We design, build and support the following Data Management Infrastructure

Biz@nalytica is a business and technology consulting company that specializes in helping clients develop solutions that will improve business results, reduce operating cost, reduce total cost of ownership and improve efficiency and performance. We have experience in creating strategic data stores such as: Data Warehouses, Datamarts, Operational Data Stores and Data Lakes. Biz@nalytica also has extensive technology expertise in Database, ETL/ELT, Hadoop, Governance, Data Quality and Analytical Software. Our consultants can assist with the development of complex analytics employing in-database, Spark and machine learning technologies. In addition, we routinely assist customers with the full development lifecycle: business case development, requirements definition, technology selection, design, testing, training and application rollout. The focus of these offerings is to enable our clients to deliver higher value solutions at a lower cost to gain advantage over their competitors.


A Data lake is a large storage repository that holds data until it is needed. The term was coined by James Dixon, Pentaho Chief Technology Officer. As of 2015, data lakes could be described as one of the more controversial ways to manage big data. While a Hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analyzed to help answer the question.


In Computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for Reporting and Data Analysis. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise. Examples of reports could range from annual and quarterly comparisons and trends to detailed daily sales analyses. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data Store for additional operations before it is used in the DW for reporting.


Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for Business Analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online Analytical processing, analytics, Data mining, Process Mining, Complex Event Processing, Business Performance Management, Bench marking, Text mining, Predictive analytics and Prescriptive Analytics. BI can be used to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions include priorities, goals and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a more complete picture which, in effect, creates an “intelligence” that cannot be derived by any singular set of data.


Mobile device management (MDM) is an industry term for the administration of Mobile devices, such as smartphones, tablet, Computers, Laptops and desktop computers. MDM is usually implemented with the use of a third party product that has management features for particular vendors of mobile devices. MDM is a way to ensure employees stay productive and do not breach corporate policies. Many organizations control activities of their employees using MDM products/services. MDM primarily deals with corporate data segregation, securing emails, securing corporate documents on device, enforcing corporate policies, integrating and managing mobile devices including laptops and handhelds of various categories. MDM implementations may be either on-premises or cloud-based.

MDM functionality can include distribution of applications, data and configuration settings for all types of mobile devices, including smartphones, tablet computers, ruggedized mobile computers, mobile printers, mobile POS devices, etc. Most recently laptops and desktops have been added to the list of systems supported as Mobile Device Management becomes more about basic device management and less about the mobile platform itself. MDM tools are leveraged for both company-owned and employee-owned (BYOD) devices across the enterprise or mobile devices owned by consumers. Consumer Demand for BYOD is now requiring a greater effort for MDM and increased security for both the devices and the enterprise they connect to, especially since employers and employees have different expectations on the type of restrictions that should be applied to mobile devices.

By controlling and protecting the data and configuration settings for all mobile devices in the network, MDM can reduce support costs and business risks. The intent of MDM is to optimize the functionality and Security of a mobile communications network while minimizing cost and downtime. With mobile devices becoming ubiquitous and applications flooding the market, mobile monitoring is growing in importance. Numerous vendors help mobile device manufacturers, content portals and developers, test and monitor the delivery of their mobile content, applications and services. This testing of content is done real time by simulating the action of thousands of customers and detecting and correcting bugs in the applications.


Predictive Analytics encompasses a variety of statistical techniques from modelling,machine learning and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. One of the most well known applications is which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.


Machine learning is a subfield of that evolved from the study of Pattern Recognition and computational learning theory. Machine learning explores the study and construction of Algorithms that can learn from and make predictions. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Machine learning is closely related to and often overlaps with Computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical Optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit Algorithms is infeasible. Example applications include spam Filtering, Optical Character Recognition (OCR),Search Engines and Computer vision. Machine learning is sometimes conflated although that focuses more on exploratory data analysis. Machine learning and pattern recognition “can be viewed as two facets of the same field.”


We’ll meet and even exceed your IT and security teams’ requirements. We have set up sophisticated Virtual Private Cloud infrastructures for our enterprise customers. As AWS Advanced Partners, and because we only run on AWS, we can leverage the AWS certified infrastructure, including SOC 1 Type 2 (formerly SAS-70), ISO 27001, PCI DSS, FISMA Moderate Compliant Controls, HIPAA & ITAR Compliant Architecture. Depending on your requirements, we can set up any level of security: from simple whitelisting or VPN, to direct connections into your data center.

The alignment of IT strategy with business strategy is important. If those two strategies are misaligned, then it becomes more difficult for your company to stay on track with accomplishing business goals. One way to ensure that those two strategies are aligned, is to utilize the IT Governance model from BizAnalytica to create structure in your own company between IT and business operations. In order to achieve true success in your business, all operational processes need to achieve quantifiable outcomes. Additionally, all shareholders need to benefit from a well-oiled machine achieving all proposed goals. When working with BizAnalytica, our team of professionals can complete a comprehensive review of your business operations in order to answer important questions regarding how well your IT Department is operating. This review will ascertain what specific parameters management has set in place regarding IT operations, and whether or not there is a positive ROI on that department.