Let us start with a discussion on something unrelated to Natural Language Processing (NLP). Your car. The automobile in your driveway (or garage) is not just a machine. It is a solution. It is a solution to your needs. Depending on where you are in Maslow's hierarchy, those needs could range from getting from point A to point B to having the most hi-tech navigation and drive assist technologies. Also, most of us associate these machines parked in our driveways with just one brand. But we are very well aware that most of the parts used to assemble the automobile were manufactured by hundreds or thousands of suppliers across the globe and assembled into the current form by the brand you associate your car with. So, where are we getting at with this example? Well, we will get back to this example later in the article.

Tapping the Untapped Opportunities in Data and Analytics

Fortune Business Insights estimates that the global big data analytics market will reach $655.5 billion by 2029, at a CAGR of 13.4% from 2022 to 2029. I believe the opportunity is much larger. These estimates are based on categories of big data analytics solutions that already exist. A huge opportunity exists in creating new categories. But the hard fact is, it is becoming increasingly difficult for one solution provider to create these new categories by themselves. In an article, Conversational AI in Inventory Planning, published on June 18th, 2022, we highlighted, with an example, how powerful Natural Language Processing (NLP) solutions can be. This acquisition announcement in July 2022 essentially validated our hypothesis. Now let us pause here and return to the opening example of viewing automobiles as a solution. To you, your car is one single comprehensive solution, but considerable work has been done behind the scenes to get it to a dealership in that form. That gist of that work is various components, from multiple sources, coming together to complete the solution. The same holds for data and analytics solutions. It is becoming increasingly difficult for a single solution provider to cater to every need of its current and potential customer base, specifically when the customer base is as large as SAP's. You can keep developing new solutions for eternity, or make smart acquisitions, leveraging the rich startup and technology landscape. This acquisition was an example of later. NLP, in my opinion, in many application scenarios, is becoming a mature technology, and hence now is the right time to leverage it. There is no doubt that this acquisition will enhance SAP's offerings in many ways . Opportunities to couple it across modules, product lines and offerings is immense. However, an unchartered opportunity still remains. The opportunity to create a new category in data and analytics by itself- the moola beyond the $655.5 billion mentioned above.

Big Technology Companies Have the Capability to Create New Categories of Data and Analytics Solutions

This acquisition, in tandem with SAP's BTP platform and other offerings, can help build some rich and new capabilities. The example in the article shared above was supply chain related, but opportunities span industries. SAP has been a treasure trove of data for decades with almost two-third of business transactions going through its systems. SAP is now capitalizing on the fact to evolve its offerings. It is now helping companies that leverage its products with solutions to transform, using a portfolio of tools embedded in a platform. But the fact is that NLP enabled augmented solutions is already a category of data and analytics solutions. While SAP can certainly grab a significant piece of the pie in this category if it plays it right, this acquisition still does not create new niche categories of data and analytics. Going back to the car example again- there is an opportunity to "assemble" few additional products to create entirely new category of data and analytics. So while this acquisition allows SAP to expand its presence in the existing opportunities, this big void is still open to combining NLP with some other technologies that are either nearing maturity or emerging, and then embedding them within BTP. There are many opportunities and possibilities and acquisition candidates exist in the market for big technology companies like SAP to create new categories in data and analytics market.

Parting Thoughts

In many of my articles on the growth of the cloud, I have emphasized that while all key players will continue to grow during this decade, due to the exponential growth in the cloud arena in general, the eventual thrives will be the ones who can offer their offerings as tools to build innovative solutions. The same goes for the extremely competitive data and analytics space. While the market is booming, there is an opportunity to cash into this boom for sure in existing categories of data and analytics. But the eventual winners will be the ones who can carve new and unchartered space in this area. And this process has to be continuous, as new technologies emerge. And this is not an difficult endeavor for big technology companies that have the muscle and resources. You can define a methodology or framework to scout for possible matches with your existing portfolio within the innovation ecosystem. And then, that same framework can be used for continuous monitoring of prospective acquisition targets.