4 breakthrough data science startups transforming data management
Data is the oil that powers modern businesses. However, there has been a transition from traditional data management to modern data science approaches that has been underway for over a decade now. While building data science teams internally is one option, the more interesting option is to leverage ready-made data tools that greatly quicken data operations. Let’s look at four such tools that are innovating in the space of data science and data analytics. They’ve all been funded recently and are the hottest players in the space today.
Mode Analytics is one of the fastest-growing data science startups in recent years. It enables organizations to connect their cloud or on-prem databases to the Mode platform, and then analyze the data in these databases to glean insights. Examples of databases that Mode connects to include AWS Redshift, Snowflake, Google BigQuery, and traditional SQL databases.
Mode allows a single analyst to do the job of an entire team and thus quicken data operations. With use cases spanning all departments within the organization such as marketing, finance, and HR, Mode helps unify business intelligence across an organization.
Mode builds on top of existing data tools and frameworks like R, Python, and SQL — and in doing so, looks to improve existing data operations. They offer notebooks for R and Python and a cloud-based SQL editor. The point of all this is to deliver a code-free experience for data scientists.
The company is gaining traction with a reported 600 paying customer organizations and 52 percent of Fortune 500 companies already on board. Additionally, it has 2,000 organizations as free users. Mode has recently raised $33 million in Series D funding.
Explorium, as the name suggests, is out to solve challenges around data exploration. We’ve moved on from the Big Data story of the past decade. Today, it’s a given that companies have a lot of data, where the game has moved onto now is the use of the “right” data. The vendor that delivers the quickest time to insight is bound to win this round. Explorium wants to be the go-to solution in this space.
Explorium knows that the quality and quantity of data matter when doing data science. This is why it equips data scientists to pull in data from sources, both internal and external, to an organization. This process of data discovery is geared towards finding the “right” data. Once ingested, Explorium analyzes the data and makes it ready for analysis.
Explorium doesn’t just report on historical and real-time data, but rather puts focus on predictive analytics. It aims to leverage AI and machine learning to draw patterns and make accurate predictions about what organizations should expect. It offers solutions in the domain of lead scoring, demand forecasting, risk modeling, and customer lifetime value. These are strategic business objectives that are typically handled by experienced business intelligence professionals and data scientists. However, there is a severe talent crunch for exceptional data scientists, and the process of data science is complex and prolonged. Explorium looks to change this situation and become the data scientist’s tool of choice. Its goal is to make organization not just data-driven, but data science-driven.
All the big cloud vendors are making big bets in this space. IBM Watson, Microsoft Azure Machine Learning, AWS SageMaker, and Google Cloud AI Platform are all out to grab a piece of this pie. Explorium recently raised $31 million in funding to further its expansion.
Context is crucial to making high-quality decisions based on data. Quantexa knows this and is focused on contextual decision intelligence.
