Ted Friedman, VP Research at Gartner, shared an interesting view on data in a recent Linkedin post:
“One of the common challenges we observe with data management strategy and execution is a desire to get all data “under control”. Reality is that in the modern digital business environment, and particularly amid current business conditions, it’s just not possible and pursuit of it can be detrimental to the organization. Rather than always striving for perfectly structured data and completely consistent data-related practices, #data and #analytics leaders can find advantages and opportunities that arise from disorder and can generate significant business value when they challenge conventional data management wisdom.”
This was the post I quoted from:
From Ted’s statement, we can derive four forces that are impacting the lack of control:
- Data: You don’t need additional research to know and understand that the amount of data continues to grow. This growth leads to more scale, diversity, complexity – and all at a faster rate of demand to analyze, understand, and organize it every day.
Examples: Everybody knows the stats that proof how much data is being produced and published every day. So scale is what we have accepted, same as the new speed which just increases. Diversity adds human behaviour, its social and biological causes.
2. Distribution: While analysts and vendors often refer to the MDM (master data management) market as being all about the “golden record in one single hub,” now, the market has evolved from collect to connect. We see business need in a time of hyper-connectivity, creation, and consumption of data everywhere; data is just more distributed than ever before.
Examples: Customers create reviews of fashion and ratings of hotels from around the world on their mobile phone…instantaneously. Or if customers create video food reviews from around the world on their mobile phone…instantaneously just as they are consuming their meal. Or 1000s of vendors and suppliers create and upload new products to e-commerce marketplaces – millions of new SKUs ready for consumer perusal and commerce.
3. Human factor: Even as AI drives more automated decisions, we humans still play the key role. We create data, we consume data, we have our own behaviors in business roles or private. We have company policies, industry regulations, data governance rules, and more. And we make mistakes.
Examples: When writing new product copy, marketers make mistakes by adding the wrong product code or energy class of a fridge. Consumers or vendors make typos when adding on their address in written or online forms.
4. Change: Cliché, yes, but the only constant is change. And the year 2020 has played with “change” like a bad joke. The pace of change will only continue to accelerate…affecting our lives, businesses, and of course the data on which we rely on personally and professionally.
Examples: When stores were forced to close due to the pandemic, most owners had less than four weeks to pivot and sell online to simply survive, including looking at new online selling channels like Instagram commerce? Or, when Covid-19 affected your supply chain, did you have to change your sourcing strategy ASAP?
Applying nearly 20 years in the master data management (MDM) and product information management (PIM) market, I have seen that the expectations of companies are shifting like never before.
Here are the five new emerging criteria for businesses when selecting a data platform for their digital transformation:
1. Scale to large volumes and complexity if needed: The data and computing power are now more readily available and affordable.
2. Be ready to adapt to change fast in no-code style by adding a new channel, a User Interface Widget or a full add-on App for your industry. Go to where your users, whether B2B or B2C, are. Be ready to adapt to change fast in no-code style by adding a new channel, a User Interface Widget or a Full add-on App for your industry
3. Empower data governance and user experience: make life for all internal and external users depending on their need while you execute data quality and governance rules to enable better data for better decision or more sales. For example this can be a simple task list I have to do after my login, or an easy way to select my products I want to publish to Amazon for business users. For admins and power users this means I have full control of defining data quality conditions, workflows and rules for example to ensure a compliance with regulations.
4. Enable connectivity: Build a connector fast and directly to enterprise applications and e-commerce channels. Less manual work will provide scale and reduction in errors.
5. Use automation for match, merge, better operational efficiency. Automation and AI can be a good thing. Use it for its strengths while never forgetting the human element.
Over the coming weeks and months, we’ll explore these five new emerging criteria more deeply. Do these criteria make sense to you?
PS: This is what Gartner says on the Magic Quadrant for MDM Solutions.
Data Talks 