What do you do if your data analytics performance goals are falling short?

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As a data analyst, you are expected to deliver accurate, timely, and actionable insights from data. But what if your data analytics performance goals are falling short? How do you identify the root causes, address the gaps, and improve your results? In this article, we will share some practical tips and best practices to help you overcome data analytics challenges and achieve your objectives.

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Muhammad Hamza

Logistics & Supply Chain Analyst
Shubham Savaj

Co-Founder of Dataflex Analytics || Hiring Sales Executive

1 Assess your data quality

One of the most common reasons for poor data analytics performance is low data quality. Data quality refers to the accuracy, completeness, consistency, timeliness, and relevance of your data. If your data is inaccurate, incomplete, inconsistent, outdated, or irrelevant, it will affect your analysis and lead to erroneous or misleading conclusions. To assess your data quality, you need to perform regular data audits, validations, and cleansing. You can use tools like data profiling, data quality rules, data quality dashboards, and data quality reports to identify and fix data quality issues.

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Wilfredo Alpirez

Senior Data Engineer |Data Scientist|AWS|Azure|Backend Engineer|Python|Snowflake|SQL|LLMS|Data Driven|
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Una de las razones más comunes del bajo rendimiento del análisis de datos es la baja calidad de los datos. La calidad de los datos se refiere a la precisión, integridad, coherencia, puntualidad y relevancia de los datos. Si sus datos son inexactos, incompletos, inconsistentes, desactualizados o irrelevantes, afectarán su análisis y conducirán a conclusiones erróneas o enga?osas. Para evaluar la calidad de los datos, debe realizar auditorías, validaciones y limpiezas periódicas de los datos. Puede utilizar herramientas como la generación de perfiles de datos, las reglas de calidad de datos, los paneles de calidad de datos y los informes de calidad de datos para identificar y solucionar problemas de calidad de datos.

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David Heraty

Senior Commercial Manager | Oracle Advisory | Oracle Cloud Solutions Partner
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Exploring alternative approaches or incorporating advanced techniques that may be better suited to the complexities of your datasets. Stay informed about the latest advancements in data analytics methodologies. Regularly revisiting and refining your data analytics methods ensures that you adapt to changing data landscapes and evolving industry best practices.

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Sabelo Mthembu

Continous Improvement | Lean Six Sigma | Data Analyst | Business Analyst | Business Process Management| BPMN | SDLC Methodologies| Scrum Master | Power BI | Tableau | SQL | Project Manager| Machine Learning| C2C
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If your performance metrics are falling short, particularly in terms of data quality, it's essential to conduct a thorough examination of the data you're working with. Look for any potential biases that might not have been considered when establishing your performance benchmarks. Additionally, investigate whether any significant events have occurred that could have led to data drift, potentially impacting your overall output. Collaborate with subject matter experts to scrutinize the data together and identify the root cause of the problem. It's crucial to validate these findings with further data analysis to ensure accuracy.

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2 Review your data analytics methods

Another possible reason for suboptimal data analytics performance is inappropriate or outdated data analytics methods. Data analytics methods are the techniques and tools you use to collect, process, analyze, and visualize your data. Depending on your data type, size, complexity, and business question, you need to choose the most suitable data analytics methods to achieve your goals. For example, you might need to use descriptive, diagnostic, predictive, or prescriptive analytics, or a combination of them. You might also need to use different statistical, machine learning, or deep learning models, or different data visualization tools. To review your data analytics methods, you need to evaluate their effectiveness, efficiency, and validity. You can use criteria like accuracy, precision, recall, speed, scalability, and interpretability to compare and optimize your data analytics methods.

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Shubham Savaj

Co-Founder of Dataflex Analytics || Hiring Sales Executive
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- Assess your data analytics methods for effectiveness, efficiency, and validity. - Choose the most suitable methods based on data type, size, complexity, and business questions. - Consider descriptive, diagnostic, predictive, or prescriptive analytics, as well as statistical, machine learning, or deep learning models. - Evaluate criteria such as accuracy, precision, recall, speed, scalability, and interpretability to optimize your methods.

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3 Align your data analytics goals with business goals

A third reason for poor data analytics performance is misalignment or disconnect between your data analytics goals and business goals. Data analytics goals are the specific, measurable, achievable, relevant, and time-bound outcomes you want to achieve from your data analysis. Business goals are the overarching, strategic, and long-term objectives of your organization. If your data analytics goals are not aligned with your business goals, you will not be able to deliver value or impact from your data analysis. To align your data analytics goals with business goals, you need to understand the business context, problem, and needs. You also need to communicate and collaborate with your stakeholders, clients, and users to define and prioritize your data analytics goals.

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4 Enhance your data analytics skills and knowledge

A fourth reason for poor data analytics performance is insufficient or outdated data analytics skills and knowledge. Data analytics skills and knowledge are the abilities and expertise you need to perform data analysis effectively and efficiently. Data analytics skills and knowledge include technical skills, such as programming, data manipulation, data modeling, data visualization, and data storytelling. They also include soft skills, such as critical thinking, problem-solving, communication, and collaboration. To enhance your data analytics skills and knowledge, you need to keep learning and updating yourself with the latest trends, tools, and best practices in data analytics. You can use online courses, books, blogs, podcasts, webinars, and communities to learn and grow as a data analyst.

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5 Seek feedback and support

A fifth reason for poor data analytics performance is lack of feedback and support. Feedback and support are the inputs and assistance you receive from others to improve your data analysis. Feedback and support can come from your peers, managers, mentors, coaches, or external experts. They can provide you with constructive criticism, suggestions, guidance, encouragement, and recognition. To seek feedback and support, you need to be open and receptive to different perspectives and opinions. You also need to ask for help when you encounter challenges or difficulties. You can use tools like surveys, interviews, reviews, and ratings to collect and analyze feedback and support.

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6 Monitor and measure your data analytics performance

A sixth reason for poor data analytics performance is lack of monitoring and measurement. Monitoring and measurement are the processes of tracking and evaluating your data analysis results and outcomes. Monitoring and measurement can help you identify your strengths and weaknesses, assess your progress and impact, and adjust your actions and strategies. To monitor and measure your data analytics performance, you need to define and use relevant key performance indicators (KPIs), metrics, and benchmarks. You also need to use tools like dashboards, reports, and charts to visualize and communicate your data analytics performance.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Muhammad Hamza

Logistics & Supply Chain Analyst
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If data analytics performance goals are falling short, I would conduct a thorough analysis to identify specific areas of underperformance. This involves reviewing data quality, assessing the effectiveness of analytical models, and evaluating the efficiency of processes. Implementing targeted training or professional development initiatives can address knowledge gaps or skill deficiencies. Adjusting goals to be more realistic or breaking them into smaller, achievable milestones may also be considered. Regularly reassessing and adapting strategies, along with maintaining open communication, is crucial to overcoming challenges and meeting data analytics performance goals.

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Peter Fassan

Process Mining Consultant
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Technological limitations could also be a root cause for falling short of data analytics performance goals. When addressing technological limitations as a potential cause, it's essential to consider various aspects of the existing technology infrastructure and tools. This examination includes evaluating factors such as infrastructure scalability, processing power and speed, data storage and accessibility, compatibility and integration, tool suitability and versatility, data governance and security, as well as user experience and adoption.

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What do you do if your data analytics performance goals are falling short?

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1 Assess your data quality

2 Review your data analytics methods

3 Align your data analytics goals with business goals

4 Enhance your data analytics skills and knowledge

5 Seek feedback and support

6 Monitor and measure your data analytics performance

7 Here’s what else to consider

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