I just came back from a Product Information Management (PIM) event this week had had a lot of discussions about how to evaluate vendors and their solutions. I also get a lot of inquiries on vendor selection and while a lot of the questions center around the functionality itself, how to evaluate is also a key point of discussion. What peaked my interest on this subject is that IT and the Business have very different objectives in selecting a solution for MDM, PIM, and data quality. In fact, it can often get contentious when IT and the Business don't agree on the best solution.
General steps to purchase a solution seem pretty consistent: create a short list based on the Forrester Wave and research, conduct an RFI, narrow down to 2-3 vendors for an RFP, make a decision. But, the devil seems to be in the details.
Is a proof of concept required?
How do you make a decision when vendors solutions appear the same? Are they really the same?
How do you put pricing into context? Is lowest really better?
What is required to know before engaging with vendors to identify fit and differentiation?
When does meeting business objectives win out over fit in IT skills and platform consistency?
Data management is becoming critical as organizations seek to better understand and target their customers, drive out inefficiency, and satisfy government regulations. Despite this, the maturity of data management practices at companies in China is generally poor.
I had an enlightening conversation with my colleague, senior analyst Michele Goetz, who covers all aspects of data management. She told me that in North America and Europe, data management maturity varies widely from company to company; only about 5% have mature practices and a robust data management infrastructure. Most organizations are still struggling to be agile and lack measurement, even if they already have data management platforms in place. Very few of them align adequately with their specific business or information strategy and organizational structure.
If we look at data management maturity in China, I suspect the results are even worse: that fewer than 1% of the companies are mature in terms of integrated strategy, agile execution and continuous performance measurement. Specifically:
The practice of data management is still in the early stages. Data management is not only about simply deploying technology like data warehousing or related middleware, but also means putting in place the strategy and architectural practice, including contextual services and metadata pattern modeling, to align with business focus. The current focus of Chinese enterprises for data management is mostly around data warehousing, master data management, and basic support for both end-to-end business processes and composite applications for top management decision-making. It’s still far from leveraging the valuable data in business processes and business analytics.
The number one reason I hear from IT organizations for why they want to embark on MDM is for consolidation or integration of systems. Then, the first question I get, how do they get buy-in from the business to pay for it?
My first reaction is to cringe because the implication is that MDM is a data integration tool and the value is the matching capabilities. While matching is a significant capability, MDM is not about creating a golden record or a single source of truth.
My next reaction is that IT missed the point that the business wants data to support a system of engagement. The value of MDM is to be able to model and render a domain to fit a system of engagement. Until you understand and align to that, your MDM effort will not support the business and you won’t get the funding. If you somehow do get the funding, you won’t be able to appropriately select the MDM tool that is right for the business need, thus wasting time, money, and resources.
Here is why I am not a fan of the “single source of truth” mantra. A person is not one-dimensional; they can be a parent, a friend, or a colleague, and each has different motivations and requirements depending on the environment. A product is as much about the physical aspect as it is about the pricing, message, and sales channel it is sold through. Or, it is also faceted by the fact that it is put together from various products and parts from partners. In no way is a master entity unique or has a consistency depending on what is important about the entity in a given situation. What MDM provides are definitions and instructions on the right data to use in the right engagement. Context is a key value of MDM.
As the new analyst on the block at Forrester, the first question everyone is asking is, “What research do you have planned?” Just to show that I’m up for the task, rather than keeping it simple with a thoughtful report on data quality best practices or a maturity assessment on data management, I thought I’d go for broke and dive into the master data management (MDM) landscape. Some might call me crazy, but this is more than just the adrenaline rush that comes from doing such a project. In over 20 inquiries with clients in the past month, questions show increased sophistication in how managing master data can strategically contribute to the business.
What do I mean by this?
Number 1: Clients want to know how to bring together transitional data (structured) and content (semi-structured and unstructured) to understand the customer experience, improve customer engagement, and maximize the value of the customer. Understanding customer touch points across social media, e-commerce, customer service, and content consumption provides a single customer view that lets you customize your interactions and be highly relevant to your customer. MDM is at the heart of bringing this view together.
Number 2: Clients have begun to analyze big data within side projects as a way to identify opportunities for the business. This intelligence has reached the point that clients are now exploring how to distribute and operationalize these insights throughout the organization. MDM is the point that will align discoveries within the governance of master data for context and use.
Watching the Mobile Device Management market is a bit like watching a sneeze. My colleagues Christian Kane and Benjamin Gray are tracking nearly 75 vendors in the space, many of them just a few years old. We've also seen a fresh round of acquisitions as established endpoint management vendors look to shore up their flanks and freshen their portfolios.
Differentiation amongst vendors is hard to come by, as is long-term enterprise MDM experience. And that's what makes LANDesk's acquisition of Wavelink interesting. Mobile Device Management in an industrial or field setting is more than just enforcing passcode restrictions, enabling remote wipe in case of loss, or rolling out software. Companies like Wal-Mart and FedEx have significant portions of their businesses that depend on handheld devices for package delivery, inventory and point of sale. MDM in these settings involves a range of capabilities from diagnosing connectivity and printing issues over the air, to interfacing modern mobile apps to mainframe-based warehouse inventory systems.
Perhaps the best way to describe what Wavelink does is "Industrial MDM". They boast 15,000 customers in 85 countries, and have been in the business for several years. The flagship product is called Avalanche and its historical strengths have been in Windows Mobile environments. They added iOS and Android a couple of years ago and are about to release their 2nd generation release of the same.
Why it makes sense for LANDesk:
Competitive: It gives LANDesk the opportunity to own the IP for MDM technology and positions them differently than other MDM solutions on the market given Wavelink's industrial focus.