Data analytics is the process of analyzing collected data in order to help organizations make informed business decisions. To that end, data analytics companies analyze the data in order to help uncover hidden value in data. Just as important, they also make recommendations for how to optimize business performance. For example, data analytic companies can analyze inventory data to help manufacturers better plan when to purchase parts, establish how long those parts should be stored, and identify where exactly to house the inventory – thus improving business operations and performance.
Why Data Analytics is Important
Today, as more organizations implement big data strategies, data analytic companies can help these organizations identify performance issues and make informed business decisions to improve and optimize results. Those that fail to leverage the information in their data, or do it poorly, will suffer competitive pressure and challenges. As such, it is imperative for organizations to invest in and strengthen their data analytic capabilities.
Data Analytic Process
Data analytics companies employ some form of the following for conducting data analyses strategies and activities:
- Requirements need to be carefully understood with a plan designed for capturing and safely securing the relevant data
- The data needs to be collected, cleansed, and organized in a manner so that the data can be analyzed
- The data is analyzed, conclusions rendered, and insights provided which may or may not include visualizations
Data analytics companies have implemented capabilities to support specific types of business situations, including information management, the use of machine learning software, and the processing of personal data as a source of information. In addition, data analytics companies often develop analytics capabilities that are designed to capture user behavior. They can also employ capabilities to help automate business processes, provide product support, and provide customer service.
Types of Data Analytics
In order for data analytic companies to deliver key insights, data professionals utilize any of three types of analytics. These are descriptive analytics, predictive analytics, and prescriptive analytics.
Descriptive analytics: Descriptive analytics assesses historical data to better understand changes that have occurred in a business. The purpose of descriptive analytics is to answer the question “what has happened?”. This is the simplest form of analytics and many companies employ descriptive analytics in their organization.
Predictive analytics: Predictive analytics makes predictions about future outcomes based on historical data combined with analytical techniques. It is an integrated, interactive analytics program aimed at improving the performance and efficiency of its operations in the future. Predictive analytics is a forecasting technique.
Prescriptive analytics: Prescriptive analytics focuses on identifying the best course of action in a scenario given the available data. It also suggests courses of actions that depend on the results of descriptive and predictive analytics. Optimization scenarios are assessed to answer “what should we do?”.
Tools of the Trade
Some of the more popular tools utilized by data professionals and data analytics companies include the following (not exhaustive):
Apache Spark: Apache Spark is an open source big data platform which combines data science with web analytics to provide a high-quality, data driven, and accurate analytics solution to applications.
Python: Python is an open source scripting language which is easy to learn and has a plethora of libraries, many of which are useful to data professional.
SAS: SAS is an analytics platform offered by the SAS Institute which helps organizations access, manage, analyze and report on data to aid in decision-making.
R Programming: R is an open source programming tool primarily used for statistics and data modeling provided by the R Foundation for Statistical Computing.
Tableau: Tableau is an interactive data visualization software tool used by data analysts, data scientists, and others to present data in a meaningful and interactive way. Tableau has a free version as well as paid version of their visualization tool.
The Future for Data Professionals
The demand for data professionals is high-demand but there is a significant shortage of qualified professionals to fill the need. According to a paper published by IBM, the number of positions for data and analytics talent in the United States will increase by 364,000 openings, to 2,720,000 in 2020. Additionally, Chief Data Officers (CDOs) will become a more in-demand position. Data analytic companies can help fill the skills gap in these data intensive fields.