Data Analytics

As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question “what happened?” This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.

The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics. This information provides new insight from data. Advanced analytics addresses “what if?” questions.

The availability of machine learning techniques, massive data sets, and cheap computing power has enabled the use of these techniques in many industries. The collection of big data sets is instrumental in enabling these techniques. Big data analytics enables businesses to draw meaningful conclusions from complex and varied data sources, which has been made possible by advances in parallel processing and cheap computational power.

The work of a data analyst involves working with data throughout the data analysis pipeline. This means working with data in various ways. The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis.

Data mining is an essential process for many data analytics tasks. This involves extracting data from unstructured data sources. These may include written text, large complex databases, or raw sensor data. The key steps in this process are to extract, transform, and load data (often called ETL.) These steps convert raw data into a useful and manageable format. This prepares data for storage and analysis. Data mining is generally the most time-intensive step in the data analysis pipeline.

Data management or data warehousing is another key aspect of a data analyst’s job. Data warehousing involves designing and implementing databases that allow easy access to the results of data mining. This step generally involves creating and managing SQL databases. Non-relational and NoSQL databases are becoming more common as well.

Statistical analysis allows analysts to create insights from data. Both statistics and machine learning techniques are used to analyze data. Big data is used to create statistical models that reveal trends in data. These models can then be applied to new data to make predictions and inform decision making. Statistical programming languages such as R or Python (with pandas) are essential to this process. In addition, open source libraries and packages such as TensorFlow enable advanced analysis.

The final step in most data analytics processes is data presentation. This step allows insights to be shared with stakeholders. Data visualization is often the most important tool in data presentation. Compelling visualizations can help tell the story in the data which may help executives and managers understand the importance of these insights.

As the importance of data analytics in the business world increases, it becomes more critical that your company understand how to implement it. Some  benefits of data analytics include:

1. Improved Decision Making – Companies can use the insights they gain from data analytics to inform their decisions, leading to better outcomes. Data analytics eliminates much of the guesswork from planning marketing campaigns, choosing what content to create, developing products and more. It gives you a 360-degree view of your customers, which means you understand them more fully, enabling you to better meet their needs. Plus, with modern data analytics technology, you can continuously collect and analyze new data to update your understanding as conditions change.

2. More Effective Marketing – When you understand your audience better, you can market to them more effectively. Data analytics also gives you useful insights into how your campaigns are performing so that you can fine-tune them for optimal outcomes. Using the Lotame Campaign Analytics tool, you can gain insights into which audience segments are most likely to interact with a campaign and convert. You can use this information to adjust your targeting criteria either manually or through automation, or use it to develop different messaging and creative for different segments. Improving your targeting results in more conversions and less ad waste.

3. Better Customer Service – Data analytics provide you with more insights into your customers, allowing you to tailor customer service to their needs, provide more personalization and build stronger relationships with them. Your data can reveal information about your customers’ communications preferences, their interests, their concerns and more. Having a central location for this data also ensures that your whole customer service team, as well as your sales and marketing teams, are on the same page.

4. More Efficient Operations – Data analytics can help you streamline your processes, save money and boost your bottom line. When you have an improved understanding of what your audience wants, you waste less time on creating ads and content that don’t match your audience’s interests. This means less money wasted as well as improved results from your campaigns and content strategies. In addition to reducing your costs, analytics can also boost your revenue through increased conversions, ad revenue or subscriptions.