Once a domain has been selected and data sources have been combined into a model, the next step is to perform an analysis of the data. This is a key process within business intelligence as this is when you attempt to answer questions that are relevant to the business using internal and external data. Simply having data about sales is not immediately useful to a business. In order to predict future sales revenue, it is important that such data is aggregated and analyzed in some form. For example, analysis can determine the average sale for a product, the frequency of purchases, and which customers purchase more frequently than others. This is the information that allows for better decision-making by an organization.
Data analysis can take many forms, such as grouping data, creating simple aggregations such as sums, counts, and averages, as well as creating more complex calculations, identifying trends, correlations, and forecasting. Many times, organizations have, or wish to have, key performance indicators (KPIs), that are tracked by the business in order to help determine the organization's health or performance. KPIs might include such things as employee retention rate, net promoter score, new customer acquisitions per month, gross margin, and Earnings Before Interest, Tax, Depreciation, and Amortization (EBITDA). Such KPIs generally require that the data be aggregated, calculations performed on it, or both. These aggregations and calculations are called metrics or measures and are used to identify trends or patterns that can inform business decision-making. In some cases, advanced analysis tools such as programming languages, machine learning and artificial intelligence, data mining, streaming analytics, and unstructured analytics are necessary in order to gain the proper insights.
There are numerous programming languages that have either been specifically designed from the ground up for data analytics or have developed robust data analytics packages or extensions. Two of the most popular languages in this space include R and Python. Other popular languages include SQL, multi-dimensional expressions (MDX), Julia, SAS, MATLAB, Scala, and F#.
There is also a wide variety of machine learning and data mining tools and platforms for performing predictive analytics around data classification, regression, anomaly detection, clustering, and decision-making. Such systems include TensorFlow, Microsoft's Azure ML, DataRobot, Alteryx Analytics, H2O.ai, KNIME, Splunk, RapidMiner, and Prevedere.
Streaming analytics becomes important when dealing with Internet of Things (IoT) data. Tools such as Striim, StreamAnalytix, TIBCO Event Processing, Apache Storm, Azure Streaming Analytics, and Oracle Stream Analytics are used.
When dealing with unstructured data, tools such as PIG and HIVE are popular, as well as tools such as Apache Spark and Azure Cognitive Services for vision, speech, and sentiment analysis.
Of course, any discussion around data analytics tools would be incomplete without including Microsoft Excel. Spreadsheets have long been the go-to analytics tool for business users, and the most popular spreadsheet today is Microsoft Excel. However, other spreadsheet programs such as Google Sheets, Smartsheet, Apple Numbers, Zoho Sheet, and LibreOffice Calc also exist.