Earlier, we looked at the importance of using Business Intelligence in a business setting, as well as 6 critical steps to becoming a data-driven business. We had quite a bit of interest and feedback from these articles, which prompted our team to outline some key terminology readers should know.
Before venturing into the world of data analytics, knowing and understanding these key terms is pretty helpful.
Metric: A metric is a standard of measurement used to quantify or assess performance, attributes, or behaviour in various contexts. Metrics are fundamental in analytics, helping businesses, researchers, and professionals evaluate efficiency, effectiveness, progress, or quality. They are usually defined and tracked to achieve specific goals or to compare against benchmarks. Metrics can be simple, like counting the number of visitors to a website, or complex, involving multiple data points and formulas. Effective metrics are relevant, measurable, and directly tied to strategic objectives.
KPI (Key Performance Indicator): A Key Performance Indicator (KPI) is a measurable value that demonstrates how effectively an organization achieves key business objectives. High-level KPIs may focus on the overall performance of the business, while low-level KPIs may concentrate on processes in departments such as sales, marketing, HR, or operations. Effective KPIs are actionable, providing clear indicators for decision-making and helping guide organizational strategies. They are foundational for benchmarking and monitoring progress and performance.
Dashboard: A dashboard in data analysis is a visual display of important information needed to achieve objectives, consolidated and arranged on a single display so it can be monitored at a glance. Dashboards are used to present large amounts of data in an easily digestible format through the use of charts, graphs, and tables. They provide real-time updates and insights, allowing users to quickly assess performance metrics and key data points relevant to their specific tasks or projects. This tool is essential for making informed decisions, as it highlights trends, comparisons, and patterns in the data. Dashboards are widely used in various industries to monitor business processes, track key performance indicators (KPIs), and manage resources.
Forecasting: Forecasting is about predicting future events based on historical data. It uses various statistical tools and models to estimate outcomes in areas such as sales, weather, economic trends, etc. Effective forecasting helps organizations plan operations, allocate resources efficiently, and make informed decisions to mitigate risks. The accuracy of forecasts depends significantly on data quality, the appropriateness of the models used, and understanding the variables influencing the outcomes. Forecasting is a fundamental component of strategic planning in business, logistics, and other fields.
ETL (Extract, Transform, Load): The process of extracting the data from the data source(s), transforming it to the proper structure, and loading it into an analysis system.
Data type: A data type defines what kind of value a column or a field can hold: integer data, text data, date and time data, binary strings, and so on.
Database: A database is a collection of information that is organized to allow users to easily access, update and manage information.
Data model: A data model defines how data are connected to each other and how they are processed and stored inside an information system. The data model should capture all the entities, relationships and, ultimately, the flow of data. A data model is a conceptual representation of the data objects, the associations between different data objects, and the rules which govern operations on the data. It serves as a blueprint for designing and constructing a database and ensures that all data objects required by the database are completely and accurately represented. Data models help define how data is connected, how it will be stored and retrieved, and what data is important for the business processes. They can vary from simple designs for a small database to highly complex structures for large enterprise systems.
Data governance: The overall management of the availability, usability, integrity, consistency, and security of data used in an organization. Data governance includes the people, process and information technology required for creating consistent and appropriate handling of an organization’s data throughout the business.
Big data: Big data refers to structured and unstructured data sets that are so huge and intricate that traditional data processing software cannot deal with them.
Drill-down: A method of exploring multidimensional data by going from one level of detail to the next, depending on the granularity of the data.
Query (computing): A query is a request for data or information from a database. Most queries start with a simple business question such as ‘How many clients do I have that spent above $x amount during the last 2 years?’ which is then transformed into a machine-readable format (such as SQL).
Insights: Insights are the value obtained through the use of data analytics. Insights can be gained through data analytics to help businesses identify areas of opportunity and growth.
Structured Query Language (SQL): SQL is a standardized language for managing, housing, transforming, and retrieving data from databases.
If you are considering a data-driven approach to your business, Enkel can help you propel forward with confidence. Contact us today to learn more about our accounting and data analytics service.
