has had the same team around for a while.
Not everyone is a giant mega-corporation and has the budget of VW or Coca-Cola to constantly measure and mark brand perception. You can post on social media, read comments and otherwise guess, but it’s incredibly hard to understand exactly how the customer sees you. 69.3 million Americans use Twitter, out of a population around 335 million. Not everyone is on Twitter or social media and it’s a false belief to simply rely on comments, likes and subscriptions to give you an overall view of your brand perception.
It’s also worth pointing out that brands are entirely organic, and are not only perceived as an individual entity, but how they relate to other brands. Wendy’s is a perfect example of an aging brand, revitalized by changing their brand perception when measured against, say, Burger King.
The value of a brand is entirely organic. Taking data from the organization and plugging it into a dashboard can give deep insights into customer perception. One thing that’s clearly obvious, Wendy’s isn’t winging it.
Their data governance team is probably constantly working alongside the marketing department to maintain a crystal-clear market perception. It’s too effective to be organic marketing maven driven. According to LinkedIn, they have 541 employees with the keyword “data” in their bio. cont
What seems like an in-house marketing wild-card, roasting Chilli’s and McDonald’s daily, is most likely a calculated, data driven tour de force.