What is Analytics?

Analytics defined

Analytics is the process of discovering, interpreting, and communicating significant patterns in data. . Quite simply, analytics helps us see insights and meaningful data that we might not otherwise detect. Business analytics focuses on using insights derived from data to make more informed decisions that will help organizations increase sales, reduce costs, and make other business improvements.

Business analytics

Business analytics is ubiquitous these days because every company wants to perform better and will analyze data to make better decisions. Organizations are looking to get more from analytics—using more data to drive deeper insights faster, for more people—and all for less. To meet those goals, you need a robust cloud analytics (PDF) platform that supports the entire analytics process with the security, flexibility, and reliability you expect. It needs to help you empower your users to do self-service analysis without sacrificing governance. And it must be easy to administer.

But how can you get the benefits of an enterprise-class system without enterprise-class costs and infrastructure?

With business analytics—using personalization, machine learning, and deep domain knowledge—companies can gain relevant, actionable insights from data across applications, data warehouses, and data lakes. Business analytics should be a complete process that calls for an action. Once insights are achieved, a business can then re-evaluate, re-execute, and reconfigure its processes. It’s all about taking the action.

Analytics fundamentals

Data in and of itself is meaningless. We can turn over every single rock and learn every possible lesson but if we don't act, if we don’t pivot, if we don't adjust, all our work will be for not. If we don’t leverage all the technology at our disposal, we are not getting every single dollar back that we could on our investment. In our world today, we are effectively able to speak with our data; have it answer questions; have it predict outcomes for us; and have it learn new patterns. This is the potential of your data.

The business value of analytics

  • A new way to work

    The nature of business is changing, and with that change comes a new way to compete. Keeping up with the demands of today’s tech-savvy workforce means having a method for creating value and running quickly. Deliver speed and simplicity to your users while maintaining the highest standards for data quality and security. A centralized analytics platform where IT plays a pivotal role should be a fundamental part of your business analytics strategy. The combination of both business-led and IT-led initiatives is the sweet spot for innovation.

  • Uncover new opportunities

    Advancements in analytics technology are creating new opportunities for you to capitalize on your data. Modern analytics are predictive, self-learning, and adaptive to help you uncover hidden data patterns. They are intuitive as well, incorporating stunning visualizations that enable you to understand millions of rows and columns of data in an instant. Modern business analytics are mobile and easy to work with. And they connect you to the right data at the right time, with little or no training required.

  • Visualize your data

    You want to see the data signals before your competitors do. Analytics provides the ability to see a high-definition image of your business landscape. By mashing up personal, corporate, and big data, you can quickly understand the value of the data, share your data story with colleagues, and do it all in a matter of minutes.

Past: History of analytics

Comparing statistics and analyzing data predates written history, but there are some significant milestones that helped develop analytics into the process that we know today.

In 1785, William Playfair came up with the notion of a bar chart, which is one of the basic (and widely used) data visualization features. The story goes that he invented bar charts to show a few dozen data points.

In 1812, mapmaker Charles Joseph Minard plotted the losses suffered by Napoleon's army in their march on Moscow. Starting at the Polish-Russian border, he created a linear map with thick and think lines showing how the losses were tied to the bitter cold winter and length of time the army was away from supply lines.

In 1890, Herman Hollerith invented a "tabulating machine," which recorded data on punch cards. This allowed the data to be analyzed faster, thereby speeding up the counting process of the U.S. Census from seven years to 18 months. This established a business requirement to constantly improve on data collection and analysis that is still adhered to today.

Present: Analytics today

The 1970s and 1980s saw creation of the relational database (RDB) and Standard Query Language (SQL) software that would extrapolate data for analysis on demand.

In the late 1980s, William H. Inmon proposed the notion of a “data warehouse” where information could be accessed quickly and repeatedly. Additionally, Gartner Analyst Howard Dresner termed the phrase, "business intelligence," which paved the way for an industry push toward analyzing data with the intent of better understanding business processes.

In the 1990s, the concept of data mining allowed businesses to analyze and discover patterns in extremely large data sets. Data analysts and data scientists flocked to programming languages like R and Python to develop machine learning algorithms, work with large datasets, and create complex data visualizations.

In the 2000s, innovations in web searching allowed for the development of MapReduce, Apache Hadoop, and Apache Cassandra to help discover, prepare, and present information.

Future: Next-generation analytics

As businesses shifted from just gaining data visibility and requiring more insight, the tools and their capabilities have evolved as well.

The first analytics toolsets were based on the semantic models forged from business intelligence software. These helped with establishing strong governance, data analysis, and alignment across functions. One drawback was that reports were not always timely. Business decision makers were sometimes unsure the results were aligned with their original query. From a technical standpoint, these models are primarily used on premises, making them cost-inefficient. The data is also often trapped in silos.

Next, the evolution of self-service tools advanced analytics to a broader audience. These accelerated the use of analytics since they did not require special skills. These desktop business analytics tools have gained popularity over the past few years, particularly in the cloud. Business users are excited about exploring a wide variety of data assets. While the ease of use is appealing, blending of data and creating a "single version of the truth" becomes increasingly complex. Desktop analytics are not always scalable to larger groups. They are also susceptible to inconsistent definitions.

Most recently, analytics tools are enabling a broader transformation of business insight with the help of tools that automatically upgrade and automate data discovery, data cleansing, and data publishing. Business users can collaborate with any device with context, harness the information in real time, and drive outcomes.

Today, humans are still doing most of the work, but automation is gaining support. Data from existing sources can be combined easily. The consumer works by executing queries, then gains insight by interacting with visual representations of the data and builds models to predict future trends or outcomes. These are all managed and controlled by people at a very granular level. The inclusion of data gathering, data discovery, and machine learning provide the end user with more options in a faster time frame than ever before.

Embracing business analytics

Analytics permeates every aspect of our lives. No matter what question you are asking—whether it's about employees or finances, or what customers like and dislike and how that influences their behavior—analytics gives you answers and helps you make informed decisions.