Data analytics

Each day, your customers generate an abundance of data. Every time they open your email, use your mobile app, tag you on social media, walk into your store, make an online purchase, talk to a customer service representative, or ask a virtual assistant about you, those technologies collect and process that data for your organization. And that’s just your customers. Each day, employees, supply chains, marketing efforts, finance teams, and more generate an abundance of data, too. Big data is an extremely large volume of data and datasets that come in diverse forms and from multiple sources. Many organizations have recognized the advantages of collecting as much data as possible. But it’s not enough just to collect and store big data—you also have to put it to use. Thanks to rapidly growing technology, organizations can use big data analytics to transform terabytes of data into actionable insights. What is big data analytics? Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools. Big data has been a buzz word since the early 2000s, when software and hardware capabilities made it possible for organizations to handle large amounts of unstructured data. Since then, new technologies—from Amazon to smartphones—have contr...

• Data analytics is the science of analyzing raw data to make conclusions about that information. • Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions. • The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. • Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics). • Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, and reporting tools, data mining programs, or open-source languages for the greatest data manipulation. Understanding Data Analytics Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying m...

Summary. By observing the different approaches to data analytics taken by a wide range of companies, we can see some best practices for connecting data to real business value.Data science can’t happen in a silo. It must be tightly integrated into the business organization, operations, and processes. Business leaders and data scientists should jointly decide which business problems to focus on. If there is any question about priority, the final call should go the business heads. Leaders need to be conversant in data science. Business leaders don’t need in-depth expertise in data science, but they require a basic, working understanding. Data inevitably creates transparency and reveals business insights that can be unexpected, uncomfortable, and unwelcome. Data analytics will unearth inefficiencies and misconceptions that complicate leadership and disrupt conventional thinking. Business leaders who crush or ignore answers they don’t like will rapidly undercut the value of data analytics. In practicing data analytics for more than 30 years, and leading, advising, interviewing and teaching executives in many industries on data analytics for five years, I’ve observed that their approaches generally fall into one of five scenarios: two that typically fail, two that sometimes work partially, and one that has emerged as best. Let’s take a look at each: 1. We’re here to help — do you have any problems to solve? This scenario often starts with the CEO (sometimes prompted by the board...