Here's how you can utilize data analytics and insights from new technology to inform your decisions.

Utilizing data analytics and insights from new technology can significantly enhance decision-making across various domains. We need to base decisions on empirical evidence rather than intuition or assumptions. Its important we analyze historical data, current trends, and implement predictive analytics to identify patterns, correlations, and insights that inform our choices.

In today's data-driven world, utilizing data analytics and new technology is key to making informed decisions. Define Objectives: Clearly define the goals and questions you want to address using data analysis. Gather Relevant Data: Collect both internal and external data sources that are relevant to your objectives. Continuously Monitor and Adapt: Regularly review and update your analytics strategies to stay ahead of changing trends. By following these steps, businesses can unlock the power of data analytics and new technology to make informed decisions that drive success and innovation.

Encuentra KPIs asociados a tu actividad, ya que no se puede mejorar lo que no se puede medir. Estos KPI no son estáticos, deben ir evolucionando y adaptándose para que los datos que aporten sirvan para mejorar las decisiones y planes de acción.

Structured Data: Data that is organized in a tabular format, such as databases and spreadsheets. Examples include customer information, sales records, and inventory data.Unstructured Data: Data that does not follow a predefined format, such as text, images, videos, and social media posts.Semi-Structured Data: Data that does not fit into traditional relational databases but has some organizational properties, such as JSON or XML files.2. Data FormatsText Files: CSV, TXTSpreadsheet Files: Excel (XLS, XLSX)Database Files: SQL, NoSQLMarkup Languages: XML, JSONBinary Files: Images (JPEG, PNG), Audio (MP3), Video (MP4)

I love to hone in on what clients are feeling by using the feedback they give by gathering them and categorizing them by theme using the data analytics tools we have. Then finding trends that my team and I can pinpoint and find solutions for.

Combining AI and powerful tools like excel and power BI, can turn very mind numbing and trivial reporting very easy. This can then be translated into real time dashboards for various function. .

Los HR Analytics incluyen muchas funcionalidades para categorizar automáticamente toda la información en intuitivos tableros de instrumentos. Estos módulos desarrollarán informes sobre diferentes aspectos en una empresa: economía de la empresa, competencias, compromiso del empleado (distribución de empleados, aprendices, condiciones de trabajo...), inversiones y actividades diarias. Gestión de recursos humanos Los sistemas de información de recursos humanos (SIRH) permiten almacenar datos de personas y documentos de todos los empleados de la empresa. Estos datos se guardan en una base de datos centralizada y de fácil acceso. El HR Analytics es capaz de procesar estas fuentes de datos.

Choosing the right technology tools is crucial. Working across 21 countries, I have seen how real-time data analysis and visualization significantly enhance decision-making. -Explore data tools:Start with Excel for basics, then try Tableau for visualization and Power BI for business intelligence. -Choose based on data complexity:Use Excel for simple sales tracking, R or Python for advanced analytics. -Ensure compatibility:Power BI integrates well with Microsoft products like Azure and Office 365. -Routine integration:Schedule weekly/monthly meetings to review action not just KPIs on dashboards, make it business relevant. -Stay updated:Regularly assess new tools and features to keep your strategy current.

Tech tools are critical when working in all fields. Basic tools available to most people for minimal financial investment are Google Sheets or Excel. There are a lot of things you can do with that and they can be used in conjunction with other tools your company has like Tableau or Slack.

As a first step, I would strongly suggest only picking tools that correspond to your data complexity and integrate well with your existing systems so that you and your team(s) can successfully use them. This will enable the data analysis process to be done smoothly, thus making data management part of your daily management routine toward better decision-making and performance tracking.

Por ejemplo la Big Data, como también se aplica en los procesos de reclutamiento. En este caso, su principal función es facilitar las tareas que se llevan a cabo en el desarrollo del mismo. Principalmente su aplicación permite a la empresa obtener información valiosa sobre el candidato.

Identifying trends in data transformed my team. Predictive analytics helped us forecast performance accurately, addressing issues & seizing opportunities. -Analyse trends: Find correlations between engagement scores & productivity. Higher engagement may correlate with increased productivity. -Tailor responsibilities: Align tasks with team members’ strengths. If data shows some excel in specific areas, assign tasks leveraging their strengths. -Guide decisions: Make data-driven decisions on team development & resource allocation. -Enhance collaboration: Use 1-1 meetings & psychometric assessments to understand strengths & improve task allocation. Assign partners/ buddies based on objective insights for enhanced teamwork.

Insightful Data: Turning Analysis into Actionable Strategies Unlocking the full potential of data means going beyond mere numbers. It involves deep dives into the nuances of team dynamics, identifying subtle shifts that reveal both opportunities and risks. For example, harnessing data to tailor roles around individual strengths not only optimizes performance but also enhances job satisfaction. Regularly revisiting data ensures strategies remain agile and responsive. Anomalies should trigger proactive measures, preventing small issues from becoming significant. Leaders who excel in data literacy foster environments where continuous improvement and innovation thrive, driving organizational success. What do you think? Let me know!

One thing I found beneficial is to develope a team of data analyst and data scientist who can interpret data and provide actionable data. This will help improve team performance and increase productivity .

1. Consider a scenario where a team leader notices a drop in productivity and an increase in conflicts among team members. By analyzing communication patterns using data analytics tools, the leader discovers that certain team members are frequently excluded from important discussions. Armed with this insight, the leader takes steps to encourage more inclusive communication, leading to improved collaboration and a more harmonious team dynamic.

2. Imagine a project manager who wants to optimize their team's performance. By analyzing collaboration metrics, such as how often team members interact and the nature of their interactions, the manager discovers that certain team members are consistently overloaded with work while others have capacity to spare. Using this data, the manager decides to redistribute tasks more evenly, leading to a more balanced workload and improved overall performance.

El People Analytics en RRHH nos va permitir tambien identificar las habilidades y competencias del talento con el que contamos y como bien desarrollado impulsará a la organización al éxito, de modo que se definan y clasifiquen con precisión los perfiles de valor para la empresa.

You can, for instance, analyze the open collaboration tools to find out the frequency of team members communicating among themselves and even detect what causes the communication bottlenecks. If data shows they don't work with each other as much as teams, maybe team-building activities or partnering them up will help encourage such behaviour. In addition, if the statistics based on project outcomes dictate that smaller teams produce better results, then this approach can be used to reorganize larger teams into smaller units that are more focused. Use these insights and leadership intuition to enhance the performance and cohesiveness of the team.

Once you’ve collected the general data, you can then have a cleared clearer picture of next steps to start mapping out workflows and steps to improve processes or implement new ones.

Para implementar People Analytics de forma correcta hay que seguir una serie de etapas: identificar los objetivos empresariales y de los empleados, definir métricas y KPIs relevantes, elegir las herramientas adecuadas para recopilar datos, analizar esos datos para tomar decisiones en base a ellos y evaluar los resultados y hacer ajustes si es necesario.

Analiza los datos en solitario y posteriormente en grupo, ya que de esa forma se unifican distintos criterios de lo que se está interpretando y puede ayudar a ver la necesidad de modificarse algún KPI o informe de datos.

Identify Goals: Clearly define the business or research objectives.Stakeholder Consultation: Discuss with stakeholders to understand their requirements and expectations.2. Data CollectionData Sources: Identify relevant data sources (internal databases, surveys, web scraping, third-party data, etc.).Data Gathering: Collect data using appropriate methods (ETL processes, APIs, manual collection).Ensure Quality: Verify the accuracy and completeness of the collected data.3. Data PreparationData Cleaning: Handle missing values, remove duplicates, correct errors, and normalize data.Data Transformation: Convert data into a suitable format for analysis (e.g., converting categorical variables into numerical ones)

Continues learning is your self-motivated persistence in acquiring knowledge and competencies in order to expand your skill set and develop future opportunities. It forms part of your personal and professional development in an effort to avoid stagnation and reach your full potential.

Commit to continuous learning in data analytics through participation in regular industry webinars and training exercises. Implement new tools, for example, advanced analytics software, and gather feedback from your team(s) on its efficacy. For instance, if new software or a tool can identify the need for time management skills, have workshops and gather responses from your team on the impact they have had in realizing this need. Change strategies depending upon this feedback. This approach will ensure your management level remains current, dynamic, and responsive, developing a culture of continuous improvement.

People are complex and sometimes unpredictable and they can't always easily be categorised into numbers and figures on a spreadsheet. Important to take into consideration when using data analytics to inform any decisions that involve people!

There are a few drawbacks to utilizing data analytics (in its entirety) for an organization that is new to it. It might be resource-intensive because time and resources must be directed to understanding the basics. There might be a skill gap, requiring extra training. Incorporating new tools into the existing system can become complicated and disruptive. Managing massive volumes of data can lead to data overload. There is the risk of misinterpretation, giving rise to poor decision-making. Advanced tools could be expensive, and overreliance on data may dismiss human intuition. Finally, handling sensitive data raises privacy and ethical concerns, which should be addressed prior to any implementation and further development.