Which Data Analytics Tools Is the Best?
Information, in a broad sense, is organized, processed and structured data intended to give meaning to other data. It helps decision making and gives context to individual data. For instance, a single consumer’s sale in a particular restaurant is valuable data-it becomes valuable information when the company is able to establish the most popular or least common dish.
However, information exists without data, i.e., we do not need to have information in order to decide how to behave or what to do. For example: “A man’s life is lived in an environment where he earns a living and enjoys things that make him happy.” Without a context to the information, it is only a series of unimportant facts about an individual; it does not create value or meaning.
This insight into the importance of context reveals another truth about data analytics tools. The true power of data analytics tools lies in enabling business managers and executives to understand the difference between what is important to a company and what is not. They need to understand the difference between what is strategic and what is tactical. They need to be able to separate operational requirements from non-strategic priorities. Thus, if these managers were to apply their existing data science skills to strategic decisions, rather than relying on business information, they would improve company performance.
Data science applies mathematics to large databases, e.g., database management, which, as a field, is very different from what we normally think of when we imagine a data analytics tool. In data analytics, we want to exploit the power of the internet, we want to leverage technological developments such as text mining to mine information from massive databases, and we want to use advanced technologies such as machine learning, neural networks and artificial intelligence. These are just some of the applications that make up a complete analytics tool. However, not all of these applications are powerful. We need to focus on two critical examples: domain specific tools and generic tools.
Domain specific tools refer to those which target a single business domain or industry. These domain specific data analytics tools must be able to provide a meaningful solution for the problem at hand. For example, if you are interested in finding out how sales are going to be up year over year, rather than looking at overall growth metrics, you’ll want a data analytics tool that targets that question specifically. Likewise, if you want to know why customer satisfaction is increasing so badly, rather than looking at overall profit figures, again, you’ll want to select a domain-specific tool.
Generic tools, meanwhile, are tools that help managers extract and manage information from a large database, and which can then be used to solve business problems. This generic category includes things like customer relationship management (CRM), marketing data analysis, and various other areas. If you’re more interested in building applications that generate reliable, actionable information, rather than specific answers to business questions, this is the best area for you. As these generic data analytics tools fall within a larger framework of business analytics tools, they can help your team learn a lot more about how people work, and what it takes to make them run as well as they can.