What we do?
Unleash the power of Data, Enable Informed Decisions
- Data Capturing and Integration
- Data Governance and MDM
- Data Warehousing
- Business Intelligence
- Performance Management
As a specialized information technology service provider, Inseyab offer a unique set of services best suited to meet the customers’ information management requirements. Inseyab is one of the first companies in the MEA region to provide a total end-to-end Data Management and BI consulting and systems integration solution to its customers. Our services are aligned with ten functions described in the Data Management Body of Knowledge (DM BOK) from the Data Management Association (DAMA). Inseyab’s Business Intelligence consulting team is experienced in helping companies analyze their business data strategies and environment to gain accurate historical, current, and predictive views of their business operations. Our solutions incorporate Business Intelligence best practices and leverage clients’ existing investments in various tools.
Our full spectrum data services are implemented using what we refer to as the Inseyab Data Discovery Methodology (Inseyab DDM™). Our methodology consist of 5 data process streams that are aligned with the services that can be implemented sequentially, simultaneously or independently fashioned to the strategy and design of our clients.
Connecting the Dots with Inseyab DDM™
At Inseyab, we’ve coupled our experience & best practices to orchestrate the delivery of services for our clients in a detailed, meticulous methodology known as the Inseyab Data Discovery Methodology (DDM™). This proprietary methodology and many years of BI/DW solution delivery experience is put to work for our customers to provide end-to-end services, from strategy and planning to tool selection and implementation.
The Inseyab DDM™ is equipped to provide both consultation and implementation services from initiation-to-completion projects as well as for existing data processing initiatives. Our methodology can be used to build project from ground up or can be injected into an existing process for improving effectiveness, increasing scope and/or expanding the end objective.
While our services remain technology and platform agnostic for the most part, we do not shy away from taking sides and go for the best tool available for getting the job done. At the same time, our methodology is flexible enough to cater to unique client requirements and environment – The methodology is flexed to work around the business and technology constraints presented, instead of seeking to uproot existing operations and business processes.
Our methodology consist of 5 data process streams that are aligned with the services that can be implemented sequentially, simultaneously or independently fashioned to the strategy and design of our clients. While each stream follows the linked list approach, streams can be focused upon independently in a client’s existing data management practices. Sequentially, each stream’s output is the input of its forthcoming sequence. The Inseyab DDM™ acknowledges the value of a tailored and flexible methodology and each stream can be deployed to a client’s benefit, independently. This flex DDM™ stream approach transforms existing process and inputs so that they can be consumed by a stand-alone stream.
What are your Business goals?
Identify the questions that need to be answered
What are the main problems that need solving?
What answers would translate to: more profit, value creation, increased market share or cost reduction?
Preparation of the data
Is the collected data reliable?
Are there any errors within the data set that may lead to misinterpretation?
Does it have to be reformatted?
How to interpret particular features?
Evaluate your findings
Did we get the answers we wanted?
How accurate is the created model for this process?
How does it perform on a new data set?
Is data available?
If yes great, if not there are several options:
Build a process capturing data (CRM, Auto-tagging, questionnaires)
Use publicly available data (Open data, social media, web, other sources)
A small sample of future data can be used to run a trial system
Converting data into information and knowledge
Lets find the visible and hidden relationships in the data
Can the process be converted into a reproducible model?
Can we use the model for forecasting?
Implementing your findings effectively
What is the best way to use the knowledge from the Data Mining process?
Will one analysis and summary report be enough?
Is a cyclical process required that could also be incorporated into the system?