The business world is becoming increasingly data-driven, and, as time goes on, that world is only going to be swamped with even more data as it pours in from a multitude of sources. Most organizations today realize that if they capture and store all of this data, they can apply data analytics to gain significant value from it.
Data analytics is the process of examining large amounts of data to uncover hidden patterns, correlations, connections, and other insights in order to identify opportunities and make informed, evidence-based decisions. With today’s advanced tools and technology, it’s possible for organizations to conduct data analyses in near real-time and get the answers they need almost immediately.
Data analytics has many benefits for businesses that utilize and master the various tools and technologies available. With data analytics, businesses can extract value from their data in many ways, helping them to increase revenues, improve operational efficiency, optimize marketing campaigns and customer service programs, respond more quickly to emerging market trends, identify opportunities for new products and services, and gain a competitive advantage over rivals.
With so many advantages, it’s hardly surprising that a growing number of companies are increasing their efforts to become data-driven. According to NVP’s Big Data and AI Executive Survey 2019, 48% of those surveyed said their organizations were competing on data and analytics, with 31% stating they had a “data-driven organization”, and 28% a “data culture”. True, these are still minority figures, but all show increases over last year’s numbers, and considering that only 5% of executives said they were competing on data and analytics back in 2006, the upwards trend is as clear as it is impressive.
But what is data analytics really? What are the steps involved, the processes used, and the associated terms and job roles that combine to form the discipline that is “data analytics”? Let’s take a deeper dive.
The Data Analytics Process
As a science, data analytics is fundamentally about analyzing raw data in order to draw conclusions from it. Much of this is now automated using powerful computer programs and algorithms designed to reveal trends and metrics that would otherwise be invisible to the human eye. In truth, data analytics is something of an umbrella term that encompasses many diverse types of data analysis – but at a rudimentary level, the process involved in data analytics can be broken down into five steps.
First, an organization needs to determine the data requirements for a data analytics project. In terms of customer data, for example, this might mean separating the required data by age, demographic, income, purchase history, gender, etc. The second step is to collect the data. This is usually done via computerized or online sources. For retail sales, for instance, data might be collected from mobile applications, loyalty programs, website visits, online surveys, etc. Third, once the data is collected, it then has to be organized so that it is ready for analysis. This may take place on a spreadsheet, or other form of software that can take statistical data. The fourth step is to clean the data. This means it is thoroughly checked over to ensure there are no errors, duplications, and that it is not incomplete. Finally, the data analyst performs analysis on the data, looking for patterns, trends, outliers, etc.
That’s the basic process. It might sound simple enough, but when dealing with advanced data analytics projects at large organizations, each step becomes more and more complex as more and more data needs to be collected, organized, cleaned and analyzed. Often, during the collection phase, for instance, data from different source systems will need to be combined via data integration processes, transformed into a common format, and loaded into an analytics system, such as a data warehouse of NoSQL database. This requires teams of data scientists and data engineers as well as analysts to help prepare data sets for analysis.
During the organization and cleaning phases, the big task is to ensure that any problems with data quality that could affect the accuracy of results are fixed. This includes running data profiling and data cleansing routines, and ensuring that all processes are carried out in line with data governance policies.
Once the data is prepared, only then does a data scientist start to build an analytical model. To do so, predictive modeling tools, analytics software, and programming languages such as Python and R will be utilized. To ensure the model will produce accurate results, it will first be run against a partial data set to test for accuracy. This will then be revised and tested again – and again and again – until finally the data scientist is satisfied that the model functions as intended. Only at this point will the analytical model be run against a full data set and meaningful data analytics work can begin in earnest.
There is one further step in process, of course – communicating results to stakeholders to aid decision making. Usually, this is done with the help of data visualization and reporting techniques and tools, which data analytics teams use to produce charts, maps, graphs, and other infographics that present findings in a manner that is easy to understand. Such visualizations can be incorporated into dashboard applications that display on a single screen and can be updated in real time as new information becomes available.
Types of Data Analytics
As put forward by Gartner Research in 2017, there are essentially four types of data analytics. These are:
- Descriptive Analytics: This answers the question “What happened”? It analyzes and summarizes historical data to give valuable insights into the past. For example, a manufacturer can learn how many products were returned last month; a healthcare provider how many patients were hospitalized; a retailer how many sales were made.
- Diagnostic Analytics: Answers the question “Why did it happen?” It takes the insights found from descriptive analytics and drills down to find the cause or causes of the outcome.
- Predictive Analytics: This helps an organization answer the question “What is likely to happen next?” by using the findings of the first two types of data analytics to predict future trends. Our manufacturers, healthcare providers and retailers would use predictive analytics to forecast how many returns, patients and sales there are likely to be in the future based on what’s known about the past.
- Prescriptive Analytics: Finally, the most valuable type of data analytics is the prescriptive variety – combining the insight from all previous analyses to determine the course of action to take in the face of a current decision or problem.
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Naturally, the further down the list we go, the more advanced the toolset needs to be – especially when dealing with big data. When we get to predictive and prescriptive analytics, machine learning tools need to be deployed that use automated algorithms to churn through huge data sets. Using machine learning, organizations can pose and, with well-designed systems, go a long way towards accurately answering the question “What would happen if we tried this?” without having to spend time and resources trying out numerous variables in real-world scenarios. Predictive and prescriptive analytics are indeed the most valuable types of data analytics, though of course require big investments and commitment from organizations that wish to use them.
Data analytics initiatives are supporting businesses in a wide variety of industries. Aside from manufacturers, retailers and healthcare providers, financial services providers are using data analytics to analyze withdrawal and spending patterns to recommend new financial products and prevent fraud and identity theft. In agriculture, farmers are building yield prediction models to help determine what to plant and where to plant it. And mobile network operators are forecasting customer churn by examining customer data so they can create targeted campaigns to prevent customers from defecting to rivals.
Data analytics is becoming increasingly valuable for organizations of all stripes around the globe – and the more data that’s generated, the more valuable data analytics skills will become in the future. To remain competitive in our data-driven business world, the time is now to start learning, recruiting, and investing in analytics, and begin unlocking the key insights that will drive business growth as we head into the new decade before us.