SAS and the Theory of Delayed Agility

SAS and the Theory of Delayed Agility

Reading time: 4 mins

By Kumar Singh, Research Director, Automation & Analytics, SAPinsider


SAS refused the Broadcom offer

After all hype around Broadcom’s potential acquisition of SAS, we finally learned that SAS has refused the offer. And my take is that it was a wise move. Create a grid of Broadcom offerings and you will find that there wasn’t a great strategic fit. I mean you could fit it, but not in a great way.

Even if you are getting acquired, a company like SAS would like to keep the brand name alive and thriving. Acquisition will be approved only when it leads to a synergy where the brand will get into a state better than where it is currently, eventually. That did not seemed like the case to me. And looks like folks at SAS thought the same. But this failed acquisition bid is not what the article is going to be focused on. It is about theory of delayed agility and how it impacted SAS in the rapidly evolving world of business analytics.

What the heck is “Theory of Delayed Agility”

Before you start looking for this on the web, let me clarify that this is a theory that I am postulating so you may not find anything about it online. The theory is simple though. The theory is:

Companies that have been technology leaders in their category for a long time, develop a momentum in a certain direction in terms of strategy. The inertia from that momentum becomes so strong in the haydays of the company that  at a certain point, despite recognizing that the direction of the momentum needs to change, the company does not have the agility to course correct in time.

Let me explain this using the example of SAS. SAS was once a leader in the world of analytics software (It still is a player to reckon with, though not considered top of the line when you think about the new world of data science). With strong dominance, fewer competitors and large market share, came the momentum. Expanding within the core competency. Features and functionalities evolved around the core offering. But the fact was, at its core, SAS was a programming languages, that aided statistical analytics.

Then came the open source wave !

And with open source programs, “programs as products” lost relevance. Solutions became products. And the capabilities of those solutions also extended beyond the conventional areas SAS played around in (statistical analytics). SAS did realize that it was time to change the direction of momentum. But the scale of momentum was so massive that theory of delayed agility came into play. By the time SAS started branding itself as a solutions company it was alreaday too late. And by then no amount of being ranked as leader or innovator in rankings was enough to actually establish it as a true leader. It was already very deeply imprinted in our minds as a statistical analytics software product.

Just a sidebar on rankings and categorization of software solution companies. In 1990s, you would probably refer a ranking to understand which solution is best in a category. Today, with information being free, available in vast quantities, confident new solution providers offering free trials, hundreds of new solutions being launched every month in new category- being branded a leaders or innovator in a ranking actually does not matter. And SAS is not the only example. I can list five additional software companies easily that will get accquired this decade, solely because they are not as relevant today as they were few years ago, but I see them listed in studies as leaders, innovators etc.

To be fair to SAP, not that they did not try. I will give it more points than Mathwork for aligning itself to the new reality. Its just that it could not do it fast enough to be as relevant in the age of data science, as it was a decade ago (despite what the rankings or categorization say). Any decently trained data science manager will not use SAS as the tip of the spear in their data science war. But the good news for SAS is that it has developed some good solution capabilities in true data science space, even though it is one of the many now in the domain. Theory of delayed agility got it!

Last, but not the least- the mental block on acquisitions

All that does not mean SAS is not a good grab. It is but not for every organization. Broadcom was never a good alignment though. However, one aspect that came to my mind when following this was a mental block that we have as far as acquistions go.

A decade or two ago, a non-tech company accquiring a software company was an almost impossible and absurd proposition. Even if  you keep SAS out of this discussion, things are totally different today. What if as a non-tech company, like CPG, retail or manufacturing, you accquire a software company , that aligned with your unique business and operating nuances? Build certain proprietary solutions and platforms leveraging the software that then no one can copy ?

Afterall, your operations will eventually be driven by technology and building a plethora of competitive differentiator solutions leveraging a software that no one else can use- doesn’t that sound nice ? I can think of many possible pairs of non-tech companies and small and medium size software companies that can be terrific. So I believe that in today’s age of digital explosion, we need to eliminate one more mental constrain- this one in the area of acquisitions.


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