How we work is fundamentally changing, not just in engineering, but across all disciplines involving information management and collaboration. There’s an escalating revolution in enterprise software, where the grand unification dreams of the past are now being set aside. Spoiler alert: there really can’t be one system to rule them all. It’s not that we haven’t tried to forge the one system in the fires of Mount Doom. But in many cases we tried and failed. Instead of a single-vendor monolithic solution, there’s renewed emphasis on specialization within a larger heterogeneous mix of options. Tools in collaboration, communication, and analysis that aren’t bound to their masters like their monolithic precursors, but instead flourish in an alliance of interconnecting and distributed technology. And here we all stand, at the turn of the tide.
A great void continues to expand in the application of Product Lifecycle Management (PLM) and even progressively simpler Product Data Management (PDM) technology solutions. A chasm exists between the large enterprises successfully engineering complex information infrastructure into manufacturing juggernauts, and the small design firms merely surviving with the digital equivalent of brute force. It’s literally becoming an abyss, a bottomless pit, baby; two-and-a-half miles straight down. When it comes to PDM or PLM, two flavors of solutions have dominated for an age: the big complex monstrosity, or the anti-solution, which usually means cobbling all things together with whatever came with Microsoft Office. The next step in PDM/PLM technology evolution must strive to fill that void, and find a meaningful compromise between the inequity of large and small, balancing robustness with agility.
Let’s state the obvious: we need more Computer Aided Design (CAD) and Product Lifecycle Management (PLM) startups tackling the big problems in unique and unexpected ways. The enterprise software landscape desperately needs an infusion of innovation and entrepreneurship, especially in PLM. Most PLM thought leadership is unsurprisingly locked into a pattern of sustaining innovation, firmly entrenched in the technology choices of the last two decades. It’s a situation I have highlighted before in Why We Need more PLM Epic Fails. We need outright disruption. Oleg Shilovitsky has called attention to the same problem in two recent articles: A Potential Surge of CAD/PLM Startups and Traditional PLM Have Reached Their Limits. The good news is there has never been a better time for entrepreneurship, technology (especially the Amazon Cloud) has removed many traditional barriers to entry. We need more people in the pool. Jump on in, the water’s fine. It’s time to innovate or die.
Any single Product Lifecycle Management (PLM) implementation is a unique combination of configuration and customization – a direct reflection of a particular company’s process, values, and structure. It’s a rare thing for PLM software to be introduced into a green field IT landscape, devoid of any data ownership competition or entrenched legacies. Not surprisingly, these pre-existing conditions color the solution, and uniqueness shines through even if two companies are executing on the exact same software platform. Long has the industry sought a magical Out of the Box (OOTB) alternative, something aiming to commoditize design and implementation as a simple turn-key package. Just add data, and a magical unicorn pops out, bringing transformational business process and rainbows everywhere. Certainly there have been no shortage of grand visions to achieve exactly that, but most such attempts have been rather disappointing. The common conclusion is you can’t get there from here; implementation must necessarily be unique. Companies are snowflakes. But snowflakes bring customization, and ownership costs rise considerably, becoming an adoption barrier. This is especially true for the little snowflakes, also known as Small-to-Medium Business (SMB). What to do?
Tensions in the larger enterprise war between Product Lifecycle Management (PLM) and Enterprise Resource Planning (ERP) appear to be on the rise, and the latest battleground is apparently waving the Master Data Management (MDM) banner. The genesis of this conflict stems from the intersection of two different visions, PLM and ERP born from opposite ends of the enterprise. To understand how we got here, check out The Multi-Headed Dragon. Remember how I told you not to taunt happy-fun multi-headed dragon? Well, Multi-Headed Dragon is cranky. Increasingly, it looks like new battle lines are being drawn on the stage of a very old war. An important question to ask: will territory finally be ceded one way or another, or is this just another episode of Bill of Material (BOM) Groundhog Day? Integrations shall be shaken! BOMs shall be splintered! Migrate now to ruin, and the world’s ending! Continue reading
Big data. No, that’s not quite big enough. Let’s adjust the reverb on the mic a bit… ahem: BIG DATA-A-A-A-A. There’s quite a bit of buzz these days about Big Data -some might argue it’s just buzz- but there’s relevant substance to the concept and intriguing propositions for the future. But what exactly constitutes Big Data? Big Data is in some ways a transient side effect of the information age – as the amount of data we accumulate, process, transmit, and store exponentially grows, the totality of said data has exceeded our capacity to conventionally manage it. The great invention of the information age: the almighty Relational Database Management System (RDBMS) is ill equipped to contend with Big Data. Databases store structured relational information in a centralized repository or a highly ordered federation of repositories. As the size of the dataset expands, so too must the size and power behind the database. Naturally, once the data exceeds a certain amazing colossal size, the corresponding database required to conventionally manage such a dataset exceeds reasonable practicality. Furthermore, as the diversity of the data increases, and the speed at which it is collected accelerates, any measure of structure becomes impossible. Even our favorite positronic golden boy is left scratching his head in bewilderment. The technology and innovation surrounding Big Data is very much about solving this particular problem. So how is this going to help us reroute power from the phaser banks to the starboard cappuccino machine? More importantly, what does Big Data have in store for engineering and, more specifically, Product Lifecycle Management (PLM)?
Enterprise software, especially Product Lifecycle Management (PLM) Enterprise Resource Planning (ERP) is complex both from a functionality and integration perspective. Whether such software must necessarily be complex is a topic for another time. Success in any Enterprise software implementation often requires dedicated resources, careful planning, technical expertise, executive sponsorship, and a receptive culture, among other things. Sometimes the results of such efforts are transformational, producing both measurable and significant business benefit. In other instances, however, enterprise software implementation attempts can and do fail, due to a variety of possible factors. My last post regarding PLM Startups instigated a bit of unexpected controversy with regard to PLM failure. In case you’re not up to speed catch yourself up with the last post here: Why We Need More PLM Epic Fails. The point of controversy, surprisingly, is contention about whether PLM failure exists at all. Despite the fact that all other Enterprise software implementation is known to fail, is PLM somehow –perhaps magically- immune?
Quick, what’s the most assured certainty in startup land? Grossly irresponsible valuations? Nope. Grossly profitable exits? Try again. How about failure. Glorious, unbridled, OMGWTFBBQ failure. Innovation is an inherently risky affair; pioneering new concepts and ideas requires the willingness to both discard and overcome both mental and physical barriers. Dear reader, take careful heed of the universal wisdom of FakeGrimlock, legendary robot startup dinosaur. He advises that if you line up enough failures, they can become the collective foundation for a win. The cumulative knowledge gained from iterative experimentation manifests true innovation; you have to keep throwing yourself at the wall of impossible until impossible no longer exists. And the end result is… awesome. But what does this have to do with Product Lifecycle Management (PLM)? Currently, very little. And that’s the point.