Searching for a business model

The aforementioned examples show the key potential, role and added value of data in our society. Hence, one should address every impediment slowing it down. But what exactly is holding back the data economy from the next big upturn?

In our view, the primary enabler would be making data easily exploitable by any IT systems. Data standardisation is the #1 problem of the data economy and yet nobody is talking about it.

As we define it within Scalia, standardisation has 2 aspects:

Having a way to exchange data rapidly and efficiently from one system to another or from one organisation to an another is crucial. The more systems and users can exploit data the more it can be leveraged. And yet, most sectors and industries haven’t agreed on a standardisation protocole because (i) it’s complicated and (ii) it requires for people to see the bigger picture.

History shows that the best way to address a problem is to find an economic benefit to it. But which business model should one go for in order to solve this issue efficiently? Below, we will review 3 potentiel business models and analyze if they are appropriate.

In a ‘many-to-many’ setting where there are multiple actors on the demand and supply sides, a common tech solution would be to set up marketplaces according to overarching data typologies. The data owners and consumers can then trade between them subject to market forces. As such, we could envision a virtual marketplace for price history data of a given product, insurance statistics or investment data. An obvious prerequisite for this market to work would be to impose a data standard on all its players.

2. Data aggregation

In the aforementioned examples, the main players simply resell public data which is already accessible to anyone who has the patience to find it. As such, their added value does not lie in the data they provide but in the way they consolidate and structure public information. By becoming market leaders, such services become the de facto standard towards which everyone converges.

Even if this business model has proven to be applicable, is it the most suitable for every sectors?

3. Data customization

A third option has appeared more recently. Some sectors don’t require consolidation as a service. Instead, they are hungry for customization. Sure they need to share data around efficiently but they also need to display it in a unique way to remain consistent with their branding. This is especially true for B2C sectors where content marketing and SEO have become key components for success.

Any business who needs to display products online have come to realize that collecting content and displaying it in a unique and yet standardized way is absolutely critical for ranking and searching purposes.

It turns out that there are already some middlewares dedicated to streamlining raw inputs into structured data where one entity can set up its own transformation rules. They are called ETLs which stands for Extract, Transform and Load. Unfortunately, such middlewares are complex (and expensive) to set up and maintain and they aim for perfect transformation. A solution to off set the complexity is by combining deeptech suggestions and human training. As more organizations would use such ETL, through the effect of machine learning and the power of data network effect, suggestions will get smarter over time.

So far, data standardisation has not been popular because it is not a glamorous business. Yet at Scalia, we are convinced it is one of the greatest opportunities for our society and it also represents a huge market.

Unoriginally, it all starts by finding a viable business model which could differ from one industry to another.

After months of discussions with potential customers, we’ve decided to explore a whole new approach in order to serve poor data standards industries. By mixing a long-standing yet robust tech - ETL, an advanced web UX and deeptech, we are convinced that our platform will boost some sectors to the forefront of standardisation.

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