In the era of big data, the volume of data collected daily continues to expand exponentially, but collecting data is only the initial stage. Transforming that data into actionable insights that can drive business decisions is where the real value lies. Many companies struggle to make the transition from data collection to data insights. Often, this difficulty arises from a lack of expertise in data analysis and an inability to properly interpret the data. It is also critical to have the right tools and technologies in place for effective analysis and visualization of data. Without proper tools, companies risk drowning in a sea of meaningless data. It is essential to invest in the skills, tools, and technologies required to transform raw data into valuable insights that can drive business decisions.
Data analysis using targeted metrics offers critical insights into an organization’s performance and allows for informed decision-making regarding operations, products, and customers based on actionable statistics. It can also furnish vital information about market fluctuations, providing an opportunity to stay ahead of the curve and be at the forefront of industry developments. Through a comprehensive examination of crucial data points, decision-makers can make better-informed choices regarding tactics, product development, and service investments, facilitating risk management and cost reduction.
The significance of data governance
Implementing thorough data governance is a pivotal part of any business plan. The appropriate acquisition and processing of data allow for improved accuracy and timeliness, which then gives decision-makers integral information. It is essential to align internal standards with external regulations to remain compliant with the way data is collected, used, stored, and disposed of. And when developing a strategy for data governance, prioritizing security is critical, protecting both the organization’s safety and privacy, as well as the individuals from whom the data was collected.
Challenges in extracting data insights
One of the primary challenges the industry faces when seeking data insights is dealing with the overwhelming volumes of data known as big data. The influx of massive amounts of information poses the initial hurdle, requiring the identification of the most critical data before proceeding to the analysis phase. Most organizations waste countless time and money pursuing irrelevant information, resulting in subpar decision-making and misguided investments.
Integrating new systems with existing equipment or software can be challenging, but it is a necessity. Attempting to implement an incompatible program amounts to forcing a square peg into a round hole—not only is it unlikely to work, but doing so may compromise the accuracy of results and lead to faulty interpretations. Effective data governance and security are only successful when businesses integrate them properly into a functioning system. Thorough research and planning are vital in ensuring compliance with regulations and industry standards when collecting and storing data.
Another common mistake lies in interpreting the actual data itself. It is vital that organizations utilize the right tools and resources to obtain the results they are looking for, and having skilled experts proficient in analyzing complex and vast amounts of data is crucial. These experts possess the ability to discern between critical and dismissible information, enabling accurate and meaningful insights to be extracted.
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Best practices to mitigate challenges
A systematic top-down approach is essential to effectively address the most common pitfalls experienced with extracting valuable data insights. The first step is to establish goals that align with the business objectives and stakeholders’ needs. These goals will serve as a guiding force for the data collection and analysis process, ensuring the extracted insights are relevant and actionable. Meanwhile, the company can develop a robust data governance plan to ensure compliance and effective data management.
Developing an integration strategy is key. The business must obtain a comprehensive understanding of its existing systems and programs and determine how to seamlessly implement new resources and technologies. This strategy should outline the implementation timeline, specific add-ons, and deployment plans for enhancing data analysis capabilities.
Another best practice is to invest in training employees on data analysis techniques and tools that are pertinent to the organization’s objectives, arming staff with the skills to effectively analyze and interpret data. Companies need to consider involving data analysis experts who can offer specialized insights and support when dealing with complex and extensive datasets.
Leveraging the power of visualization tools can simplify the analytics process and facilitate the comprehension and communication of intricate data patterns and insights. By employing suitable visualization tools, companies enhance data exploration and understanding, enabling decision-makers to extract valuable insights more effectively.
Businesses need to establish a system for ongoing data analysis process monitoring and evaluation. This enables the timely identification of any issues or inefficiencies, allowing for prompt remediation. Regular evaluations also provide opportunities for continuous improvement and optimization of data analysis practices.
Tools for data visualization and insight
One of the simplest ways to consolidate and quickly analyze data sets is through visual representations, such as graphs, plots, charts, infographics, and more. While researching the building of a robust strategy, it’s important that companies invest time in identifying the most suitable software for their needs, both for visualization and analysis. Some options offer free trials, allowing for cost-effective experimentation to find the optimal fit.
- Apache Spark. Apache is a versatile engine that supports data engineering, data science, and machine learning operations across multiple programming languages.
- KNIME. This is a free and open-source data miner, analyzer, and integration platform that incorporates machine learning capabilities.
- Jupyter. This platform has interactive computing features, is document-centric, and uses multiple programming languages for notebooks, code, and data, allowing users to arrange workflows.
- Microsoft Power BI. Power Business Intelligence is a unified, scalable platform with an interactive collection of data software that can extrapolate data insights from seemingly unrelated sources of data, like Excel sheets and cloud-based services.
- Qlik Sense. Qlik Sense can be used as a SaaS or hybrid cloud-based solution to collect data from hundreds of sources and provides artificial intelligence-generated analytics and suggestions.
- Tableau. This is a visual end-to-end software platform that enables data prep, analysis, governance, and collaboration utilizing an intuitive drag-and-drop system.
Innovative methods are constantly in development to unbury the massive amount of data that can be extracted in practically the blink of an idea. These methods are no longer limited to multi-million-dollar corporations, but the proper tools must be put in place. Alarmingly, approximately 68 percent of data teams struggle to extract the necessary insights from reports. An example of the successful 32 percent is Walmart, which has leveraged data insights to create a new hybrid system, resulting in annual cloud spending savings of 10 to 18 percent. Had Walmart not created a development plan, obtained the right data tools, and cultivated knowledgeable analysts, the critical data collected could have been lost amongst the less vital information, and a major cost-saving opportunity would have disappeared. Proper data insights can separate a company from its competition and can make or break the organization altogether.
About the Author
Zihao Li is a senior-level expert on product and data management with additional experience as a software engineer and expertise in stakeholder engagement, financial planning, and data analysis. He holds a Master of Science degree in information science from UNC Chapel Hill. For more information, connect at LinkedIn.
