About Us
We have helped companies all sizes develop business strategies and build proprietary technologies and data solutions custom to their needs. We've developed a practical and impactful understanding of how to leverage data and technology effectively for our clients.
Since our inception, we've recognized a gap in data analytics: technical expertise often lacks business acumen. At MOKA, we combine deep data analysis with a solid grasp of your business needs. This blend of skills ensures we act with speed and strong focus on the business impact while being technology agnostic.
Our History
Our Driving Beliefs
There is a lot of noise in the space
The proliferation of SaaS solutions a couple of years ago and the broad adoption of AI/AGI over last few months is putting a lot of pressure on board rooms. It has led many companies into forced digitalization projects which get abandoned after a few months. The space is noisy with extreme fragmentation of tech solution suppliers. We advise clients to focus on understanding deeply their use cases, fixing their basics, before adventuring on adopting new technologies.
Data infrastructure is a pre-requisite
Most companies that are successfully leveraging analytics have already built solid and safe data architecture, moving way from on-premise only to internal cloud architecture, with modern data infrastructure and automation of data capabilities. The analytics infrastructure should be insulated from IT operations to move at much higher speed.
Technology can’t solve a problem the business can’t articulate clearly
While certain analytics use cases do not need the business to be involved, the biggest value unlocks we have witnessed are when complex business problems are well designed. Technically competent engineers often fail to understand the nuances of a specific business problem. Custom and practical solutions rely as much on effective design thinking as it does on engineering execution.
Smart data often wins over Big data
There is a common bias among business decision makers that more data will lead to more accurate prediction or better decision making. While this holds some value, it is mostly true when training large ML/AI models for specific use cases. Most analytics use cases in the enterprise could be solved better and quicker with smarter, more contextualized and informed data as opposed to more data points and added complexity.
Technology is getting commoditized, but your data and business constraints are unique
A lot of executives confuse developing advanced analytics with becoming a technology company. The competitive edge is not in internalizing the R&D around technology but rather leveraging your unique data to feed the right existing technology capabilities to solve your unique problems.
