Tag Archives: organizations


One of the major challenges organizations face during digital transformation is dealing with legacy systems and information silos that have built up over time. Legacy systems refer to old software and architectures that organizations have relied on for many years but may now be holding them back. Information silos occur when different parts of an organization store data separately without any connection or standardization between the silos. This can create data management challenges and inhibit collaboration.

There are several strategies organizations can take to address legacy systems and silos during their digital transformation journey. The key is to have a plan to gradually modernize frameworks and break down barriers in a systematic way. Here are some recommendations:

Start with mapping and assessments. The first step is to conduct a thorough mapping and assessment of all existing legacy systems, applications, databases, and information silos across the organization. This will provide visibility into what technical and information debts exist. It can identify areas that are most critical to prioritize.

Define a target architecture. With a clear understanding of the current state, organizations need to define a target or future state architecture for how their IT infrastructure and information management should operate during and after the transformation. This target architecture should be aligned to business goals and incorporate modern, flexible and standardized practices.

Take an incremental approach. A “big bang” overhaul of all legacy systems and silos at once is unrealistic and risky. Instead, prioritize the highest impact or easiest to upgrade systems and silos first as “proof of concept” projects. Gradually implement changes across different business units and functions over time to minimize disruption. Automating migrations where possible can also reduce manual effort.

Embrace application rationalization. Many organizations have accumulated numerous duplicate, overlapping or unused applications over the years without removing them. Rationalizing applications involves identifying and consolidating redundant systems, retiring older ones no longer in use, and standardizing on a core set of platforms. This simplifies the IT landscape.

Adopt API-led integration strategies. To break down information silos, application programming interfaces (APIs) can be used to create standardized connector points that allow different databases and systems to exchange data seamlessly. This facilitates interoperability and data-sharing across organizational boundaries. Master data management practices can also help consolidate redundant records.

Focus on data and analytics. A major goal of digital transformation is to unlock the value of organizational data through advanced analytics. This requires establishing standardized data governance policies, taxonomies, schemas and data lakes/warehouses to aggregate data from various sources into usable formats. Robust BI and analytics platforms can then generate insights.

Leverage cloud migration. Public cloud platforms such as AWS, Azure and GCP offer scalable, pay-per-use infrastructure that is easier to update compared to on-premise legacy systems. Migrating non-critical and new workloads to the cloud is a practical first step that drives modernization without a “forklift” upgrade. This supports flexible, cloud-native application development as well.

Use DevOps and automation. Adopting agile methodologies like DevOps helps break down silos between IT teams through practices like continuous integration/delivery (CI/CD) pipelines. Automating infrastructure provisioning, testing, releases and monitoring through configuration files reduces manual efforts and speeds deployment of changes. This enables rapid, low-risk development and upgrades of existing systems over time.

Train and reskill employees. Digital transformation inevitably causes disruptions that impact roles. Organizations must reskill and upskill employees through training programs to gain qualifications relevant to emerging technologies. This eases adoption of new tools and ways of working. Change management is also vital to guide employee mindsets through transitions and keep motivation high.

Monitor and course-correct periodically. A digital transformation is an ongoing journey, not a one-time project. Organizations need to continuously monitor key metrics, assess progress towards objectives, and adjust strategies based on lessons learned. Addressing legacy and silo issues is never fully “complete” – the focus should be on establishing evolutionary processes that can regularly evaluate and modernize the underlying IT architecture and information flows.

Tackling legacy systems and silos is a massive challenge but essential for digital transformation success. The strategies outlined here provide a systematic, incremental approach for organizations to gradually modernize, simplify and break down barriers over time. With ongoing commitment, monitoring and adjustments, it is very possible for companies to effectively transition even highly entrenched technological and organizational legacies into more agile, data-driven digital operations.


Healthcare organizations can support nurses in developing cultural competence through a variety of educational initiatives, trainings, and resources. Cultural competence is an important skill for nurses to possess as it allows them to better understand and care for patients from diverse cultural backgrounds. Developing cultural competence is an ongoing process that requires continuous efforts from both individual nurses as well as support from their employer organizations. Some key ways that healthcare organizations can support nurses include:

Providing mandatory cultural competence training programs. Organizations should require all nurses to complete annual cultural competence trainings. These trainings can educate nurses on common cultures and beliefs of patient populations, health literacy and health disparities, effective communication strategies, and biases and stereotypes to avoid. The trainings should be evidence-based and involve interactive activities like case studies and role plays to apply the learning. Videos, written materials, and online modules can supplement in-person trainings. Competency assessments after each training can ensure nurses understand the content.

Facilitating ongoing educational opportunities. Beyond baseline trainings, healthcare organizations should offer continuous educational opportunities for nurses to further develop their cultural competence skills. Things like grand rounds, journal club discussions, continuing education workshops and seminars allow nurses opportunities to learn about new issues or dive deeper into topics. Partnering with local cultural community centers can provide educational experiences for nurses to learn directly from diverse patient advisors. Supporting nurses’ attendance at relevant conferences also aids in lifelong learning.

Providing translation and interpretation services. Effective communication is key to providing culturally competent care but is challenging without proper language supports. Organizations need to offer qualified medical interpretation services in the top languages of their patient populations, both in-person and via telephone. Translation of common patient materials into these languages is also important. Training nurses on how to access and utilize interpretation services appropriately is necessary. Interpreters should also receive ongoing education to ensure high quality, nuanced interpretations.

Conducting organizational cultural assessments. Healthcare organizations need insight into their own practices, policies and initiatives through cultural assessments. Surveying nurses, patients and families can identify areas where the organizational culture may unintentionally prioritize certain groups. Assessments should examine things like representation of diverse cultures in leadership, marketing materials, quality metrics tracked, and community outreach efforts. Insights can guide the development of inclusive strategic plans and quality improvement projects.

Integrating cultural competence into operations. For nurses to enact their cultural competence skills, organizations need to operationalize these values throughout their systems. This involves things like incorporating standards related to health equity, bias-free communication and cultural adaptation of care into nursing competencies and performance evaluations. Collecting sociocultural data allows customized care plans, and capturing quality metrics stratified by factors like race/ethnicity identifies disparities. Translation of standardized screening tools and decision support tools also supports culturally competent care delivery.

Providing resources and support for individual growth. Organizations should offer nurses tools and encouragement for their own cultural learning beyond what is required. Things like access to an online library of multicultural resources, reimbursement for cultural community events, and paid time off for cultural exposure trips communicate the importance of life-long individual development. Mentoring programs pairing experienced nurses with those wishing to further their skills aids sharing of best practices. Recognition awards for nurses demonstrating exemplary cultural competence further incentivize the commitment to growth.

Partnering with local community groups. Collaborating with diverse patient advocacy groups, religious organizations, and cultural centers allows bidirectional relationship and trust building between healthcare facilities and the populations served. This helps keep an organization grounded in community needs and priorities. Involving community advisors in trainings, materials review and quality initiatives infuses local expertise. Supporting community health workers and promotoras brings essential cultural navigation supports directly into care. Joint community health events help normalize healthcare while introducing it in culturally meaningful ways.

The implementation of robust, multi-pronged strategies as described provides layered supports enabling nurses to continually strengthen their cultural competence capabilities. When healthcare organizations fully integrate these philosophies and best practices into their cultures, structures and partnerships, it demonstrates commitment to equitable, community-centered care for all patients. With guidance and empowerment from their employers, individual nurses are better positioned to respectfully and effectively care for an increasingly diverse population.


Data issues: One of the biggest hurdles is obtaining high-quality, relevant data for building accurate predictive models. Real-world data is rarely clean and can be incomplete, inconsistent, duplicated, or contain errors. Premises must first invest time and resources into cleaning, harmonizing, and preparing their raw data before it can be useful for analytics. This data wrangling process is often underestimated.

Another data challenge is lack of historical data. For many types of predictive problems, models require large volumes of historical data covering many past examples to learn patterns and generalize well to new data. Organizations may not have accumulated sufficient data over time for all the variables and outcomes they want to predict. This limits what types of questions and predictions are feasible.

Technical skills: Building predictive models and deploying analytics programs requires specialized technical skills that many organizations do not have in-house, such as data scientists, predictive modelers, data engineers, and people with expertise in machine learning techniques. It can be difficult for groups to build these competencies internally and there is high demand/short supply of analytics talent, which drives up costs of outside hiring. Lack of required technical skills is a major roadblock.

Model interpretation: Even when predictive models are successfully developed, determining how to interpret and explain their results can be challenging. Machine learning algorithms can sometimes produce “black box” models whose detailed inner workings are difficult for non-experts to understand. For many applications it is important to convey not just predictions but also the factors and rationales behind them. More transparent, interpretable models are preferable but can be harder to develop.

Scaling issues: Creating predictive models is usually just the first step – the bigger challenge is operationalizing analytics by integrating models into core business processes and systems on an ongoing, industrial scale over time. Scaling the use of predictive insights across large, complex organizations faces hurdles such as model governance, workflow redesign, data integration problems, and ensuring responsible, equitable use of analytics for decision-making. The operational challenges of widespread deployment are frequently underestimated.

Institutional inertia: Even when predictions could create clear business value, organizational and political barriers can still impede adoption of predictive analytics. Teams may lack incentives to change established practices or take on new initiatives requiring them to adopt new technical skills. Silos between business and technical groups can impede collaboration. Also, concerns about privacy, fairness, bias, and the ethics of algorithmic decisions slowing progress. Overcoming institutional reluctance to change is a long-term cultural challenge.

Business understanding: Building predictive models requires close collaboration between analytics specialists and subject matter experts within the target business domain. Translating practical business problems into well-defined predictive modeling problems is challenging. The analytics team needs deep contextual knowledge to understand what specific business questions can and should be addressed, which variables are useful as predictors, and how predictions will actually be consumed and used. Lack of strong business understanding limits potential value and usefulness.

Evaluation issues: It is difficult to accurately evaluate the true financial or business impact of predictive models, especially for problems where testing against real future outcomes must wait months or years. Without clear metrics and evaluation methodologies, it is challenging to determine whether predictive programs are successful, cost-effective, and delivering meaningful returns. Lack of outcome tracking and ROI measurement hampers longer-term prioritization and investment in predictive initiatives over time.

Privacy and fairness: With the growth of concerns over privacy, algorithmic bias, and fairness, organizations must ensure predictive systems are designed and governed responsibly. Satisfying regulatory, technical, and social expectations regarding privacy, transparency, fairness is a complex challenge that analytics teams are only beginning to address and will take sustained effort over many years. Navigating these societal issues complicates predictive programs.

Budget and priorities: Establishing predictive analytics programs requires substantial upfront investment and ongoing resource commitment over many years. Competing budget priorities, lack of executive sponsorship, and short-term thinking can limit sustainable funding and priority for long-term strategic initiatives like predictive analytics. Without dedicated budget and management support, programs stagnate and fail to achieve full potential value.

Overcoming these common challenges requires careful planning, cross-functional collaboration, technical skills, governance, ongoing resources, and long-term organizational commitment. Those able to successfully address data, technical, operational, cultural and societal barriers lay the foundation for predictive success, while others risk programs that underdeliver or fail to achieve meaningful impact. With experience, solutions are emerging but challenges will remain substantial for the foreseeable future.


International standards organizations can play a crucial role in developing governance frameworks and best practices to help regulate artificial intelligence technologies responsibly on a global level. As AI continues to advance rapidly and become integrated into more applications and workflows worldwide, it is important to establish common standards to address concerns around safety, fairness, transparency, accountability and human rights.

Standards development organizations like the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the International Telecommunication Union (ITU) bring together experts from industry, government, academia and civil society to work on consensus-driven standards. They have the ability to facilitate discussions between stakeholders from different nations and cultural perspectives. By leveraging this multistakeholder approach, international AI standards can help align regulations and build trust globally in a way that reflects diverse societal values.

Some areas where international AI standards could provide guidance include establishing common frameworks for:

Algorithmic accountability and auditing methods. Standards can outline best practices for documenting design processes, implementing oversight mechanisms, detecting biases and ensuring systems behave as intended over their entire lifecycles. This helps ensure those developing and applying AI are accountable for any social and economic impacts.

Data governance and management. Common standards around data collection methods, personal information protection, documentation of data sources and ongoing monitoring of data distributions can help address privacy, surveillance and social discrimination concerns that might emerge from large datasets.

Transparency into AI system decision-making. Requirements for explaining model inputs/outputs, flagging uncertain predictions and disclosing limitations can help users understand what an AI system can and cannot do. Technical standards specifying explanation formats and human-interpretable justifications facilitate oversight.

Risk assessment and mitigation protocols. Circumscribing when an impact assessment should be conducted, what types of risks to examine (job disruptions, safety, bias etc.) and mitigation strategies can minimize unintended consequences before systems are widely adopted.

Human oversight of high-risk applications. Critical domains like healthcare, education, criminal justice or welfare require human review of significant AI decisions. Standards specifying oversight roles, skills qualifications and intervention procedures can maximize benefits while preventing individual harm.

Validation and certification processes. Common testing methodologies, benchmark datasets and certification schemas give users confidence that systems meet standards of reliability, robustness and fairness before use in real-world, high-stakes scenarios. This encourages responsible innovation.

Transnational data sharing. Agreeing on baseline privacy andconsent standards facilitates international collaboration on medical, scientific and public policy challenges that benefit from large, multinational datasets while preventing exploitation.

ISO and IEC are already working on standards for fairness in machine learning, AI concepts and terminology, data quality assessment and model performance evaluation through Technical Committee ISO/IEC JTC 1/SC 42 on Artificial Intelligence. Other standards under development focus on bias, explainability, auditability and more. The ITU has created focus groups examining ethics, AI applications for good and the environmental impact of technologies.

Developing enforceable international AI regulations will certainly require cooperation between governments. But standards provide a starting point by codifying non-binding best practices. By bringing together diverse views, they can gain broader acceptance than rules unilaterally imposed. And standards encourage continuous improvement, allowing practices to evolve alongside fast-paced technologies.

With participation from AI developers, governments, civil society groups, domain experts and others, international standards offer a framework for addressing cross-border challenges like dis/misinformation, cybersecurity threats, facial recognition abuses and more. By outlining governance procedures, they build institutional capacities and establish mutual obligations between nations. They help foster responsible global development and application of these powerful technologies to benefit humanity.

International standards organizations are well positioned to play a leading role in developing universal guidelines and governance models for using and developing AI responsibly. Their multistakeholder, consensus-driven processes can harmonize regulations worldwide and drive accountability by promoting transparency, oversight, and shared best practices. AI standards established through these venues lay important groundwork to help maximize AI’s benefits and safeguard against unintended social and economic consequences on a global scale.


Encourage continuous learning and skills development through various training programs. Organizations should offer a wide range of formal and informal training opportunities to help employees consistently upgrade their skills. This can include technical skills training, leadership development programs, soft skills or professional certification training. Training should not just be limited to when employees are first hired but made available throughout their careers. Integrating continuous skills development into the company culture helps motivate employees to keep learning.

Implement tuition reimbursement or educational assistance benefits. Offering financial assistance to employees who want to pursue further education makes lifelong learning more attainable. This could cover costs of degrees, courses, certifications or other programs taken externally that align with employees’ career goals and the organization’s needs. Having educational benefits demonstrates the company’s commitment to investing in employees’ career advancement and future employability.

Use mentoring and coaching programs. Pairing junior or mid-level employees with senior leaders and managers for career guidance fosters skills transfer within the organization. Mentors can help mentees gain new perspectives, provide advice, share lessons learned and recommend on-the-job development opportunities. Mentees benefit from the career-tracking experience while organizations retain and develop talent from within using existing expertise. Regular check-ins keep the development process ongoing.

Offer rotational or stretch assignment opportunities. Moving employees laterally or vertically into new roles across departments or functions presents chances to broaden skillsets. Temporary project work, special task forces or interim management roles allow testing strengths in different contexts. While challenging existing abilities, such rotations prevent skills stagnation and encourage skills renewal, important for lifelong learning mindsets. Organizations benefit from a more multi-skilled, adaptable workforce as well.

Conduct skills mapping and gap analyses. Understanding employees’ current qualifications and identifying skill areas needing improvement helps create targeted development plans. Comparing competencies against emerging job requirements due to changing markets or technologies highlights potential skills obsolescence risks. Regular skills assessments and discussions with individuals keep development goals relevant and addressed proactively through appropriate training interventions.

Promote self-directed learning and development. Provide resources and encourage personal responsibility for skills currency. For example, enable access to online courses and learning portals, offer subscriptions to industry publications, or approve conference attendance based on career-relevant topics. Supporting self-study shows commitment to empowering lifelong learner identities. It also supplements formal training and knowledge stays fresh with flexibility to explore new trends and ideas independently based on personal curiosity.

Tie development goals to performance management and career planning. Incorporating continual skills enhancement goals set jointly by managers and direct reports into annual performance reviews ties it to career progression expectations. Development goals then carry real consequences if left unaddressed rather than remaining abstract intentions. Tracking goal completion over time and linking it to compensation decisions or promotions makes the effort worthwhile. This ongoing integration reinforces skills optimization as necessary for long-term career marketability and success within the organization.

Strategically link skills growth to organizational needs. Anchor development goals to both individual career aspirations and where the company foresees facing future challenges. This ensures targeted skills stay relevant and employees maintain flexibility to transition internally, while supporting the organization’s changing demands. Organizational strategies, marketplace insights and industry trends help determine priority growth areas to focus training dollars on, such as AI, analytics, customer engagement or strategic thinking. Purposeful skills alignment promotes career management and workforce planning cohesiveness.

Create a learning culture through leader role modeling and support. Leaders play the biggest role in shaping attitudes that learning is an ongoing priority, not just an intermittent requirement. By participating in development themselves, leaders encourage continuous learning through their own example setting and willingness to adapt. Taking risks in new areas and soliciting feedback also demonstrates a growth mindset to emulate. Leaders who support employees’ time and resources dedicated to growth activities further reinforce the cultural value of skills optimization.

Implementing strategies focused on diverse training opportunities, ongoing skills assessments, flexible development planning, performance management integration, and emphasizing self-directed learning fully embedded in career management fosters dynamic, lifelong skills development cultures within organizations. A learning-centric approach keeps both individuals and the business continually advancing and future-ready in changing times.