How can data reporting help you reduce costs?

Data reporting can help reduce costs by providing insights into inefficient processes, identifying areas for optimization, and enabling data-driven decision-making. By understanding where resources are being underutilized or wasted, organizations can streamline operations, allocate resources more effectively, and identify opportunities for cost savings.

Through data reporting and analysis, organizations can identify inefficient spending or underutilized resources. This can enable them to streamline operations and cost reduction. For example, an organization, through observing the data, may find its outreach program allocates a significant budget to advertising in an unresponsive demographic. By reallocating resources and adjusting the plan based on data insights, the organization can reduce costs while maintaining or improving program effectiveness.

Data reporting is a key way to find useful information from the data a company collects. If you've used a program like Microsoft Excel, you've already done some data reporting, maybe without knowing it. Data reporting helps businesses achieve goals like making things better, spotting unusual data, or just understanding patterns in the data. This can help in many areas like managing things the company owns, understanding what customers like, and making the process of selling better.

A well-analyzed data report can identify cost-saving opportunities by revealing inefficiencies, highlighting areas with excessive spending, and providing insights into resource utilization. It allows you to make informed decisions, optimize processes, and streamline operations based on actionable data.

Effective data reporting goes beyond visuals; it necessitates goal-setting, proper data source selection, and metric determination. Understanding what, why, and how to measure ensures alignment with business strategy and stakeholder needs, facilitating informed decision-making and strategic direction.

Data reporting tools include diverse software like Tableau, Power BI, and Google Data Studio. These tools transform raw data into visual reports, offering insights into business performance, trends, and patterns. They empower users to make informed decisions, drive strategy, and communicate findings effectively.

Integrity data is the most important word in the context of data extraction and to obtain it it is essential to use data quality techniques. and with this you will have consistency in the data and correct information.

People tend to just jump on a tool without an afterthought on how the data will be consumed. I've seen time and time again that people will create a Power BI report from a relational database, which then is turned again into a spreadsheet. There is no need for that. Before you jump to a tool, you need to analyze first how the outcome will be used. Is this a high-level report a C-level executive will consume? Power BI is your friend. Is this data you need to pour into a calculation? Consider Python.

Data reporting tools are needed for transforming data into actionable insights, fostering informed decision-making, enhancing collaboration, and promoting overall efficiency within an organization. This helps to have a data-driven culture of modern businesses.

Data reporting serves as a strategic ally in cost reduction by tracking KPIs, revealing trends, and identifying root causes of inefficiencies. It enhances decision-making through evidence-based insights, fostering confidence and credibility while minimizing guesswork and bias.

Data reporting can make cost savings through at least, three axis: 1°) Time savings: Automatisation of reports creation and information analysis, improve the time allocated to these recurring tasks, 2°) Quality and depth of analysis: By improving precision and quality of the analysis, reporting leads to take better and appropriate decisions, supported by a robust process, 3°) (Last but not least) Prioritisation: Well designed Reportings are done to focus on the most important informations (among high volumes of data) that should be monitored and controlled. Decision maker attention can be focused on these points.

Data reporting streamlines decision-making, enhances transparency, and drives performance improvements. It transforms complex data into clear insights, enabling informed actions. With visualizations and trends, it fosters collaboration and empowers stakeholders to seize opportunities, innovate, and achieve organizational goals effectively.

You make the best decisions in companies after reading the report. It is important to read the combination between KPIS, as the benefits for the vision between the different areas of the company will be better for decision making. The main benefits are the union of business need versus numbers to validate that need.

It always surprises me when people want a Power BI report on whatever KPI, only to find out that the KPI is either poorly defined or not as meaningful as previously assumed. Example: what does utilization of a tow truck really mean? Or cost per hour? And does the database have the data necessary to compute the same, or do you have to bend over backwards using SQL or Python to produce this metric?

In my experience, one of the best practices in data reporting is not to overwhelm the end-user with too much data. As a technical expert, it is exciting to be able to work with and analyze large datasets, but if the end-user is a manager who wants to grasp the most important features with a quick look, the reporting needs to be focused and crisp.

When you need to report, the most important thing is Storytelling. You must put the data organized in the report in the way you tell the history. And through this history it will be easy to draw up action plans to obtain better results

This is what a discovery call is for. What does your client need? In what way? And do they really need it? Last but not least, keep it super simple. I've been tempted here and there to throw in a super cool visualization into a Power BI report, only to self-censor or discover after a client call that they have no idea what to do with it. Even a scatter plot can be too much.

The essential path is users. There is no report if users don't know how to use it. Training and encouragement are necessary for the user to feel how important it is, day after day, in decision making.

I double-check any report I make mathematically. It takes time. However, if you rely on plausibility checks, any data can look plausible, but it will not be correct. This will erode your end users' confidence in your report. On top of this comes extensive training. The report will be second-nature to me or people in my department, but end users won't perceive it that way. A Power BI report is only as good as the % it's being used.

Um relatório de dados pode ajudar a reduzir custos, quando é construído com base em dados confiáveis para tomada de decis?o. Por este motivo, na minha vis?o, é fundamental que seja estruturado com base em dados conectados a um solido processo de data quality, com monitoramentos e implementa??es de corre??es frequentes. Caso contrário, você poderá tomar uma decis?o acreditando que está reduzindo custos quando na verdade está indo em dire??o contrária, por estar utilizando dados incorretos.

Every organization is at a war (to access resources, maximize value, gain market share etc.,) and data reporting is a strategic double-edged sword. On one hand it can reduce costs, in terms of time and money, of making business decisions and on the other hand it can increase costs because it can lead to making incorrect decisions faster. So companies can maximize the value of their investments in data reporting tool by understanding who’s going to use it, why is it necessary, and how will it be used. In other words, aligning the data reporting to the specific job/task and the strategic objectives or goals around those jobs/tasks could help organizations see through what improves decision-making and what increases other costs.