DATA MANAGEMENT & ANALYTICS
INSPECT
CLEAN
TRANSFORM
MODEL
The value of data and information assets is key to any organization’s success.
Since decision quality is making the decision which best addresses the problem, the team must have the best data possible from customers. A decision based on inadequate data will be a poor decision. A decision that addresses 'all' customers needs is a quality decision.
We focus on the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Data integration is our precursor to data analysis and data analysis is closely linked to data visualization and data dissemination.
Data Mining is essential and focuses on modeling and knowledge discovery for predictive and descriptive purposes.
Business Intelligence covers data analysis (whether descriptive, exploratory or confirmatory) that relies heavily on aggregation, focusing on business information. Exploratory Data Analysis focuses on discovering new features in the data, and, Confirmatory Data Analysis focuses on confirming or falsifying existing hypotheses. We use statistical models for predictive forecasting and classification. Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data.
Data visualization is both an art and a science.We focus on data visualization, closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development.
The value
of data and information assets
is key
to any
organization’s success.
The most important buying criteria for BI tools are scalability and performance, usability and UI, ease of development, and mobile/cloud based customization.
An effective BI solution should be able to access any data source and provide capabilities for internal and external users from the same platform, as well as provide better integration with other systems.
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ETL Technologies (Informatica, Ab Initio, IBM Infosphere Datastage, Oracle Data Intergrator), Reporting (SAS, Business Objects, MicroStrategy, Hyperion, Cognos).
ETL tools have started to migrate into Enterprise Application Integration, or even Enterprise Service Bus, systems that now cover much more than just the extraction, transformation, and loading of data. Many ETL vendors now have data profiling, data quality, and metadata capabilities.
The industry is moving towards open but highly scalable solutions. Traditional databases such as DB2, Oracle, SQL Server license proprietary software, but run on commodity hardware. The nature of Symmetric Multi Processing (SMP) architecture favors a few large expensive servers.
In recent times, the rate at which data is generated has increased, driven by an increasingly information-based economy and social media.
Data created by Internet activity and an expanding number of sensors in the environment, such as satellites and traffic cameras, are referred to as "Big Data". Processing, analyzing and communicating this data present a variety of ethical and analytical challenges for data visualization. As data scientists, we help address this challenge. Security is also a key concern.
Massively Parallel Processing (MPP) database vendors all have proprietary software. Netezza and Vertica were on open source PostgreSQL database. PostgreSQL is a powerful, open source object-relational database system. It has more than 15 years of active development and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness. It runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows.
Teradata and Netezza even implement custom hardware.
Development languages like Hadoop have open sourced the software component leading to a vibrant ecosystem of tools and applications. And with built in redundancy, it’s easy to deploy on cheap commodity servers.
Our strengths:
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Analyze and identify “raw data” that allows the customer to achieve strategic business value, keeping security as a key requirement.
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Iterative design, development and implementation of Data Analytics that optimizes and leverages the processing power of MPP data appliances.
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Neutral evaluation - Provide on-going support - onsite and IaaS models.
