Data analytics helps companies gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build related digital products, optimize operations, and increase employee productivity.
These goals differentiate data analysis from similar disciplines like business analytics and data science. Business analytics is a form of data analytics that is only used by businesses. Data science and analytics solve problems through deeper learning and strategic oversight. In this article, readers can dive deep into courses in data analytics.
History of data analysis
The use of data analytics by businesses can be found as far back as the 19th century when Frederick Winslow Taylor initiated time management exercises. Another example is when Henry Ford measured the speed of assembly lines. In the late 1960s, analytics began receiving more attention as computers became decision-making support systems.
Data analytics work function
Courses in data analytics involve a series of steps to give an accurate analysis. While performing these steps, data analysts include data scientists and data engineers to create data pipelines or help set up models.
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Data collection
There are two ways to practice data collection. The first approach is to identify the data you need for the analyses and assemble it for use. If the data are from different source systems, the data analyst would have to combine the different data using data integration routines. But in some cases, the data needed might just be a subset of a data set. The data analyst would include a series of steps to extract the relevant subset and move it to a separate compartment in the system. Doing this allows one to analyze the subset without affecting the overall data set easily.
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Adjusting data quality
The next step is finding and correcting data quality problems in the collected data. It also entails setting up the data for the analytical model according to corporate standards. Data quality problems include inconsistencies, errors, and duplicate entries. They are resolved by running data profiling and data cleansing tasks. The data analysts also manipulate and organize the data according to the requirements of the analytical model they intend to use. The final task in data quality is implementing data governance policies. These policies ensure the data is used correctly and is according to corporate standards.
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Building and analytic model
Moving forward, the data analyst works with data scientists to build analytical models that would run accurate analyses. These models are built using analytical software, like predictive modeling tools, and programming languages like Python, Scale, R, and SQL. After building, the model is tested with an experimental data set. The results from the test are reviewed, and changes are made to the model. The model is tested over and over until the model works as intended. Finally, the model runs against the intended data set in production mode.
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Presentation
The final step in data analytics is presenting the models’ results to the end-users and business executives. It is best practice to use tools like charts and infographics for presentations. They are easy to understand and communicate results.
Data analytics in business
The main goal of business analytics is to extract meaningful insights from data that an organization can use to inform its strategy and, ultimately, reach its objectives. Business analytics can be used for:
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Budgeting and forecasting
By assessing a company’s historical revenue, sales, and cost data alongside its goals for future growth, an analyst can identify the budget and investments required to make those goals a reality.
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Risk Management
By understanding the likelihood of certain business risks occurring—and their associated expenses—an analyst can make cost-effective recommendations to help mitigate them.
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Marketing and sales
By understanding key metrics, such as lead-to-customer conversion rate, a marketing analyst can identify the number of leads their efforts must generate to fill the sales pipeline.
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Product Development
By understanding how customers reacted to product features in the past, an analyst can help guide product development, design, and user experience in the future.
In Summary
Courses In data analytics are among the top areas of research and investment today. Gartner predicts that by 2025, new forms of analytics like context-driven analysis and artificial intelligence will replace existing technologies. Connected governance, data sharing, and the rise of data fabrics are among the other critical trends anticipated by Gartner.
To leverage the power of these technologies, companies need to know all about data analytics, its types, and applications. Implementing analytics properly makes it possible to drive business success and accelerate outcomes, even in a challenging market.
