Data Science & AI
InfoCentric offers advanced analytics, data science and AI solutions to help you address your most pressing challenges – achieving insights to drive organisational performance and success.
WHAT SETS US APART
The business doesn’t care how you solve a problem. They just care about it being solved!
InfoCentric has a practice of experienced data science & AI consultants dedicated to assisting customers to build their capabilities and solve real-world business problems.
Our practice is centred on the most pressing data science & AI problems facing data leaders today split into our three key service areas – Solution Development, Consulting/advisory and Specialised Resourcing
Using our maturity model for assessing your capabilities, we help organisations journey from descriptive and diagnostic to predictive, prescriptive and intelligent analytics. Moving from defence to offence.
We spend time analysing your specific use cases to streamline your operations and drive business insights – and securing you greater value in the long term.
KEY PRINCIPLES
To rapidly build data science and AI solutions, we embrace an iterative development and design approach which is often not linear in practice as we refine our solution.
Begin with a clear business need:
We incorporate design thinking and business case methodologies in our analysis to reduce risk by evaluating the likely paths to end states.
Define how your solution will be deployed and it will meet your end user needs:
To realise a significant value return Data Science & AI solutions need to be deployed on a cloud platform to scale user benefits, enable MLOps and support accessibility across an organisation.
KEY PROBLEMS WE SOLVE
Fundamental to our Data Science and AI practice is focusing on answers to the key problems facing our customers today:
- How can I quickly uplift the data science / ML capability of my organisation without a Data Science team?
- How can I accelerate my organisation’s data science development cycle?
- How do I create a successful Data Strategy to steer my organisation’s Data Science & AI capability development?
- How do I assess then improve my current capability regarding Use-case identification, operating model or ML Production?
- How can I quickly plug emerging specialised resource gaps with skilled people who can be immediately impactful?
- How can I manage my resourcing costs with fluctuating flows of project work?
OUR KEY SERVICES
We have three categories of engagements that we use as guides to co-design the best engagement to fit our client’s specific business challenge or resource needs.
AI Solution Development and Implementation
We rapidly create a working prototype which is then refined with your data into an effective solution fit for your specific needs and systems to achieve your goals.
Customer Propositions and Business Optimisation are two of the most valuable areas for an organisation to deploy Data Science and AI due to the scale of impact and ROI potential from optimising costly resources.
Customer Propositions
We analyse your customers behaviour, profiles and interactions to identify opportunities to deepen your relationships with personalization and predictive models.
- Acquisition & Retention Models
- Profiling & Segmentation Models
- Lifetime Value Scoring Model
- Product Personalisation Model
- Pricing Optimisation Model
Business Optimisation
We improve business performance through optimization of resources & leveraging predictive modelling to enhance decision making .
- New Product Development Tools
- Sales Forecasting Models
- Fraud Detection Systems
- Scenario Decisioning Tools
- HR Talent Retention models
Opportunity Mapping, operating models and ML Operations best practices
We support your team to deliver faster, more impactful data science and AI projects leveraging our industry experience and delivery frameworks.
- Risks, Dependencies and Value Mapping – We identify key risks, system dependencies and link potential data solutions and products to core business value drivers
- Processes and Operating Models – We assess current state processes, team structures, routines and governance to identify improvement opportunities.
- ML Ops Maturity – We assess current state ML production maturity and deliver customised recommendations to uplift capability
Data Science Managed Teams and Specialist Resourcing
We have deep expertise that can supplement your own internal capability across multiple data platforms that include Snowflake, Data Bricks, Azure, AWS and GCP.
Beyond delivering cloud data and analytics transformation, InfoCentric supports clients with specialist data resourcing assistance for ad-hoc projects, capacity uplift, or long-term resource-based partnerships.
We work with your management team to co-create the best resourcing model to fit your current capability needs and support your long-term goals.
BEST PRACTICES ML OPS
A popular area of focus is around ML Ops. Although each organisation may have different ML deployment capability, common best practices apply to all teams. These best practices include standardising processes and technology capabilities for building, deploying, and operationalizing ML systems rapidly and reliably.
- Start Versioning practices on Day 1
- Be organised, be consistent
- Don’t reinvent the wheel
- Apply CI/CD principals
- Simplify orchestration