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Bring Life To Your Data

The Data Market


The creation and consumption of data is accelerating at an incredible pace. This ongoing growth in data is fueling big investments in strategies and technologies that will empower enterprises to harness data-driven insights to uncover new innovations. Whether it is discovering new drugs to solve cancer, creating a more engaging shopping experience that will delight customers, or uncovering ways to improve supply chain efficiencies —enterprises are turning to data science and AI as a means to tap into the immense value embedded in the mountains of data they are generating.

94 %

of organizations use multiple sources for their data.



hours per week equivalent spent by advanced spreadsheet users doing repetitive manual analytics tasks in spreadsheets.

60 %+

have FIVE or more different data sources.



$56 B

Worldwide Big Data and Analytics Software Market (2020)


$10 B

21 mm Spreadsheet Users who work on advanced data analytics in 2020


Finance analytics, fraud detection , forecasting
IT infrastructure & Web App optimization
Legal discovery and document archiving
Social network analysis
Traffic flow optimization
Recommendation engines
Churn analysis
Location based tracking & services
Oil & Gas exploration
Weather forecasting for business planning
Healthcare outcomes
Personalized Insurance
Cyber Security
Life sciences research
Advertising analysis
Equipment monitoring
Pricing Analysis
Smart meter monitoring

Data Challenges

unnamed (3)


Infrastructure Complexity
The move to the cloud is fast becoming a primary objective for businesses looking to reduce costs and create competitive differentiation. Part of the challenge associated with this inexorable shift is the complexity that surrounds setting up and maintaining a big data infrastructure. The explosion in data growth pushes organizations to move faster with infrastructure investments that can harness and derive value from this data. But doing so ultimately creates an over-reliance on DevOps teams to do the heavy lifting. For companies that don’t have dedicated DevOps teams to help with these infrastructure issues, the responsibility often falls on the data scientists to fend for themselves. This creates an environment where they are not able to focus on their core responsibilities because they are spending so much time configuring and setting up infrastructure.


Disparate Technologies

Companies are trying to use a myriad of technologies to achieve their goals of a more data driven business. Open source projects such as Apache Spark™, Hive, Presto, Kafka, MapReduce, and Impala, offer the promise of a competitive advantage, but also come with management complexity and unexpected costs. Relying on disparate technologies can be incredibly challenging as they all follow different release cycles, lack institutional support
mechanisms, and have varying performance deliverables. Additionally, the skills to integrate these technologies are in short supply and can jeopardize innovation in order to maintain a stitched together infrastructure. Again, this responsibility can fall on the data scientists which further slows their progress.


Siloed teams

The productivity of the team structured across a data organization can be severely impacted without a seamless and dependable big data platform. It’s very difficult for the traditionally siloed functional roles of data scientist, data
engineer, and business user to achieve any synergy and work together both within a function and across teams to explore data and solve business problems. By viewing data through separate lenses, collaboration is very difficult, trust in the analytics can be misplaced, and speed of innovation is


Data exploration at scale

Exploring data at scale can be difficult and costly. Most organizations rely on single threaded tools to perform data exploration. The limitations of this approach are directly associated with the amount of memory on the data scientist’s machine, impacting their ability to scale. As a result, they are often forced to train models against small samples of data which can result in less accurate models.

Model training is resource intensive

Model training involves using the data to incrementally improve the model’s ability to solve a given problem. This process involves many steps including training the model, evaluating the results, tuning parameters to further optimize your model, and repeat. Training complex machine learning models against massive data sets can be very challenging in isolation without the ability to collaborate on models with peers. The more complex the models, the longer it will take to bring new capabilities to market.

Difficult to share insights

Part of the role of a data scientist is the need to share results with team members and stakeholders for input and decision making. The trick is sharing the insights in a way that resonates with non technical audiences. The inability to do so can hamper cross team collaboration and slow progress.



Data at rest

  • IoT Data Collections
  • Sensor, Social Data
  • New Data Stores
  • Tera, Peta, Exa Byte

Data in motion

  • Streaming Data
  • Real-time Events
  • Fast Response Times
  • High Throughput

Data forms

  • Structured Data
  • Machine Logs
  • Images, Video
  • Speech Text, Sensors

Data accuracy

  • Data Integrity
  • Data Consistency
  • Data Validation
  • Data Governance

The Solution


It’s no secret that better collaboration often leads to improved operational efficiency and productivity. With no shortage of data to sift through and the pressures of the business to build accurate models quickly, it’s important for data scientists to work effectively with other team members, colleagues across teams such as engineering, and stakeholders. Achieving a highly collaborative environment can positively impact team efficiency, productivity, and innovation resulting in delivering more models to production faster which can result in more revenue. As organizations continue to try to become more data driven, creating easier access and visibility into the data, models trained against the data, and insights uncovered within the data is critical.


With so many data challenges facing enterprises that act as a brake on innovation, distracting the organization from their core competencies and slowing time to market for new products and insights, a new approach needs to be considered. DASS offers an interactive workspace that takes traditional notebook environments to the next level. By integrating and streamlining the individual elements that comprise the analytics lifecycle, these teams can quickly access data, provision compute resources, and work together to build models, creating a culture of accelerated innovation.

Accelerate Innovation with Collaborative Data Science:
Increase the productivity of your data science team by 4-5x through collaboration and the democratization of data and insights.

Collaborative Workspace: Speed up iterative model building and tuning with interactive notebooks purpose-built to instill collaboration across teams.

Support for Multiple Programming Languages: Interactively query largescale data sets in R, Python, Scala, or SQL.

Built-in Visualizations: Visualize insights through a wide assortment of point-and-click visualizations. Or use powerful scriptable options like matplotlib, ggplot, and D3. • Highly Extensible: Make use of popular libraries within your notebook or job such as scikit-learn, nltkML, pandas, etc. A Unified Approach to Data Science Share Insights via Interactive Dashboards Share insights with your colleagues and customers,orlet them run interactive queries with Spark-powered dashboards.

One Click Publishing: Create shareable dashboards from notebooks with a single click. One notebook can be tailored into multiple dashboard views.

Continuous Updates: Publish dashboards and schedule the content to be updated continuously.

Parameterized Dashboards: Enable non-technical users to perform scenario analysis directly from published dashboards. • Dashboard Widgets: Input widgets allow you to parameterize your dashboards.



The Product




Feature DASS
Multi-language support Enables commands across Python, Scala, SQL or R – allowing users seamlessly to mix and match as needed.
Collaboration Designed for collaboration, DASS web based contains features such as comments, viewer log and history.
Live Sharing & Editing Real time collaboration among team members performing data modeling or analysis.
Revision History Simplifies version control by not having to create, save and manage model changes made during development.
Big Data Support Allows data scientists to leverage Big Data distributed processing engine to analyze large scale datasets and interactively run jobs.
No Vendor Lock-in Users can import and export notebooks in source format, allowing for seamless migration of source code (Scala, Python, R, SQL) in and out to an IDE of your choice.
Debugging Easier debugging capabilities for model jobs.
REST API Trigger or query externally via the DASS REST API for easy programmatic access.
Consulting Services Professionals provide advisory, development and modelling services


4 (1)



Use Cases/Case Studies


Government cuts 5 million property assessments fromdays to under 4 hours.


Retailer reduces time to run queries by 80% to optimize inventory

Utility avoids power failures by analyzing 10 PB of data in minutes


Stock Exchange cuts queries from 26 hours to 2 minutes on 2 PB of data

Hospital analyzes streaming vitals to intervene 24 hours earlier


Telco analyzesstreaming network data to reduce hardware costs by 90%


Ranging from retail banks, asset management companies, to capital market firms, it is imperative for financial services enterprises to develop effective business models that would equip them to combat the current & future challenges of the industry, as well as, improve services for customers.
We help our financial services partners extract and analyze massive amounts of disparate data in both structured and unstructured formats and turn it into actionable insights. We begin by identifying, strategizing and ultimately solving your most difficult and challenging business problems with our AI and ML data science platform.

Our AI and ML products are used by financial services to effectively:

  • Identify operational efficiencies.
  • Mitigate risk with early fraud detection flags.
  • Improve critical compliance processes and automatically identify weaknesses.
  • Enhance informed and data-driven investment decisions aligned with desired portfolio characteristics.


Healthcare data volume and complexity is growing exponentially creating a demand on healthcare across the full ecosystem. Considering the rising health care costs, vast patient population, and growing challenges, data analysis has become a prerequisite for healthcare providers. The industry is looking too clinical data analyses as solutions for triggering system-wide quality improvement, delivering better value for patients, reducing costs, enhancing the quality of care, and of course, saving lives.
We leverage our skills of machine learning and computer science to extract trusted and evidence-based analyses from multifarious data sets. Leveraging deep knowledge and analytical capabilities, our cross-sector teams strive to uncover actionable insights about patient management, clinical pathways, and public health.

Our AI and ML products are used by healthcare organizations to effectively:

  • Data mining to uncover the right statistical correlations.
  • Optimize their resources in a manner that would drive better efficiency.
  • Increase patient safety and decrease hospital readmissions.
  • Reduce the risk of the patient population.



MPAC initially evaluated DASS as a modelling collaboration platform, but upon seeing its governance and on-demand scalable capabilities decided to deploy it as the central platform for all data projects. At the time, their data was decentralized with multiple evaluation platforms causing painful system performance at times It took 40 hours to value 80,000 properties on a legacy system, whereas, now with the DASS solution, MPAC is able to value all 5 million properties in 4 hours.


Within only three months, MPAC had achieved over 75% savings resulting in and ROI savings of  $ 5 million/annually by reducing enterprise database license and analytics software cost, improved business efficiency by scaling to over 200 user accounts, two million inquiries, and a privacy by design framework. Therefore , they worked toward what they called the “one version of the truth”. “DASS has removed the technical obstacles that have historically restricted analytics. Now the sky is the limit”



As part of the regulatory mandate, the client had to stress
test their various portfolios to demonstrate resiliency to
untoward macroeconomic situations. The new solution
framework enabled the client to increase loss reserves
forecast accuracy by 10% resulting in reducing reserves
by more than 5 %.
The client establish this framework for stress testing the
portfolios by introducing macroeconomic variables as part
of the loss forecasting process.


This approach evolved over a period of two months. The client’s internal analytics team along with DASS solution were able to explore a lot more options, including survival data analysis and vector autoregressive models. The client was able to improve the overall loss reserving process by driving down the cost of reserving by more than 5%

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