A Typical project implementation does vary according to each project requirement. Ideally, we plan for a 5-6 month project time-line, half the time of traditional IT deployments.
Month 1. – Project establishment, Define Business Requirements and roles, Identify Data /IT infrastructure, Develop Implementation Plans
Month 2. – Data Modelling and Data conversion programming
Month 3. – Data conversion test & balancing
Month 4. – User training, test conversions, system configuration,
Month 5. – 2nd data conversion, some parallel transactions, on-site support, end of month final data conversion.
Month 6. – Live operations, off-site support services.
Little to none. All business data is aggregated automatically by read-only access and then blended in a secure cloud environment so as to not interrupt any business processes or interrupt an employee’s workday. However, as is common when reviewing data that has never been closely examined before, we do identify broken business processes and gaps between functional departments. This is a net positive result and it is up to the department head and their team to determine their preferred course of action.
You can expect high levels of collaboration and interviews to develop a 30,000 foot view of the business model, functional silos, and business processes. We then move to code and connect various application systems with external data sources in the cloud followed by launching a prototype visual console of KPIs. While these phases occur in a linear fashion, we include a feedback loop to ensure that even the most complex a project is completed thoroughly and benefits from revisiting an earlier phase.
Integration means connecting data silos, systems, or people. Helping clients design and implement integration solutions, we repeatedly came across the following six types of integration points:
Existing BI tools typically have a Data Base Structure, Data Warehouse, Meta Data and connections to the existing CRM/ERP Systems. Enterprise Intelligence can be set up as a layer on top of the existing BI tool or replace the existing BI tool to connect to CRM/ERP systems through APIs.
Enterprise Intelligence can be set up as a monthly subscription service to minimize the impact department budgets. Because we also deliver the analytics talent/resources, the impact on the Budget and Headcount would be minimum to none.
We work in phases which have milestones that allow for a stop/start of project workload. The phases occur in the following order:
Phase I – Diagnostic: The data identification and assessment is to clarify the state of existing software systems, the internal data requirements, and data quality which includes integrity and compliance ratings.
Phase II – Mapping: The project plan & design includes thorough examination of the business model and business processes which leads to connecting the dots between data in disparate silos by their use along the lines of business.
Phase III – Synthesis and QA: By joining data from the business with external sources (market and competitive) we are able to triangulate and synthesize new meta data from which the important metrics and KPIs are formed. Our feedback loop is the QA by which we iterate upon the visualization consoles needed by business users to make data-driven decisions.
Phase IV – Training and Support: Following the system launch there will be new questions and higher-level models such as predictive and prescriptive analytics that become possible. While the data visualization is user intuitive the training is especially helpful in empowering business users to ask advanced questions of their business challenges and be able to leverage Enterprise Intelligence for answers.
Enterprise Intelligence System is designed primary as a fully cloud capable. However, we can also implement the solution as a Hybrid by using the cloud as a processing and analytic engine while the on premises storage serves the visualization.
We are currently serving only companies in the U.S. and U.S. territories.
Depending on the implementation cycle, user would be fully proficient within 1 month of completion of the project.
Data analytics is a fragmented task as one group manages BI while CI and MI are managed by others. Few companies, especially the small and mid-sized, have the talent and resources to attain the analytic and data-driven proficiency needed to stay competitive. The Enterprise Intelligence system is more than a way to join data silos, it is a comprehensive solution for solving the talent and tools gap of the modern company.
The core mission of Enterprise Intelligence is to make data analysis user friendly like cutting-edge self-service BI tools while also keeping it far more inclusive of decisions that need to be made by groups of business users. As users corral around the Enterprise Intelligence to monitor performance and make company-wide decisions together, the user adoption will grow higher than that of the ordinary BI tools.
The training for the end users can be 1 month or less; shorter in time than most deployments or implementations.
Business users can easily request new data collections and visualization capabilities be added while data science and machine learning algorithms to manage and process data become accessible as add-on projects.
Consoles, KPI’s and Meta Data that manages the SKU hierarchy is developed at the beginning while over the curse of business evolution these can be altered or added as needed.
Enterprise Intelligence is fully cloud supported which means it reduces the cost of software, hardware, and maintenance to near zero. When an internal system such as the ERM or CRM tools are upgraded we’ll take the onus of ensuring connectors are updated to the Enterprise Intelligence system.
When your company expands into new markets it will likely want to add new data feeds specific to the competitors and opportunities of those markets. Building in new feeds does increase the subscription only so much as it takes to maintain a constant stream of fresh data.