Aptus Data Labs’ analytical framework for Data to Decision (D2D) provide enterprises with apt impetus essential for making key enterprise-wide, data-driven business decisions.

At Aptus Data Labs, we have understood the profound industry-need for cohesive analytics platforms, which not only help to assimilate structured and unstructured data, but also takes care of the entire analytics value chain – right from curetting and deciphering massive amounts of granular data to delivering data-driven big decisions.

Hence, we have built extremely agile and scalable domain-specific platforms on proven algorithms, software accelerators and reference architectures that addresses the most challenging big data opportunities with high-performance analytics at low latency. These platforms eventually help solve high-impact business problems rapidly.

Integrated with very high performing components and service layers, our platforms can adapt itself to changing users, analytics, or business needs. These components and service layers, which were built to fill the technological gaps we identified with our experience, are domain specific; and are designed to bring additional value expected from today’s businesses.

Eco

Ecosystem

Our Data to Decision (D2D) framework is the heart of our advanced analytics ecosystem and our platforms. It encompasses meticulously placed Data Engineering, Data Science and Decision Science components at the crux. Refer Figure 1. These components, which utilize industry-standard software tools, statistical models, behavioral science, design thinking and decision tools, facilitate businesses to take the most pragmatic approach for data management through till decision-making. The direct customer benefits from the D2D framework is speed to value achieved by : rapid data exploration, speed to data insights & accelerating data discovery.

Our Data to Decision (D2D) Framework

ecosystem

 

Figure 1

Unlike other D2D frameworks that directly collect the Big Data in heaps, Aptus Data Labs instigates by understanding the real business query and assimilates vertical/functional data according to the requirement.

The vertical/functional data, which can be structured, un-structured or semi-structured, is sourced from enterprises, businesses, syndicates, machines & sensors, geo-locations, web click streams, server logs or social media. The assimilated data is processed through several integrated layers and modules, encompassing accelerators, reference architectures and algorithms, to arrive at the required insights, as per the business query.

Some of the key components of our D2D framework, which help businesses navigate through the data analytics path easily – right from Primary Data to Data Engineering and then Data Science to Decision Science, include:

Applied Data Engineering

Our mission-focused data engineers utilize data engineering concepts to understand a business query requirement, using the raw data. Right from data acquisition, data storage, data processing and data workflow management, our data engineering model leverages upon the power of algorithms, technology and third party data management tools to extract the underlying information from the big data – irrespective of its volume, velocity and variety.

Applied Data Science

Once the information is extracted from Big Data, the functional data is further processed using Data Science, which is a niche area that extracts nontrivial knowledge from surfeit of functional data, to improve decision making. Built on our customizable accelerators, algorithms and reference architectures, our Data Science components help enterprises make strategic, operational business decisions with right statistical and mathematical techniques to reduce risk, maximize profits, minimize costs, or more efficiently allocate resources.

Our computer scientists, operations researchers, mathematicians, statisticians and above all Data Science researchers –who have hands-on expertise in applied mathematical algorithms, econometrics, statistics, pattern recognition, operations research, machine learning and decision science – extrapolate key business values using descriptive, predictive and prescriptive capabilities.

Decision Science

Since descriptive, diagnostic or predictive analytics are not sufficient to arrive at Big Business decisions, Aptus Data Labs utilizes its key decision-making systems and expertise intervention to deliver streamlined decision models that help optimize business operations and reduce operational costs.

Our domain architects, business domain experts and business analysts utilize human-driven decision-making systems, decision support approaches, operational intelligence platforms, intelligent business process management (BPM), business rule processing, management science/operations research and more to transform meaningful insights into Big Business Decisions.

Advanced Analytics through technology

 

 

Covering the complete Advance Analytics value-chain, our D2D Framework is empowered with technological components of Advanced Analytics.

Since Advanced Analytics is a niche area of analyzing data using sophisticated quantitative methods to produce insights, our proprietary D2D framework helps enterprises to optimize the data supply chain with the right data, at the right time and at the right place, to arrive at Big Business Decisions.

We at Aptus Data Labs have gained good Advanced Analytics acumen by working on Data Science, Big Data Analytics, Predictive Analytics, Real-time Text Analytics, and “R” functions and packages. Time and again our clients have approached us for our hands-on expertise on sensor/signal analytics, web click stream analytics, geospatial analytics, machine learning and more. While we continue to hone our skills, we have been recognized for our services in the area of social media analytics, Linked Data and Predictive markets too.

By applying our years of domain experience and industry’s best practices for business process and technology integration, we deliver streamlined decision models that help optimize business operations and reduce operational costs.