Build vs Buy AI-driven Analytics products: A game with no winners – or is it?

Build vs Buy

Author: Kishor Gummaraju

Data fuels digital transformations. These days there’s a start-up on every corner touting AI and Big Data solutions. Large product companies are expanding their offerings to include insight solutions. Every consulting company is developing its own product to increase client stickiness. Product sales and subscription revenue attract preposterously high valuations, a mouth-watering prospect indeed for any business, regardless of size.

It goes without saying that companies offering these products have a lot to gain. But is it the right business decision to invest in AI / Advanced Analytics / Big Data products for your company? If your industry rivals get on the bandwagon, what happens to your competitive advantage? If data is the new gold, will the small clause about Intellectual Property (IP), lurking unobtrusively in your contract, give away the keys to your kingdom?

 

Off-the-shelf products and the lure of omniscience

If data is ore, insights are the precious metal within and every modern organization is sitting on a fortune. Most organizations recognize the strategic value of their data and are building in-house analytics teams.

Uncovering value at speed is vital to competitive advantage and teams need time to scale, so every business function is in search of a solution that gets quick results.

It’s an archetype of cartoons, cinema, and TV: serious-looking men and women key in the problem, and after some completely unnecessary beeps and boops, the omniscient supercomputer spits out the answer. It’s tantalizing to think of your company owning a ready-to-use AI-driven platform that magically solves all your business problems.

Whether it’s forecasting; revenue optimization; strategic pricing; supplier analytics; or market mix modeling, product companies claim they’ve incorporated other players’ learnings into their own offerings, saving you, the buyer, time you would otherwise have spent in experimentation.

 

What’s wrong with this picture?

Buying off the shelf sounds attractively simple, but as is so often the case, the devil is in the details:

– A standardized product isn’t a particularly effective strategic differentiator, because your competitors can buy it too.
– Each product company has its own product roadmap, and the differentiating capabilities your business is looking for maybe quite far down the road. You may end up waiting a long time for a solution that really meets your needs – which would themselves have evolved while you were waiting.
– Let’s say your service provider builds the features you want into their product, and let’s also say those features are built just the way you want them: with your business experience, use cases, and expertise feeding the algorithms. This is great until you realize that you’ve shared all your tacit knowledge to improve the solution, but the fine print of your contract says it’s your service provider who owns the IP – which is now available to all your competitors!
– Most niche product companies are built on the promise of a high valuation that will ultimately attract the acquisitive attention of a larger player. This invariably results in the dissolution of the acquired entity and a change in priorities, or worse, the retirement of the product you pinned your hopes on.
– Most products solve problems in silos using data at hand today. In the future, there may be new data sources, business models, and technologies available. Your business may need cross-functional perspectives that a readymade solution can’t support.

Consider these scenarios from the CPG industry:

– You procured a forecasting solution all ready to use. Your cloud provider has developed a new algorithm that looks promising, but you’re forced to wait for your solution provider to upgrade so you can try it.
– You bought one solution for trade optimization and another for market mix modeling. Now your company wants to optimize spending across both, but the platforms can’t cross-pollinate.
– You bought a tool for strategic pricing which included a volume transfer matrix. You are rationalizing SKUs and need to estimate demand transferability. You will either need to develop this capability from scratch or pay top dollar to the provider because the tool itself is a black box.

In a nutshell, buying ready-to-use means committing your company to a suboptimal solution that gives you no sustained advantage and that will only drift further from your needs over time.

 

So bespoke has to be better, right?

Not quite

Building a customized AI-driven solution from the ground up is fraught with its own risks:

– Data acquisition, quality, and harmonization are bigger challenges – and far more common – than you would think.
– You will have to experiment with, create, and train the models: all necessary but time-consuming activities that delay value realization from your data.
– You may not have the right talent to develop and scale your solution.

This sounds like a classic Build vs. Buy problem!

 

The times, they are a-changin’

In the ‘traditional’ software world, most large companies implementing ERP solutions gravitated towards products that met their transactional needs. This made sense, because:

– There’s limited differentiation in running transactional processes.
– Core processes don’t change drastically over time.
– The huge maintenance cost precludes the development of custom solutions.

Insight solutions are different. The underlying technologies are evolving so fast that solutions need to change all the time just to keep up.

For example, we developed a forecasting solution originally using an ARIMAX model. One year later, there was Facebook Prophet and then there were Deep State Models which are more accurate and easy to maintain though not explainable. The solution needed to evolve to keep up with the available options.

Your business’s competitive advantage comes from continually evolving and improving the performance of your insight models. You can hire service providers to do this for you or employ data scientists yourself, as do a growing number of companies these days.

 

Open IP: the winning balance

We believe that if insight is a competitive differentiator, a business should never tie itself down with a product whose IP is a black box. Your company’s insight solution should be maintainable, expandable, and upgradable independent of the service provider.

That’s why Tiger Analytics takes an Open IP approach: we invest in developing business solutions and accelerators for clients like you where you keep access to the IP and source code.

By complementing your business expertise, this unique approach reduces the time to value by leveraging our IP and prowess with data to get your insight solution operational and scaled up much quicker.

Proprietary analytics products and solutions can never fully give your business the lead it needs. By adopting an Open IP approach, your company will retain the ability to add competitive differentiation without losing your competitive edge.

What do you think of the Open IP approach? Is Build vs. Buy really a game with no winners? Is there another way?

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