How is the technology landscape changing and how is it impacting organizations globally?
Speaking of technology and more specifically about cloud, we see a tremendous rise of platform-based services and a growing attention toward serverless environments. This serverless approach does offer considerable benefits in terms of security, scalability, and agility. Projects which previously required companies to invest around 10 or more person-years could be implemented in just three person-months, if done through a serverless environment. This is made possible due to the massive investments that players like AWS have put into their platforms. But going serverless is not an easy task. It requires significant groundwork like re-architecting a business’s entire infrastructure. Additionally, there’s a widespread concern amongst companies about getting tied to a given vendor. To avoid such vendor lock-ins, companies either refuse to go serverless or use something like Kubernetes on AWS. Undoubtedly the latter is a rewarding approach, but it doesn’t necessarily provide the numerous benefits that going serverless can offer.
Finally, we notice an evolving legislative structure such as the GDPR and an increase in overall privacy sensitivity across the world. Now, for example, if a company is doing business in the European Union, the data collected from there can’t leave the union. I think what that means for the cloud is that it has to start embracing cultural changes, which means respecting borders, differences, and privacy cultures around the world. All this creates a really interesting administrative and technical layer in the cloud which we haven’t had until now.
Whatever be the case, our first step every time is to precisely understand the business objective of a client, which may be something the client is trying to solve or achieve
To be able to do all the above, you need skilled data engineers and scientists who can build you the right algorithms and the right data infrastructure. However, everybody knows good talent is really difficult to hire today. You are lucky if you do manage to hire them, but a few years later, when they truly begin to understand your infrastructure and data, you lose them because they are in high demand.
We have a team of highly skilled data scientists, engineers, and strategists, the likes of whom everybody, even big corporations like Google and Facebook, want to hire. With these people, we provide data consulting and engineering services to companies globally, which can be a ten-person start-up or a Fortune 500 company. We probably don’t fit in scenarios wherein someone needs help in buying an ERP platform. What we do is vertically integrate such systems by building a data pipeline so that the data can flow through them seamlessly to give organizations an unobstructed visibility into their business environment.
Can you give us a more detailed picture of Dativa’s services?
One common thing companies come to us seeking assistance for is data monetization; sometimes they may have built a new dataset, but it might not be serving them well enough. Whatever be the case, our first step every time is to precisely understand the business objective of a client, which may be something the client is trying to solve or achieve. And that step to gain an end-to-end understanding of clients’ business objective differentiates us in the market. We look at their data granularly, and then we identify what necessary steps like data cleansing or validation need to be adopted; we evaluate whether they need artificial intelligence, machine learning, or simple predictive analytics would suffice. Once our strategists get a clear picture of their requirements, our data scientists create the appropriate model or algorithm, and then our data engineers build the data infrastructure, which we almost exclusively do on AWS these days. We are quite unique in terms of being an engineering vendor. We look at the end-to-end requirement, and we deliver in quick, small steps to meet those requirements so businesses can get a good return on what they want; we get this edge by having a wholesome team of data strategy, data science, and data engineering experts in the same organization.
Dativa is not one of those companies that will simply ask about requirements and build a solution around them. We will align ourselves with a given business objective, and we will deliver something that we are entirely sure, will work and meet that objective.
What’s next for Dativa?
Cloud technology is a great enabler; companies don’t need to worry about managing their infrastructure. Nonetheless, that doesn’t put operations people out of the equation. Productizing data operations as a service is important for us, and we are moving in that direction.
Working with different customers, we see that the legacy data they have are often quite messy. We are also investing in a product that can clean such data in an automated manner, and do some additional things like anonymizing or applying heuristics to the data. Regarding operational expansions, we are considering entering into some new markets in the next 12 months.