Your organization creates and collects an immense amount of data — probably even more than you realize. What do you do with that data once it’s there? How can it help you? How do you make sense of it?
This is where business analytics comes in. Business analytics tools fall into a subcategory of business intelligence and involves the methodical exploration of data to help businesses make data-driven decisions. It answers all three of the questions posed earlier and then some.
If you’re looking to purchase business intelligence software for analytics, you may feel a little overwhelmed by all the options out there. That’s why we’ve created a handy checklist to help you determine what your organization needs and keep track of whether the solutions you’re considering can offer those features to you.
In data discovery, humans – or, in some cases, certain types of artificial intelligence technologies – look at data from various sources and try to extract important or meaningful information from that data. This is done in order to support various business objectives. Data discovery tools use a variety of methods such as heat maps, pivot tables, pie charts, bar graphs and geographical maps to help users accomplish their goals. It’s the process of finding patterns in data sets. This capability was determined to be one of the most-desired BI features in 2017. It includes exploring the data (summarizing the main characteristics of the data); visual interaction (turning the data into something we can process); real-time processing (via in-memory processing); and collecting, cleaning and consolidating data (data preparation). Automating the process of cleaning up and organizing data will save you innumerable hours and frustrations.
Once you discover patterns (with the help of your trusty BA sidekick data discovery), the data can be visualized in various ways. This is data visualization. It’s the process of displaying information in graphics, charts, figures and bars. It is used as way to deliver visual reporting to users for the performance, operations or general statistics of an application, network, hardware or virtually any IT asset. This data is generally in the form of numbers, statistics and overall activity.
The data is processed using data visualization software and is displayed on the system’s dashboard.This helps our brains process the information in a much more direct way than analyzing numbers. Some methods for achieving this include turning the data into a visual narrative via storyboarding or animation of static data into animation. Most software offer assisted guidance through the data discovery process (auto charting) and prefabricated templates for visualizations (plugins). Some even incorporate data presented in three dimensions (geospatial interaction). Your data visualization needs will vary based on the type of data you’re analyzing.
Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. It’s also known as data analysis.
Data analytics focus on drawing conclusions from the data collected and organized from data discovery and data visualization. It can function to answer a specific question (ad-hoc), predict future events (predictive), suggest how to solve a problem (prescriptive), diagnose a problem (diagnostic) or help constantly improve your business (performance). Never be in the dark about what has, is or will happen in your business by selecting analytics packages that suit your needs.
Advanced analytics is an umbrella term for a wide range of analytics techniques that make use of cutting-edge computing techniques such as machine learning. It refers to the autonomous examination of data using techniques beyond those offered by more basic BI. The goal of advanced analytics is to discover insights, make predictions or generate recommendations for improving the business.
Some features included involve structuring unstructured data with text and statistical analysis. Other functions automate repetitive or simple tasks like data collection or examine large or varied data sets, freeing up your employees to perform less automated tasks. Web analytics features include analyzing data from web and social media pages to get to know your audience inside and out.
Big Data Analytics
Social Media Analytics
Information management (IM) is the process of collecting, storing, managing and maintaining information in all its forms. Information management is a broad term that incorporates policies and procedures for centrally managing and sharing information among different individuals, organizations and/or information systems throughout the information life cycle.
Reporting and information management refers to the gathering of information and insights into the activities and functions of an organization. This capability includes targeted reporting (ad-hoc), displays in prefabricated or custom visualizations (dashboards), and data mining and sharing features. Reporting offers a snapshot of individual processes, so you can find pain points and improve those processes.
System integration is an IT or engineering process or phase concerned with joining different subsystems or components as one large system. It ensures that each integrated subsystem functions as required. It is the ability of software to cooperate and communicate with other platforms, applications and software. This can be anything from CMMS systems to CRM software to a business’ website to an email platform. Integrating systems consolidates data and makes it easier for sales and marketing teams to coordinate efforts. Not to mention it makes everyone’s life easier to have a single dashboard for information.
MS Office Applications
Big Data Integration
Predictive Analytics Packages
OLAP (Online Analytical Processing)
Online analytical processing (OLAP) is a high-level concept that describes a category of tools that aid in the analysis multi-dimentional queries. OLAP is a kind of computer processing that allows users to present data from different points of view. It does this by storing the data in a multidimensional database that understands each attribute to be a separate dimension. Then OLAP locates the intersection of those dimensions by pinpointing the places those attributes coexist and displaying them in dashboards.
Deployment environment refers to where the data these analytics functions are interacting with is stored. The deployment options are:
On-premise: the data is stored on hardware housed at the user’s own facility.
Cloud-based: the data is stored in one or multiple servers owned by third-parties, typically the vendor of the software, and accessed via an internet connection.
Hybrid: data is stored in a combination of hardware on the premises of the user and those of a third party.
Mobile business intelligence (mobile BI) refers to the ability to provide business and data analytics services to mobile/handheld devices and/or remote users. MBI enables users with limited computing capacity to use and receive the same or similar features, capabilities and processes as those found in a desktop-based business intelligence software solution. BI systems with Mobile BI give users the ability to access data, content and other features from mobile devices like smartphones and tablets. This feature is becoming increasingly popular as people spend less time at their desks and more time on the move. It can include anything from mobile phone apps to designated web browsers to mobile versions of the site where the software is hosted.
Now that you’re intelligent about business analytics tools, you can get started with a comprehensive comparison chart to narrow down your software choices and make the right choice for your business.
Did we miss any requirements? What do you want to see in a business analytics tool? Tell us in the comments!