A 5 step process for creating an Integrated Data Science Center of Excellence (COE)

COE – Is a centralized data analytics hub surrounded by a selected group of highly intelligent experts that are like-mined and consistently aligned within business units to make better strategic decisions. 

A company’s success is established by managing resources conclusively, processes efficiently through ambitious data science projects, data-focused mindset, and strategic technology roadmap to grow its business.

Strategic management, the modern methodology of data science, and scaling a business with centers of excellence are at the heart of top-performing organizations where data innovation meets success. The importance of analytics centers goes far beyond business operations and corporate performance. Even though building a data center is a considerable investment, the rewards far exceed the risk bringing high ROI, maximizing opportunities, and securing operational improvements.

Data is everywhere; it affects everything and every business. Finding smart ways to analyze people, online information, and statistical data is now an absolute necessity for growing and learning. Data collection, data management, and data mining help leaders make accurate business recommendations through predictive analytics, statistical manipulation, and data science.

 “The corporations that embrace Data Science will find their long-term, generational growth directly proportional to how effectively they incorporated data scientist teams into their strategic planning. Data Science takes the guesswork/emotions out of answering business questions by applying logic and mathematics to find better solutions.” ― Ken Poirot

Competency data centers have a collection of IT skills, proficiencies, and expertise that are joined – have an organizational structure to provide leadership, a centralized focal point, and know-how on different disciplines within the organization.

  1. Integrates new ideas, innovation and operational efficiency and new technology implementation, cutting-edge data analytics, tracking customer actions and patterns, studying information process flows.
  2. Improves forecasting demands enables new standards and methods of operations incorporated into the fabric of business units.
  3. Identifies new and data-supported opportunities for leveraging company data to drive business solutions, breakthroughs and builds a strong competitive landscape.
  4. Drive optimization and improvement of product development, marketing techniques and business strategies.
  5. Effective and accurate new data sources and innovative data gathering techniques.
  6. Increase and optimize customer experiences, revenue generation, ad targeting and better operational business outcomes.
  7. Can design and develop A/B testing framework and test model quality processes. Coordinate data processes with different functional teams to implement models and monitor outcomes.
  8. Develop standardized processes and tools to monitor/analyze model performance and data accuracy.
  9. Brings highly qualified experts, foundational authority teams, and competent leadership, predicable outcomes, – skilled training programs to reduce risk and expand business intelligence within an organization’s structure.
  10. Good values, IT/Business ethics, data standards, procedures, processes, governance and ideologies that help the organization institute modern management.
  11. Customer relationship management tools, digital networks, and new technology to drive success and return on investment through analytical development and visualization.
  12. Hiring an IT consultation company can create a sustainable data science center of excellence. It provides a centralized platform and collaborative environment for all data structures and addresses within the COE teams.
  13. It establishes and builds a holistic, centralized team, governance structure, success criteria, business change management, and expansion into industry partnerships.
  14. Helps to breakdown silos, strengthen information sharing, and bond teams to the joint mission.
  15. Digital, data and customer insights to transform business by finding answers to internal and external problems.

COE Building Blocks

  1. Data Mining and Data Science
  2. Visualization and Communication Development
  3. Customer Relationship Management
  4. Business Process Equality
  5. Application Development
  6. Digital Operational Automation
  7. Customer Acquisition
  8. Product Development
  9. Machine Learning/AI Algorithms
  10. New Technology Expansion
  11. New System Management / Legacy Modernization
  12. Data Analytics Techniques
  13. Financial Report, Analysis
  14. Change Management / Capital Administration
  15. Employee Development
  16. Governance and Quality Improvement
  17. Process Standardization
  18. Human Resource Management
  19. Cloud Computing
  20. Infrastructure, Systems / Mobile Apps

Data Science and Centers of Excellence Structure

Data Science

“Data Science takes the guesswork and emotions out of answering business questions by applying logic and mathematics to find better solutions. It manipulates data, looks for patterns, and comes up with solutions to drive revenue, lower expenses, and increase overall business profitability.”

  • Due to big results, big revenue, and big returns. ― Ken Poirot

Since data ignites new ideas, thoughts, and inspiration, data science adds value to business models by utilizing deep learning and statistics to predict achievable outcomes, potential conditions and probable results, workable solutions, and promising outcomes.

  • Data applications generate actionable discoveries by extracting data from structured and unstructured information for improved workflow; data supported decisions from hiring to advertising. It is at the core of digital success and error-free data processing.
  • Is a scientific technique to transform business units, product lines and customer-based services. A process of information manipulation and data excellence of machine learning, big data, data analytics, data science, statistics, infrastructure, systems, and data mining.
  • Data Science is an interdisciplinary field of intelligence – methods, processes, algorithms, and systems are designed for scalable automation, competitive advantage, change management, and sustainable development.
  • It results in building a unique value proposition, viable methods of computing, business, and team synergy, and value-based management.

Center of Excellence

Is the new catchword that business people use to demonstrate to clients and potential partners they are experts in a field, topic, or discipline? To implement data science centers of excellence, many businesses will decide to reach out and hire IT consulting firms, software companies, and technical professionals for support, leadership, and expertise.

It has the ability to grow revenue through tackling uncertainties in business, provides solutions to difficulties in understanding data, removes the problems with customers, and issues with services and products that cause reduced productivity and decreased sales.

  1. Creates greater operational efficiency, value-driven services, and cost reduction projects – combined with top talent, technology, and predictable outcomes.
  2. Customer satisfaction, customer experience and customer loyalty are all the result of a well-developed COE.
  3. Helps to predict the success rate of business goals and objectives. In addition to improving business performance and creates applications to study employees and customers.
  4. A data center nurtures growth, expansion, and opportunities – COE leaders can better understand products, customers, and enhance business workflows, thereby improving the competitive opportunities and business landscape.
  5. COE IT leaders ensure data is in a useable condition and helps teams to make the right decisions at the right time with backed up facts, data, and industry knowledge.

Types of Centers of Excellence

  1. Program Management Centers
  2. AI Center of Excellence
  3. Analytics Centers
  4. Competency Centers
  5. Development of Operational Centers
  6. IT Modernization Centers / Service Level Agreement Centers
  7. Best Practice Centers
  8. Process Improvement Centers
  9. Innovation Centers
  10. Data Science Centers
  11. Big Data Centers
  12. Cloud Enablement Service Centers
  13. Social Media Service Centers
  14. Vendor Management Centers
  15. Business Case Centers
  16. Mobility Solutions Centers
  17. Automation Centers
  18. SAP Centers (Systems, Applications, Products in Data Processing)

Outcome COE Implementation

Successful data centers enable standardized processes, data science expansion, operational improvements, digital advancements, and data developments that are scalable and sustainable to the organization’s environment for future growth and long-term objectives.

An organization that leverages data science, machine learning, and implement data centers bring a positive mindset, fresh ideas, synergy and new thinking to the business, by helping to multiply ingenuity, change, collaboration, and creativity through better data collection and improved data management.

  1. Faster Operations, Automated Tasks
  2. Data Cleanup of Combined Structured/Unstructured
  3. Automated Document and Data Processing
  4. Better Services, Better Products, and Better Website
  5. Reduced Errors, More Data-Supported Decisions
  6. Consolidated Data and Digital Automation
  7. Quality of Service, Governance and Standardized Data Processing
  8. Reduced Risk, High Prevention Measures
  9. Robust Security Increased and Potential Risks Diminished
  10. Improved Site Experience for Customers
  11. Superior Infrastructure and Systems,
  12. Discovery of Top Talent
  13. Customer Satisfaction and Experience
  14. The emergence of Cloud Computing
  15. Improved Advertising and Customized-Better Targeted Marketing
  16. Customized Data Collection, Rapid Growth
  17. Strengthened Technological Foundation

Step 1 – Create Your Teams and COE Processes

Technical Teams

Map out the roles, objectives, and team member responsibilities – For the Executive Steering Committee, IT Support Services Team, Program Management Team, and the Power Users.

Corporate chief data officers (CDO), chief digital information officers, chief digital officers, and data mining experts drive business growth, digital modernization.

Including enterprise expansion and implement corporate information systems to gain data knowledge, information and help operational power performance of business processes.

  1. Executive Leadership, CIO, Business Leaders, Market Research Analyst, Data Analysts, Data Scientists, AI/ML engineers and Project Managers all work together in a collaborative, mutually mission-focused way to gather insightful information.
  2. Data Scientists work in centralized teams to quickly implement new technology and systems and build new algorithms and infrastructure with quantifiable evidence-backed data. They adopt a mixture of organizational models and business applications for data science.

A Data Science Team

“Commit to making a difference, making a distinction, and making a revolutionary decision.”  “Just don’t be afraid of falling or failing. Remember, it’s all up to you.”Seymour Howell

“Invite your Data Science team to ask questions and assume any system, rule, or way of doing things is open to further consideration.” ― Damian Mingle

The data science team – studies data sources and queries database management systems – through recommendations, pattern detection, grouping, predictions, and classifications. DS teams help to answer the impossible questions, bridges the gap between business units, and solve data overload issues by cleaning, storing, and organizing digital content into streamlined data intelligence and governance solutions.

Data Science supports change, incorporates statistical techniques to transforms data, reduces risks, and can forecast future events. By studying big data, real-time data, historical data for better marketing, targeted advertising, and reduction of churn rate.

  • Applications in data science can advance online presence, retain and acquire customers to process improvements, money managing, and data management.
  • A crucial tool and scientific method for businesses to improve data efforts, business growth, and corporate success – through quantitative analysis, data mining, deep learning, big data, and predictive data analytics by analyzing current business models.
  • Machine learning, data mining, big data, data analysis, and statistics are used to track business data, applications, customers, and users to uncover the best way to use the information from data analytics.

Building COE Environment

Data Applications are designed and build for comparing products to competition and can analyze the market, trends, and changes. Provide recommendations and find where the services and products will sell well and how businesses can build a thriving online presence. Manual processes need to be identified and chosen for digitization and analysis.

  1. Enterprise-wide adoption in data science is essential, data center buy-in, breaking down business silos, profoundly isolated data, and inaccessible documents within business units.
  2. Building reliable communication channels among teams are crucial steps to strengthening a well-built foundation through online tools and messaging services for better products and operational processing.
  3. Positive synergy means “working together seamlessly and optimistically” comes from real-team leadership, complementary talents, humility, sincerity, directness with the same values, same steadfast commitment, courage, toughness, spirit, and determination.
  4. Adopting a all-hands on deck approach and “we’re in it together” attitude helps to bridge new knowledge, strengthen bonds between teams, build trust, respect, bridge the technical and office group, and boost team spirit.
  5. Breakdown team negativity, team politics, and team bad-mouthing, and with team member openness comes growth, resilience, and flexibility.
  6. Create an environment of open communication and help to get everyone is on board. Hence, to understand the project details and what is expected by each team and deliverables documented.

The Data Science and Data Analytics Team

Two teams united by a common cause by collaborating around content-level data intelligence and data management competency.  The data center designed to empower organizations to explore big data solutions, mitigate risks, institute governance, and standardize processes.

Technical teams merge with uniquely qualified, well-groomed experts accompanied by data intelligence to help strengthen business and product lines to run more efficiently, resourcefully, competently, and predictably. A proficient, knowledgeable business center that brings experts to improve operational excellence, legacy modernization, streamline all internal/external core processes, augment digital automation, advance statistical analytics, and strengthen data governance.

A data-intelligence team – Are minds of collective wisdom of consolidated leaders with collective wisdom that share their best approaches, methods, and practices that shape the newly created data development center.

Clear-Out Business Unit Difficulties

  1. Know everyone’s competencies, shortfalls, or stumbling blocks. (strengths/weaknesses)
  2. Make sure anyone who has difficulties communicating or giving presentations get the extra help and lend a hand.
  3. Breaking down obstacles and bringing togetherness among teams by sharing good/bad experiences and success stories to get everyone to talk about their project are some of the ways to strengthen teamwork.
  4. Reward exceptional levels of achievement with team-based incentives plans to motivate and encourage productivity.
  5. Vulnerability-based trust comes from credibility, consistent follow-through, and an in-depth understanding of team members’ distinctive attributes.

Processes Analytical Problem Solving/Business Intelligence Development

Information technology leads, data analysis scientists, business process experts, functional analysts, product owners, and project managers spend their time together to make data easily digestible for creative data-driven solutions.

  1. Business analysts are invaluable and provide feedback and how to get buy-in and everyone on board. Determines requirements and recommendations – through a process of gathering and analyzing documents, organizational knowledge, and business problems.
  2. BA delivers deep insights and designs technical solutions and systems to solve issues. BA improves technology, business intelligence, operational efficiency, data analysis, processes, products, and services.
  3. Data and Digital analysts prove possible, feasible, and workable for a promising achievable outcome: key performance indicators and defined business requirements, project plan, and assignment monitoring, including budget and forecasting.
  4. Subject matter experts, data analysts, and data scientists gather and manipulate data through preparation, examine large datasets to identify trends, develop applications, reports, charts, and create visuals for COE strategic decisions.

Nearshore IT Services

From ongoing staffing needs to a rich, 24/7 onsite/nearshore model, our Data Science premium support teams are tailored specifically to meet your needs. Hire an IT resource that’s relatively within the same time zone, cost-effective and easier to access than most offshore services.

Since we’re highly proficient Data Mining and Big Data specialists, our Nearshore team can extract the critical knowledge and insights you need from your structured and unstructured data.


Nearshore IT Services

From ongoing staffing needs to a rich, 24/7 onsite/nearshore model, our Data Science premium support teams are tailored specifically to meet your needs. Hire an IT resource that’s relatively within the same time zone, cost-effective and easier to access than most offshore services.

Since we’re highly proficient Data Mining and Big Data specialists, our Nearshore team can extract the critical knowledge and insights you need from your structured and unstructured data.


Step 2 – Build Business Plan and Continuity Resiliency Plan

The organization’s COE resistance to failure is “the ability to withstand changes in its environment and still function.

Building digital processes and creating systems of recovery, prevention, and knowing the potential threats by adding redundancy – so ongoing operations are not affected, disturbed, or broken through the implementation of disaster recovery.

  • It’s imperative to create a business impact analysis plan that details a list of key experts, contact information, job functions, backup program, critical documents and COE business processes, and COE equipment.
  • It is necessary to build a resiliency plan and add alternative routes to take in case of breakdown operations.

Business Plan

“We can’t wait for someone else to do it, we have to take matters into our own hands, or it will never be done.”  Tim F. LaHaye 

Optimize network and cloud initiatives, build storage expansion initiatives, and increase digital intelligence by doing research, data exploration, and data collection.

Creating a business continuity plan acts as implementation and maintenance – vast amounts of data studied and processes streamlined with machine learning, artificial intelligence technology, and predictive analytics.

 It provides the framework to help corporations transform business data into scientific approaches. Solves market-based problems, encourages collaboration, consolidates expertise and specialized skills, standardizing the delivery process and

Step 3 – Suitable Systems, Applications, and Programming Language

  1. Python is perfect for a seamlessly brilliant programming language that deploys models, workflows, data analysis.
  2. Recommender Systems – Utilizes past behavior from products purchased with predictive and ratings used with pre-tagged characteristics that link to those items.
  3. The data scientist and data analyst choose a language the best suits their project and for managing different technical teams based on skills and expertise.
  4. Python or R is fitting for any data science project since its software is an ecosystem with a strong community.


COE Enterprise Platforms

Value-Added Center of Excellence Platform

Microsoft Power Platform Center of Excellence (COE) Starter Kit

It is easy to use software with out-of-the-box features.

  • Help rebuild business goals, data infrastructure, and deliverables with scalable digital workflows.
  • System architecture, administrative tools, new templates, personalization, and customization are critical elements of COE software.

Modules within the platform streamline analytical efforts.

  • Organize business functions, business process flow, and governance.
  • The core components built for administration, data analytics, training, onboarding, and best practices/lessons learned.

Step 4 – Defining Pain-Points

Have business units submit the group’s most significant pain points.

  • Time-waster projects or energy exhausting tasks that need special attention and analysis.
  • Where are the weaknesses, obstacles, and difficulties within the business, groups, or centers?

Define new business goals and management trends focused on business continuity, business growth, and business stability.

  1. Describe Pain Point
  2. Define the Vision and Strategy of COE
  3. Build Financial Plan, Funding and Budget
  4. Secure Resources
  5. Write down the business problems down
  6. Know what the data applications will target

Pain Points Focused On

  1. Data Processing
  2. Financial Advancement, Management
  3. Security and Protection and Safety Measures
  4. Decreased Employee Productivity
  5. Supportive documentation
  6. Quality assurance and governance
  7. Training, Best Practices
  8. Services and Customer Support
  9. Reduced Operational Efficiency
  10. Old technology, legacy systems
  11. Poor online presence, weak brand recognition
  12. Low revenue, no or low repeat orders
  13. Decreased customers, customer churn
  14. Low Digital Processing

Step 5 – Infrastructure

The data center’s support infrastructure is comprised of the IT equipment and the physical systems of the business.

  • Includes – communication, computers, telecommunications networks, facility (location), installations, and usage space.
  • Uninterruptible power sources (UPS). Power supplies (heavy load generators, redundant power supplies), heating, and ventilation.
  • HVAC systems (air conditioning) exhaust systems, and computer room.

Breakdown List

  1. IT Equipment – security devices and firewalls, storage equipment, racks, power cables, disk trays, batteries, a generator to power entire infrastructure.
  • More bandwidth than 9mb, IP blocks, cooling unit, and security-like key fobs/biometrics within the data center (is critical).
  • A log of access times (in/out), fire suppression, routers, real-time monitoring of all servers, switches, routers, and firewalls.
  • Globally accepted standards need to be met (ANSI/TIA-942 standard), appliances, technological devices
  • Backup plan for the smooth functioning of data center
  1. Budget – allocation and the planning to invest the development and financial plan.
  1. Operation Staff – Infrastructure Optimization Specialist – Works in collaboration with agency stakeholders, technical leadership to plan, structural design, and deliver modernization strategies to bring responsiveness, customer delivery services, responsiveness, improvements in IT infrastructure, cost, and capital efficiency.
  2. Cloud Integration – Know current and future needs sustainable for growth. Applications, data tools, agile, networking, code-based automation using modern software development, open-source, and industry best practices. Auto-scaling and performance/capacity management.
  3. Training / Education / Knowledge Workshop – Roadmaps, agile methodologies of the project lifecycle, best practices documentation.
  4. Good principals and procedures across multi-disciplinary teams Playbook, create persuasive case studies, lessons learned database, and coaching/sharing information, know-how, and wisdom.

The Takeaway

Data can be found everywhere in today’s corporate environment, and data is linked to everybody with the CEO. CIO is exploring new ways to improve business efficiency, forecasting, developing new digital competency, and expanding customer-facing processes. Companies are now appointing elite digital and data teams to identify new online technologies to manipulate new patterns and predict future trends.

Artificial intelligence, deep learning, and data analysis are the powerful components to building a holistic data life cycle approach for generating better decision making, profitability, operational efficiency, increased revenue, advanced data opportunities, and brings cost reduction efforts and business breakthroughs to the surface.

  1. Business analysis, data analytics, and data governance all come together to increase data consistency, decrease risk, and maximize comprehension. Creates a framework for performing statistical data analysis by collecting data, analyzing data, and processing data to generate BI reporting.
  2. Highly designed, developed, and devised centers built as an automation ecosystem for data-defined scalable approaches, business expansion, technology growth, data management efficiency, process modernization, and ethical business practices.
  3. Adding a COE Platform Enterprise Software helps to quickly build organizational workflows, reporting, and data structure, which is critically essential for initiatives, application project goals, and COE objectives.
  4. Specialized data analytical teams collect historical data, consumers, online information, sales trends, market trends, and competitor data for actionable COE project development and dissemination within the business unit and product lines.

Data Science Center of Excellence

The executive leadership management team and data science team work together to institute an enterprise-wide governance system. COE establishes the data and process foundation to build a modern digital transformation hub for organizations to tie together new online technologies intended for analyzing different elements of information.

  • Standardize processes, best practice implementation, employing data science, predictive modeling, statistics to control and gain incredible business knowledge.
  • Distinctive components of enterprise-wide data manipulation through newly built algorithms, data mining, identifying new patterns in data sets, predicting future trends, and highly developed knowledge about the environment.

How Data Analytics and Data-Driven Science Produces High ROI

Data Science Center of Excellence is at the heart of workplace change and using technology to share information, encourages contribution, and breathes new life into data analysis and data mining.

  1. Some examples of Data Science applications – Connect data mining, deep learning, and big data utilizing customer segmentation, risk assessment, sales forecasting, and marketing analysis to measure and leverage data.
  2. Improve operations, optimize security and fraud protection, improves marketing campaigns, automating recruitment process, and study purchase patterns.
  3. Principal components analysis algorithms, least squares, and polynomial fitting algorithms come together, and data flow through a centralized, advanced analytics center of intelligence with end-to-end data processing.

Data engineering, data science, and data architecture are elements of Data Science CoE models – A hub of experts that create the framework which helps improve business knowledge and gain new digital information. 

  1. By balancing data opportunities, operational performance and processes, and workplace efficiency through standardized practices, business intelligence, and predictive/prescriptive analytics.
  2. Big data, understanding machine learning algorithms and high-level programming language with data governance, and smart logistics management within business units build higher-level success through business optimization and cutting-edge technology.
  3. By establishing strategic partnerships, building complex models, data product engineering, incorporating comprehensive systems, and understanding advanced algorithms creates a robust and healthy work environment.

The importance of synergy in the business allows strategic direction within teams that work together and function cohesively, thereby moving forward to practical solutions and significant results.

  • A critical business concept – product synergy, process synergy, employee synergy, and organization synergy creates a better outcome.
  • It is vital to workplace success, business performance, employee continuity, and maximum growth.

Companies Build Various Types of COE

  1. Developmental Operational Centers is focused on services and operations to an organization.
  2. Innovation Centers are driven by new approaches, continuity strategies, and modernized products that pay attention to risks, incorporating cloud-based tools, and reducing vulnerabilities. Most COE can be developed and implemented in about 18-20 months by utilizing 10 percent of the staff from business units and who can offer in-depth company knowledge, understand processes, and have analytical expertise and data intelligence.
  3. Competency Centers that adopt revenue-driven ideas to improve the organization’s processes.
  4. Best Practices Centers strictly concentrated on improving products and processes with experts to institute best management practices for adequate operational flow.

Data Science Centers Utilize Knowledge-Based Systems

  1. It helps to find more natural ways to understand the information in a person’s personal life or business setting.
  2. It’s a concept, technique, and blended approach to sort through massive data information.
  3. Incorporates recommender systems that help to understand content-based and collaborative-based filter modeling.
  4. Unifies the plethora of business analytics, data analysis, machine learning data mining, deep learning, and big data to analyze and understand theories that result in valuable data-driven business insights.

Data Science Analytics Centers leverage data sources and a technology blueprint for securing a five-year business plan and have department champions with unique skills and expertise.

Establish new data management, architecture, advanced technologies, and data stewardship are pooled together with key COE roles from data scientists, COE lead, data steward, application experts and software engineers, and data quality analysts for a modernization framework and well-constructed infrastructure.

  • To organize and prioritize business requirements and define market research models, business analysts establish machine learning and methodologies with high-quality standards and superior program management.
  • Companies need to be able to scale up or down to achieve significant performance so the business units can survive and thrive and grow.
  • It is building reliable services and forward-looking, highly advanced products that will take the organization to the next level.

Businesses are modernizing operations, incorporating new mechanisms and new approaches that can transform their company by establishing an integrated center of excellence (COE).  

  1. A Center of Excellence reforms and strengthens operations, documents, and lessons learned through digital transformation and adopting methodologies that are balanced, proven, and useful.
  2. By balancing the system of business rules through operational integrity and holistic mindset and approach.
  3. Operational excellence focused on high performance, top competencies, innovative strategies, statistical process control to empower staff and optimize operations.
  4. Statistical techniques to control production and processes using data science, analytical tools, flowcharts for the proper business foundation to maximize critical thinking and process flow.

By forming centers of excellence, each COE business unit shares the same belief across teams. Functional areas with standardized processes and value-added services with synchronized business management as projects are monitored, maintained, and follow strict rules, practices, and corporate governance.

  1. The COE will consist of a new core pair of experts from the original data science team to create a new leadership of strategic positioning, clear vision, and where new best practices are built.
  2. Training, research, support, and the pursuit of excellence shared by data scientists and highly skilled experts are disbursed into each business unit to disseminate new knowledge, new technology, and new thinking into the organization.
  3. Business transformation creates sustainable business practices, increases competitive positioning, and helps to take action around market shifts.
  4. Organizations build enterprise system integration for centralized system interconnection, product data exchange, and electronic data interchange and allow for new innovative solutions and improved communication between people and technology.
  5. Optimizing operations for workflow modeling, business process developments, and to allow for quick decisions to be made, in addition to customer demands, identify business patterns with accurate information.
  6. It helps in responding to rising business threats and supports embracing opportunities with the right resources and right data analytics and intelligence. 

Inspiration and Motivation

Success is only as good as your leaders. Teams, center of excellence and training all come after a strong leader is able to lead with conviction and push through adversity! Teamwork, cooperation, and project collaboration constitute a winning outcome!

  1. Leaders need to inspire and motivate their teams with encouraging words, quotes, idioms, or metaphors to get everyone to feel connected to each other within the groups.
  2. Establishing leadership, building a course-plotting, guidance committee add functional and core teams for a robust foundational center.
  3. Business users, developers, and members of the operations team – create an environment to foster mentoring efforts and set the ground rules for each specialized committee and business unit team.
  4. Evangelists, subject matter experts, and trainers help the transition process go smoothly and can offer lessons learned and best practices, documentation, and methodology.

At its core, COE data offers corporations a new competitive edge – building data solutions through the advancement and adoption of data technology, automated processes, data modeling, data visualization, data analysis, and diverse systems.  Ensures deliverables are custom-made and tailored to maximize performance.

“I foresee the next wave of revenue growth in corporate America will come directly from Data Science.” ― Ken Poirot

Keep Moving Forward with Aptude

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Our support services will free up your senior IT staff from the overwhelming burden of day-to-day maintenance issues. Now they’ll have time to launch those new projects and applications you’ve been waiting for. Simply put, we can free up your resources and contain your costs. Let’s have a quick chat to discuss our exclusive services.