Get started with analytics


Analytics defined

Analytics is the process of discovering, interpreting, & 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 lớn make more informed decisions that will help organizations increase sales, reduce costs, và make other business improvements.

Business analytics

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

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

With business analytics—using personalization, machine learning, và deep domain knowledge—companies can gain relevant, actionable insights from data across applications, data warehouses, và 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-exedễ thương, và reconfigure its processes. It’s all about taking the action.

What is business analytics?

Analytics fundamentals

Data in và of itself is meaningless. We can turn over every single rock và 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 baông chồng that we could on our investment. In our world today, we are effectively able khổng lồ speak with our data; have sầu it answer questions; have sầu it predict outcomes for us; và have sầu 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, và with that change comes a new way to lớn compete. Keeping up with the demands of today’s tech-savvy workforce means having a method for creating value & running quickly. Deliver speed & simpliđô thị lớn your users while maintaining the highest standards for data chất lượng & security. A centralized analytics platsize where IT plays a pivotal role should be a fundamental part of your business analytics strategy. The combination of both business-led & IT-led initiatives is the sweet spot for innovation.

Uncover new opportunities

Advancements in analytics giải pháp công nghệ are creating new opportunities for you lớn capitalize on your data. Modern analytics are predictive, self-learning, và adaptive sầu khổng lồ help you uncover hidden data patterns. They are intuitive sầu as well, incorporating stunning visualizations that enable you to understvà millions of rows và columns of data in an instant. Modern business analytics are thiết bị di động & easy khổng lồ work with. And they connect you khổng lồ the right data at the right time, with little or no training required.

Visualize your data

You want to lớn see the data signals before your competitors vày. Analytics provides the ability to lớn 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, nói qua your data story with colleagues, & vì it all in a matter of minutes.

Analytics trends

Amid the constantly evolving analytics market, the fundamental shift from IT leading the charge to pursue business analytics initiatives, khổng lồ one where the business và IT mô tả in this decision is now the new normal. There is no doubt that analytics has become strategic for most organizations today, & as such, has introduced a new wave sầu of both new consumers & new expectations.

What has changed is the way that decisions must be made in real time and shared with a wide audience. The workforce is changing, & that change brings a new way to work. Gone are the days where training manuals are commonplace in the office—today’s workforce expects khổng lồ get up and running quickly with an intuitive interface. But it doesn’t kết thúc there. While speed và simplicity are key, business leaders still have sầu high expectations around data quality & security. A centralized analytics platsize where IT plays a pivotal role is still a fundamental part of any analytics strategy. The combination of both business-led & IT-led initiatives is the sweet spot for innovation.

We believe sầu that putting analytics in the cloud is much more than just a deployment choice—it breaks down the barriers between people, places, data, và systems to lớn fundamentally shift the way people and processes interact with information, giải pháp công nghệ, and each other.

Past: History of analytics

Comparing statistics and analyzing data predates written history, but there are some significant milestones that helped develop analytics into lớn 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 (và 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 maps with thiông chồng & think lines showing how the losses were tied to lớn the bitter cold winter và length of time the army was away from supply lines.

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In 1890, Herman Hollerith invented a "tabulating machine," which recorded data on punch cards. This allowed the data to lớn 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 lớn constantly improve on data collection and analysis that is still adhered khổng lồ today.

Present: Analytics today

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

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 lớn analyze & discover patterns in extremely large data sets. Data analysts và data scientists flocked to lớn programming languages like R & Pykhông lớn to lớn develop machine learning algorithms, work with large datasets, và create complex data visualizations.

In the 2000s, innovations in website searching allowed for the development of MapReduce, Apabịt Hadoop, và Apache Cassandra Stavrou to help discover, prepare, và present information.

Future: Next-generation analytics

As businesses shifted from just gaining data visibility & 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, & alignment across functions. One drawbaông chồng 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 khổng lồ a broader audience. These accelerated the use of analytics since they did not require special skills. These desktop business analytics tools have sầu 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 và creating a "single version of the truth" becomes increasingly complex. Desktop analytics are not always scalable khổng lồ larger groups. They are also susceptible khổng lồ inconsistent definitions.

Most recently, analytics tools are enabling a broader transformation of business insight with the help of tools that automatically nâng cấp và automate data discovery, data cleansing, & data publishing. Business users can collaborate with any device with context, harness the information in real time, và drive sầu 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 khổng lồ predict future trends or outcomes. These are all managed and controlled by people at a very granular màn chơi. The inclusion of data gathering, data discovery, and machine learning provide the kết thúc 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 dislượt thích & how that influences their behavior—analytics gives you answers và helps you make informed decisions.