Slide to see the reality of AI and Data Projects Now
We are here to change this!
In a post-pandemic era, companies and governments globally aim to become smarter with an ultimate goal is to become percipient, astute, shrewd, and quick:
- Use AI to gain better understanding of their customer behavior.
- Use this ability to assess the change in customer experience across differing contexts.
- Be able to judge how do they responsibly unleash the power of data.
- Make decisions and respond quickly or effectively to maximize the company’s or nation’s return on investment in data.
Communication: Is your team able to communicate using data?
Skills: includes the skills, knowledge, attitudes, and social structures required for different teams to use data.
Semantic: Appropriate data standards formats, Metadata for Big data, Data Catalog and taxonomy for data & usabilities
Physical: Infrastructure and equipment needed for data & AI initiatives.
Syntactical: Translation of encoded and decoded data
Judicial: internal organizational laws, regulations, and constraints.
Economical and Financial constraints that hinders the development of full interoperability.
Governance: who access what? Decision making processes, layers, and responsibilities.
Business Processes: service level agreements and workflows.
Data Lineage: Information protocols and identifying the origins and transformations of the data and keep track of them
Encryption & Security: We need to unlock this data so that you are able to move it from one system to another in a safe, secure, and confidential manner.
Smart Data Models: Build models that are flexible and semantic, that exist independently of infrastructure, vendor requirements, data structure, or any other characteristic related to IT systems. As such, they can incorporate attributes from all systems or data types in a way that is aligned with business processes or specific use cases.
With our vast experience in designing standards for the data management industry, we focus our services on covering assessment, design, strategy, technology selection, and vendor-relationship management only so we are not a data engineering firm. Our consultants will help you in all aspects of data management including:
AI strategy serves three functions. First, it forces you to articulate the value of data inside your organization, so that your company as a whole can understand the importance of your data to creating value. Then, it tells you how to move forward, providing a roadmap for how to build those things of value: it tells you what you need to do and how you can get started—or continue moving forward. Third, it showcase the ways you can build successful AI experiences with a valid business case. The hallmark of a good AI strategy is that it is people-driven, not technology-driven.
Depending on your budget, readiness, and scope. We provide two AI Strategy offerings:
Our workshop will help you to create a quick data strategy (AI Readiness Strategy Checklist) aligned with business objectives.
Our workshop main output is an action plan for an AI Use Case that solves a specific business need
2nd Offering: Enterprise-wide strategy which is usually 4-6 months assessing organizational maturity, building a data architecture, benchmarking, roadmap, and use case development.
CEOs or business executive with a vision for how to use data and CIOs or CDOs with a data vision are our winners after taking this offering.
Data Governance is the main pillar for any successful AI project.
The future of master data management will derive value from data and its relationships to other data. MDM will be about supplying consistent, meaningful views of master data. In many cases, we will be able to unify data into one location, especially to optimize for query performance and data fit.
Principle: Let’s collect more and more data, but what can we do with it? Is it clean? Who will use it and how?
Business Driver: Without having both a data governance and data quality strategy “what happens in the lab will stay in the lab” Giving datasets in hackathons or distributing cool swags in these labs won’t drive any value. They might not have a clear framework for taking products from the lab
Challenge/Myth: Building data lakes in our company and announce our AI Innovation Lab, buy AI Sandbox for it and hire Python programmers. We will start getting insights!
How can we Help? Build a Data catalog: It knows everything About Your Data And Where It Came From. You can play hide-and-seek in the depths of a data lake
Data Lake is not a Platform, it is a new data-driven design pattern or architectural form. It is just a place to store Data – collection of All Data – Something you were not able to do or afford 10 years back.
It is not the traditional data modeling and governance. Data curation is a strategy and a practice as it relates to data readiness (applications, sources, and data), data literacy (people and culture). We envision a data world where data from all sources is defined, organized, de-siloed, timely, consistent, reliable, available to all authorized users, applications, analytical tools, and any imaginable use case, and bringing answers to business questions across the organization
Architecture is an abstraction. It is not driven by IT. It is driven by a purpose: business goals, outcomes, use cases.
We adhere to an architectural philosophy using concurrent viewpoints of logical, process, development, and physical perspectives, plus the viewpoint of user scenarios. We then integrate these views through consideration of business goals, operations realities, existing systems, and available technology. This combination considers the people, process, and technology aspects through an agile, iterative process. The input to our architectural method begins with the discovery phase and subsequent iterations through the workshops. This will result in technical and data architectures that meet all requirements of the enterprise.
Design and build a data quality assessments using our very own
DataOps is DevOps, but solely focused on modern data integration, data engineering, and analytics.
CoE should identify potential for data-driven business models to lead the transformation towards a data-driven company.
Data Visualization is our art.
Self service data access and preparation is now given to many stakeholders in the enterprise, thus, bringing challenges to traditional data governance and security methodologies.