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Growth of Data Market by Srecko Dzeko, General Partner at Flash Ventures

In our previous article we elaborated on how the data market landscape is progressing at the moment. You can read more about it here. We also wanted to hear how VCs are looking at the growth of the market, so we turned to our friend Srecko Dzeko, General Partner at Flash Ventures for insights.

Here is what he had to say 👇

What do VCs look as a competitive advantage between data companies? What makes a strong data player stand out?

There is a famous quote by Tolstoy that says:

“All happy families are all alike, but every unhappy family is unhappy in its own way.”

Same as families, startups rely on a lot of things to be able to really stand out in a growing market. What we see from the companies that are progressing is that they have a deep understanding of not only their customer but also of their buyer, as well as at which stage the product becomes relevant for them.

To put it in perspective, a lot of companies in the data space origin from one or more technical co-founders solving problems they experienced themselves. However, it is a very different thing to then find and execute a product in a scalable way of distribution.

Some companies do this by building products whose ROI can be expressed very easily and effectively in dollar amounts, others are extremely well in creating communities that drive bottom-up adoption from inside an organization.

Beyond this, there are some hygiene-factors which need to be fulfilled. For example, if you build data tools for developers the product has to be absolutely superb, inter-operability is a given and compared to other verticals, the actual product performance can be a real gamechanger. This can be seen by the increasing popularity of Firebolt as a speedier alternative to Snowflake. If you sell to larger clients, security and ownership of data are still major decision factors for purchases.

What are the key trends data companies need to address?

What we see is that the market is splitting into two competing forces. The first is the continuing verticalization of the data stack into highly specialized providers with the progressive nature of the category itself — e.g., warehousing, transformation, visualization, or reliability. The other one is then companies attempting to rebundle these services into more comprehensive solutions.

We here at Flash still very much believe in the unbundling of the data stack, also because the sophistication needed to address these problems at scale is and probably will stay very high. At the same time, companies will need to increasingly weigh going deeper vs. broader in their product offering, as well as confront themselves what is needed to sustain a long-term competitive advantage against peers branching into their field.

One clear trend we recognize here is definitely the transition from data tools as pure “visual dashboards” to becoming deeply embedded into the operational decisions of companies. As an example, modern data companies are not only there to analyse data and infer decisions for human decision making, but they also automatically trigger operational consequences. Some even measure the impact of these decisions back on the origin stack. What is then achieved is an appreciating data asset: one that makes the operation of the company better and better with every decision.

What is the future of data platforms and in which way are companies going to restructure themselves to be fully data driven?

Being data-driven essentially means that decisions are inferred on the base of actual data. For this, our hypothesis is that two things will happen:

Firstly, no-code and low-code as a solution will continue to spread. Most people who take decisions in companies are non-technical, and in order for them to become data-driven, they will need to embrace the toolkit that will enable them to derive insights without the help of others. The second is that data engineering will become as much, or potentially even more important than data science. While the latter has become a buzzword in the past years, the increasing realization after companies hired a generation of data scientists is that only a few have a dataset that is homogenous enough to utilize them effectively.

If you are Facebook having tons of data scientists is amazing, because the data you use is mostly collected by yourself and you fully control it. For most other companies however, it’s about building infrastructure from a variety of tools and sources and matching them along the way. One big challenge in the forthcoming years to become data-driven will be building exactly these pipelines, also because there is a big lack of data engineers wherever you look.

How is data analytics going to be restructured given that data tracking is becoming more restrictive in Europe? How are companies going to continue benefiting from elaborate data analytics without needing to shadow their customers?

If you look at which companies’ business model is impacted by more restrictive data privacy regulations, it is foremost the big B2C data collectors that are active in advertising. Regimes like the GDPR, but also private initiatives from companies like Apple with their ATT, can have an insane influence here. My personal belief — and wish is — that the systematic “shadowing” of users without their consent to sell their data is not a sustainable business model. At the same time, this only impacts probably 5% of all use cases for data analytics. There is so much more value that data can play in every part of an organisation — from Product to Sales to HR to Compliance.

Thus, we are quite optimistic about the future of data analytics. It will become more omnipresent in every aspect of a company’s operations, as well as more pervasive on the impact on the decision-making of its leaders.

We hope you enjoyed this article. If you want to read more about how to deal with your Data follow us here on medium, or on Twitter and LinkedIn. 💜

If you’re interested in Datamin’s product, reach out directly to our team, we are here for you! ➡ DATAMIN



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