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Four Simple Steps Towards Business Analytics Success

Guest Author

Your company's journey toward data-driven decisions may have many choices but sometimes the steps to success are as simple as following a path taken by other winning business models. When it comes to the new face of business, we feel Oracle Analytics Cloud offers features that are ahead of the competition—enabling the use of advanced analytics that support truly informed decisions, fuel future growth, and optimize current processes.

Here is how this looks as a flow chart.

Cognizant Four Simple Steps Towards Business Analytics Success

The Potential of Predictive Analytics

Let's start with a prediction. Predictive analytics will be on your radar in the next two years if not sooner. The global predictive analytics market is expected to grow at a compound annual growth rate of about 22 percent by 2022 ($12.45 billion) during the forecast period, according to TechNavio's latest market research report. Similarly, the global predictive analytics market is expected to generate a market value of $13 billion by 2023, growing at a compound annual growth rate of about 21 percent, as seen in the graphic below.

Global Predictive Analytics Market, 2017-2023 (US$ Billion)

Predictive Analytics Market Research Report - Global Forecast till 2023 -Report image 00

Source: Market Research Future (MRFR) Analysis

With the increased success of advanced technologies like artificial intelligence (AI), augmented reality (AR), machine learning (ML), advanced analytics, big data, cloud platforms for faster deployments, and the Internet of Things (IoT), we anticipate  analytics will be mainstream for at least the next few years.

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Gartner has predicted in its February 2019 report titled, "Magic Quadrant for Analytics and Business Intelligence Platforms," that "By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence, data science and machine learning platforms, and embedded analytics."

In the last few years, we have all seen how visual analytics has disrupted the traditional business intelligence (BI) market and customer behaviors. Similarly, Gartner foresees augmented analytics and conversational BI (natural language queries/search) with ML on huge data stores, will be the next big wave.

We already see in Oracle SaaS applications that AI, ML, and IoT are getting embedded at speed. Similarly, those in decision-making roles increasingly rely on insights coming from the data of different functions and social media to act upon growth levers or threats.

Why the Need?

Currently, businesses are running in a very dynamic and fluid environment, influenced by changing customer needs, internet usage, social influences, emerging competitive businesses, geopolitical situations, and an ever-evolving threat landscape, to name just a few. These changes are coming so fast that historical descriptive analytics is no longer very helpful.

Currently, most businesses are still focused on antiquated ecommerce best practices. They have huge data sources which have been accumulated from different applications over time. This data lives in a hybrid on-premises and cloud-based environment. To stay ahead, organizations clearly need a better way of managing business and competitive intelligence.

Large organizations need agility just like startups to respond to market changes; and small organizations need the resources to innovate and grow. Predictive intelligence is going to play a pivotal role in business survival and success over the next decade.

Before we go further, let's tackle a few definitions.

What is predictive analytics?

PA is a constituent of advanced analytics, which is used to predict the possible occurrence(s) of unknown future event(s). Some examples of PA are predicting employee attrition, marketing campaign optimizations, detecting fraud, maximizing revenues from existing customers by cross/upsells, operational optimization such as supply chain management, etc.

Predictions are derived using techniques like machine learning, data mining, AI, and statistical modeling. PA gives foresights with significant accuracy, and hence the heightened interest among major enterprises and across all industries.

What is ML?

Machine learning is the science of enabling machines/computers to learn on their own, without human intervention. ML uses data to understand patterns, predictors, and influencers from a given dataset and continues to generate predictions based on more current data feeds.

Oracle Analytics Cloud for Predictive Analytics Through Machine Learning

Oracle has thousands of customers using cloud applications such as ERP, human capital management (HCM), supply chain management (SCM), enterprise performance management (EPM), and Oracle Enterprise Resource Planning, along with industry applications on premises. These customers are in various phases of adoption, either on cloud, on premises, or using a hybrid model. With the pace of technology exploding, customers are trying to balance their priorities to make best use of technology and augment their growth story. Here are the Top 10 tech trends per research done by Gartner analysts:

Cognizant Four Simple Steps Towards Business Analytics Success

In coming years, we feel the three most important trends related to data and insights will be:

  1. Autonomous things
  2. Augmented analytics
  3. Immersive experience (including user interface and mobile)

Oracle is very well positioned to empower its customer base through this transformation. Oracle Analytics Cloud is available on Oracle Autonomous Data Warehouse, has advanced features for augmented analytics, and is available for different business roles as interactive features.

Oracle Analytics Cloud is currently available on Oracle ADCS, which enables users to get a quick start without the need for new hardware procurements and installations. ADCS is a new way to plug in analytics with reduced maintenance, as self-tuning, patching, upgrading, self-security, and self-repairing are all shifted to service providers. This enables organizations to focus on business needs and innovations rather than support.

Data has become so important, it is often referred to as "the new oil" or "the new currency." It is available in huge volumes and constantly growing. It can be so overwhelming that in most cases, users don’t know how to optimally make use of it. Here is where Oracle’s powerful analytics comes into play.

Customer demand for analytics/insights can be divided into three major categories:

  1. Central reporting capabilities
  2. Self-service reporting capabilities
  3. Augmented analytics capabilities

Central reporting is handled by the Oracle Analytics data visualization feature, which enables a self-service capability at the enterprise level, while ML supports augmented analytics.

Skilled data science resources are always in short supply at the same time that demand for predictive adaption is very high. To bridge this gap and to quickly start up a project from initial concept to adaptive intelligence, Oracle has come with a prebuilt machine learning platform. This fantastic feature allows quick deployment where business leaders and IT teams generate valuable data-driven outcomes at record speed.

Using Oracle ML for data from HCM, finance, EPM, and similar enterprise applications lets users quickly bring up a proof of concept, see the success of it, and then move it into enterprise-wide initiatives.

Oracle machine learning can be used for every facet of business life from employee attrition and personalized customer service to fraud prevention, risk management, sales prediction by product or by region, campaign optimization, and even predicting machine or asset failure.

Oracle has also enabled mobile-based augmented analytics to help people build a data story through conversational interfaces.

To learn how you can benefit from Oracle Analytics, visit Oracle.com/analytics.

 

Hemalatha Vema     Guest author, Hemalatha Vema is Senior Director of ERP at Cognizant

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