INDONESIA-based startup Datanest is a ‘Data Science-as-a-Service’ platform that helps companies analyse data using machine learning and Artificial Intelligence (AI).
The startup was founded in September 2017 by chief executive officer Manggala Ratulangie and chief business officer Thibaud Plaquet, who both have experience with companies that utilise and collect data in massive amounts.
“These companies usually keep data in silo-based technology (any management system that is unable to operate with any other system, meaning it’s closed off from other systems), which makes data mostly underutilised.
“In order to help companies, leverage on their data, and to provide valuable insights to improve their processes and increase their profitability, we created Datanest,” says Plaquet.
Initially targeting the fintech industry, in January 2018, Datanest launched a fintech aggregator platform, misterpinjaman.com that allows people to search for, compare, and apply for loans.
“The fintech industry was already collecting data and willing to use it. Then, we added the retail industry, which is a very exciting industry where there are a lot of opportunities in terms of AI and machine learning applications.”
At the end of the day, Datanest can work with any company that is already collecting data and is willing to leverage it, including SMEs.
Plaquet has seen growth in the number of visitors since the launch of misterpinjaman.com and decided to develop and integrate a new “bank scoring” feature on the platform which is still in its trial phase.
“We have also launched another platform called misterinvest.com that allows people to search, compare and apply for an investment or saving plan.”
Plaquet says that Datanest’s engine is built in-house, with a team of 12 mostly with data scientists to create models, data engineers to prepare data to be processed, and system architects to optimise system architecture, and developers.
“We are technologically agnostic, meaning that our engine architecture can be used on multiple cloud solutions such as Amazon Web Service (AWS) and Google cloud or even on premise, in order to adapt ourselves to our customer’s infrastructure.”
Datanest’s clients are mostly from the fintech industry and use behavioural scoring features. The startup is working on acquiring clients in the retail sector.
“Our strategy to acquire customers, is by offering them Data-Science as a Service. We are an agile, reliable, and affordable solution that can be easily and quickly deployed.”
Plaquet adds that machine learning is a subset of AI that is really powerful when it comes to leveraging on big data usage by identifying new trends, detecting abnormal behaviour, and categorising or predicting.
“When a company is collecting massive amounts of data, it becomes more difficult to be able to use that data in a meaningful way.”
He aims to build long term relationships with customers and provide “free” data exploration for new clients to figure out suitable infrastructure.
“Data science is not an obscure kind of magic, and we do not want to provide unuseful technology to our customers.”
Big data and AI in Indonesia
Plaquet feels that Indonesia is a huge country with a growing economy, where big data and AI are powerful technologies that can be used to assist in development and could help a lot of people and companies to improve their businesses and lives.
“There are some companies in the field trying to disrupt the industry and we wish there will be many more in the future.”
He says that Datanest’s competitors are consultant firm, which can be quite expensive, or focus exclusively online.
“We believe that our business model is affordable even for smaller companies and can be scaled based on usage.”
Building a company
Starting Datanest was full of challenges for Plaquet. From building a strong team to developing the right technology and finding as well as handling customers took time and a great deal of effort.
“In order overcome the challenges, we built our team based on respect and collaboration. For our customers, we are building our relationship based on trust.
“We are also surrounding ourselves with mentors and advisors who are successful in their fields. These challenges are exciting and make this adventure a worthy experience.”
Datanest closed its seed round funding (undisclosed investors) in February 2018, allowing them to be sustainable for the rest of the year and has plans to fundraise in early 2019.
Its next target is to productise some of its machine learning models in order to allow more companies to use it at a lower cost.
“In order to achieve that, we have to identify applications that can be generalised and build models that can keep good accuracy for customers.”
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