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: Code-free data science
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: Use the ‘right’ data
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.
Quantexa: Connect all the dots
Context is crucial to making high-quality decisions based on data. Quantexa knows this and is focused on contextual decision intelligence. Quantexa focuses primarily on fighting fraud and risk in the financial sector. Its major clients are banks and financial organizations that deal with risk on a day-to-day basis. These organizations invest heavily in security and risk operations. If Quantexa can make a case that its approach is better in some way than other similar solutions available today, financial organizations would be only happy to get on board.
Quantexa specializes in entity resolution, which is about finding the connections between various data points. In doing so, it enables its customer organizations to better investigate issues like money laundering and sex trafficking by improving your know-your-customer (KYC) processes.
Quantexa brings deeper insight into customers by helping organizations build customer profiles. These profiles are based on behavioral patterns. With its profiling, Quantexa is able to quicken customer onboarding. Once onboarded, this customer profile is always being enriched and is made available for decision making across the entire customer lifecycle.
In today’s remote and yet digitally connected world, crime is moving online. According to the UN, there’s been a 350 percent increase in phishing websites since the start of the pandemic. Quantexa is only more relevant in a world where cybercrime is growing.
Quantexa has raised $65 million in Series C funding.
K2View: Databases are now micro
Unlike the previous organizations mentioned here that focus on providing tools for analysts, K2View looks at the relationship between databases and applications. It is more upstream than downstream in the entire data lifecycle.
K2View’s unique take on databases is to break down any database into micro-databases that are loosely coupled and modular. K2View creates a data fabric that takes into account all the complexity inherent in data.
This shift to micro-databases follows the broader trend in the world of cloud computing, where applications and infrastructure are decomposed from larger monoliths to smaller microservices. While this shift has been more pronounced in the infrastructure and applications layers, it has lagged behind when it comes to the data layer. Indeed, data is the hardest part of microservices.
Micro-databases are more secure than traditional databases as they are secured at every level, not just with a peripheral firewall. Attackers would need to hack at every level to gain any substantial access to the data.
K2View’s primary product is Fabric, which connects data sources to destinations. It also offers two other products ADI (advanced data integration) and TDM (test data management). The former is a data integration service, while the latter enables testing and QA for DevOps teams.
The data needs of applications and teams are ever-changing. K2View enables real-time data for applications in a way that adapts to these needs. This agility is the biggest need for organizations, and it’s what K2View promises.
K2View has recently raised $28 million in funding, a testament to its novel approach to data management.
Data science for data management: Still at the beginning
Each of the vendors mentioned above has a unique story and take on data management. However, there is a common thread of not trying to completely reinvent the wheel, but to quicken existing data operations within organizations. This is the view that organizations looking to adopt them should take as well. What matters is not having the shiniest tool, but about making maximum use of all the data you have and driving maximum efficiency out of your existing data processes. From that point of view, each of these organizations has something meaningful to contribute to the broader discussion around data. They’re worth keeping an eye on as they continue to grow and expand with the fresh funding they’ve each received.
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