We accelerate AI tranformation for operations. We help you craft a vision, select use cases and build technology and skills
We help operational and digital teams accelerate their data transformation
Data strategy for operations
In a world where technological innovation is a company’s success factor, data strategy becomes a key element to improve performance, efficiency, and agility in all business processes.
Nowadays, data exists in various forms and structures : unlocking its full potential can revolutionize organizations, in terms of process and business performance. Data is not only coming from company’s systems (ERP, WMS, Sensors, etc.), but also from immaterial assets surrounding a company : process knowledge, organization and people interactions, social medial, and even open data (demographic, traffic, weather etc.)
In addition, new technologies such as AI, data science, robots, IoT etc. allow business models and organizations to become data-driven. They transform data into actionable business knowledge, supporting the decision-making and optimizing your business potential.
Data strategy is the first step for business teams to discover and unlock their data’s value, by addressing two main questions:
- Which technologies and business use-cases should be prioritized, in order to maximize the business outcomes?
- How can my organization follow the technological evolutions, by forming teams with both technical expertise and business knowledge?
At IRIS by Argon & Co, we support our clients by:
- Organizing a digital discovery day: external testimonies, decoding of technologies, assess use-cases and macro roadmap (ExCom seminars of one to two days)
- Preparing and establishing a digital operations strategy
- Providing platform strategy design and new business models
Data governance consists of the people, processes, and information technology required to create consistent and optimized handling of an organization’s data across the business enterprise. It provides all practices with the necessary foundation, strategy, and structure needed to ensure that data is managed as an asset and transformed into meaningful information.
At IRIS by Argon & Co, we focus on the following goals, in all data governance projects:
- Business Value: identifying and demonstrating the value hidden inside data, to improve consistency and confidence of decision-making
- Security: finding the right levels of data and transaction protection, while keeping the organization process fluid and easy-to-navigate.
- Effectiveness: optimizing team effectiveness, by enabling a better planning by supervisory staff and clarifying roles in a data organization (data owners, data stewards etc.)
- Performance: establishing process performance baselines to enable improvement efforts
- Minimizing risk: decreasing all types of risks, such as regulatory fines, financial misstatement, inadvertent release of sensitive data, or poor data quality for key decisions
Our data governance experts can provide an audit of your current information systems (governance, skills, architecture, solutions, project methods), in order to define and construct a stable and robust data governance strategy for your organization, aligned with business requirements and ongoing IT initiatives
In modern enterprises, information is found everywhere : in databases, systems, files, non-structured systems, inside every organization or business team.
Data quality management is a set of practices, in order to maintain a high quality of all that information, before exploiting or sharing it inside your organization.
IRIS by Argon & Co positions data quality at the core of every data project, in order to optimize its efficiency and performance. To do so, we establish two main pillars of success:
- People: defining the roles and responsibilities for everyone working with data (data owners, data stewards, managers, analysis etc.), both in business and IT teams.
- Process: reporting on data quality using a set of techniques (data profiling, outliers detection, rules-based assessment), allowing to zero-in on the discrepancies and define a repair process for all data not respecting the quality rules defined.
We help you answer the question “what is high quality data” in your enterprise, by finding “quality rules”, based on business rules and requirements, such as:
- Accuracy, in order to indicate whether data is void of significant errors.
- Consistency, to specify that two data values pulled from separate sources do not conflict with each other.
- Completeness will indicate if there is enough information to draw conclusions.
- Integrity, to structurally test data to ensure that it complies with procedures
- Timeliness which corresponds to the expectation for availability and accessibility of information.
In a rapidly-changing business market, agility becomes a differentiating factor. In order to achieve its potential, a company must be able to :
- Adapt its organization, having chief data decision-makers work with various data owners and business teams with different needs, to ensure end-to-end visibility in all business processes
- Quickly move forward with business use-cases implementation, from requirement definition to the deployment of a model or application
At IRIS by Argon & Co, we consider that a key success factor in all data projects is the existence of a technological layer, a data platform, which brings together all business data -structured and unstructured-, ML engines, development processes, connectivity (such as API) etc. while being synced with all transactional internal and external data sources, in order to be the “single source of truth” for all business use cases developed.
A data platform can be created:
- Using agile method in a step-by-step approach, while prioritizing and developing business use-cases, gradually ingesting new data sources into the platform
- As an MVP project, related to the development of a high ROI business use-case
- As a separate data architecture project to gather all necessary data and provide an end-to-end visibility in all business processes
Business Intelligence is now widespread to almost all business domains: Supply Chain, Finance, Logistics, Purchasing, HR, Marketing etc.
The existing variety of data sources, reporting tools and users, who express different requirements and levels of access to the information, increases the complexity of building a robust BI strategy.
Throughout the BI process, from the definition of the data architecture to the use of a report or dashboard, a common vision must be shared. This vision aims to optimize the user experience and performance in the data usage, while respecting IT vision and data governance rule
Our BI strategy at IRIS by Argon & Co, focuses on the following points:
- Business Impact: finding which data and domain have the most business impact inside your company
- Exploit: defining the best methods to exploit these data
- Enrich: transform your data, in order to create new information layers, using technological and business expertise.
- Visualize: finding the most relevant visualization methods to display your data and derive insights
- User experience: proposing the data structure that would optimize user’s reporting experience and performance
- Tools: providing experience in a variety of BI tools in the market and proposing best fit solutions for your business requirements
- Decision making: optimizing decision making by analyzing and displaying the most relevant information at every level, providing a clear message for each decision-maker.