Centre Of Excellence Creation

  1. BFSI
  2. MFG
  3. PH&HC
  4. RTL
  5. TMT
  6. EDU
  7. E&U
  8. OTH

‘Analytics Centers of Excellence’ is-a-way to institutionalize and accelerate the adoption of AI-based automation, machine learning, and big data within your organization.

How Quadratyx can help your business build an efficient Analytics Center of Excellence?

Build_Talent
Build Talent
  • Comprehensive training and workshops on data science related concepts and technologies
  • Evangelize best practices in building effective teams to execute data science solutions
  • Identify talent and train them on advanced analytics, big data, cognitive process automation, deep learning etc.
  • Mentor and actively guide business teams so that they holistically understand the business problem and measure the success of its solution
Strategic_Planning
Strategic Planning
  • Charter roadmaps for short, medium, and long-term data science success
  • Define and identify a list of analytic problems that can be solved along with their respective business impact
  • Prioritize these analytics problems
  • Recommend processes to be followed while building analytics solutions
  • Identify relevant data sources and recognize key data points that are not currently being stored
  • Recommend relevant external data sources
Next_gen_decision_making
Next-gen decision making
  • Advise on the solution architecture, tools, data management, security, and governance
  • Devise strategies for optimal storage (Big data lakes, data warehouses, Cloud etc.)
  • Best suited methodologies and algorithms to solve the give business problem
  • Solution to be scalable and adaptable to the latest technological developments

We are at an inflection point!

we_are_at_an_inflection_point
  • The proliferation of big data, cloud computing, and AI technologies are ensuring that businesses are leveraging these technologies to run multiple proofs-of-concept & engagements to solve small-to-medium scale business problems.
  • We are now convinced of the business value that these technologies bring to the table. This has been established by a significant number of use cases.
  • Buzzwords such as real-time analytics, reinforcement learning, artificial intelligence are the new normal in the business world.
  • However, organizations are still grappling with the complexities of implementing an enterprise-wide analytics solution. Leveraging big data to build a scalable solution is key to becoming a data-driven organization.

However, some factors limit an organizations ability to scale its analytics vision.

Data Science talent

The industry is facing a shortage of skilled workforce. A workforce that is ranging from data engineers, big data architects, data scientists and analytics managers. As technologies continue to evolve it becomes difficult to acquire, hire and retain good talent.

These new skills require new ways of working and therefore, companies need to explore their organizational culture.

Even if one does manage to acquire the right talent, the key is to ensure that this talent is leveraged across the firm and not be concentrated to just one or two departments.

Organizations also need

  • SME/domain experts who understand business needs to communicate with the data experts.
  • Managers with decision-making ability.
Standards and Governance

It has been noticed that Analytics teams in different divisions or business units tend to work with their own set of tools, libraries, software versions, and data sets. This lack of standardization can make it difficult to implement global solutions.

Organizations need to define standards with regard to tools, coding, version control, and quality control, and have all the teams working with the same tools and sharing their methodologies.

In addition, data science teams must have big data governance policies and processes in place, controlling access to the data and ensuring security and data privacy controls.

Why Analytics CoE?

'Analytics Centers of Excellence' is-a-way to institutionalize and accelerate the adoption of AI-based automation, machine learning and big data within your organization.

We believe, the CoE must be a mix of business and technically savvy individuals with centralized and distributed competences and resources. While simultaneously, creating a common repository in which methodologies, tools, and models are shared.

This core team will grow as the organization expands, and will be able to offer its services to multiple departments within the organization.

What do you gain?

  • Accelerate advanced analytics and AI adoption by business at scale
  • Shorter time-to-market cycles to deploy analytics solutions
  • Permeate best practices and learnings across the firm. Achieve active synergies