Analytics

Analytics is the process of identifying patterns in data. It uses statistics, operations research, and other mathematical tools to make sense of information generated or collected by organizations. It is especially helpful as data volumes grow, when manual calculations are too difficult or complex.

In this era of big data, analytics has become essential to doing everything from understanding sales trends to segmenting customers based on their online behaviors to predicting how much inventory to hold. Yes, the data itself is a tremendous asset, but analytics is what makes data deliver value. And not just to business, but to sports, medicine, engineering, or any activity in which large amounts of data are involved.

AI

AI is the computer science practice of building automated systems that are able to perform tasks that normally require human intelligence. AI encompasses a broad range of technologies, such as computer vision, NLP, and neural networks.

Machine learning is one of the technologies that falls under the umbrella of AI. It makes it possible for systems to learn from processing data. In other words, computer systems don’t need to be specifically programmed by humans to anticipate every scenario—they automatically learn and improve from what the data tells them, and from their experience with that data, to make better predictions or decisions.

IDC predicts that enterprise spending on AI solutions will top $77.6 billion in 2022, more than three times the $24.0 billion in 2018, as illustrated in Figure 1-3. This represents an “impressive” 37.3% CAGR between 2017 and 2022, according to IDC.

The top reason that marketers are adopting machine learning and analytics is to improve the customer experience. A full 82% of enterprises already use machine learning to personally target customers, and 64% use it to deliver targeted content and promotions to them.

Figure 1-3. Spending on AI solutions is accelerating.

All of this is paying off. McKinsey discovered that 82% of businesses that adopted machine learning received, on average, a 17% return on investment (ROI). Companies in the technology, media and entertainment, and telecommunications fields are achieving the highest ROI.

Data scientists write the mathematical models underlying machine learning systems. Machine learning modeling requires significant skill, education, and training, and the data professionals capable of doing this are scarce. According to LinkedIn, demand for data scientists is “off the charts,” with a shortage of more than 150,000 data scientists in the US alone. Happily, many AI and machine learning models in the public domain can be found on community websites for free; businesses can use these models to get started with AI and machine learning.

Bringing It All Together

When you embed machine learning and AI into analytics, you get augmented analytics. Augmented analytics is a technology that automates the selection and preparation of data, the generation of insights, and the communication of those insights. The main thing that is new in this space is the democratization of advanced analytics tools. Today, advanced analytics is available to a broad range of business users: executives, managers, line-of-business workers, and citizen data scientists—those employees who have a natural aptitude and excitement for data science without the formal training.

Augmented analytics solutions come prebuilt with models and algorithms so that companies don’t need a data scientist to do this work. And these models are hidden under much friendlier interfaces so that users without data science training or PhDs in statistics can use the tools. Indeed, this is one of the key differences between augmented analytics and traditional analytics. With augmented analytics, the AI and machine learning are built into the product. The very complex model-building and number-crunching is still happening—but it’s always on, always working in the background to continuously learn and help users make more accurate decisions.

Because leading augmented analytics platforms feature NLP, this allows nontechnical users to easily ask questions from source data; natural language generation (NLG) then automates the process of translating complex data into text with intelligent recommendations, thereby accelerating analytic insights.

By using automated recommendations for data enrichment and visualization, anyone can quickly uncover unseen patterns and predict trends to optimize the time it takes to go from data to insights to decisions.

The Business Application Research Center (BARC) 2018 Business Intelligence Survey found that augmented analytics will completely transform the user experience, making the shortage of data scientists less urgent for many businesses.

NLP technology also helps drive the ability for nonexpert users to make sense of large amounts of data. Users can ask questions of the data using standard business terminology, and the software will find and query the right data and make the results easy to digest using visualization tools or natural language output.

Augmented analytics can help every data-hungry user of analytics—from business analysts to IT professionals, to the C-suite—in the following ways:

Recommend, prepare, and enrich data

Rather than having to decide which datasets to query, as with traditional analytics, an augmented analytics solution will recommend which datasets to include in analyses, alert users when those datasets are updated, and suggest new datasets if users are not getting the results they expect.

Create instant charts and graphics

This helps interpret and communicate results in an easily understandable context to help make swift business decisions.

Natural language interfaces

This allows users to do querying in natural language, to activate speech-to-text capabilities, and to get results generated—and even spoken—using everyday business language.

Forecast trending and clustering of data

It takes just one click to get accurate forecasts and predictions based on historical data.

Use proactive, personalized analytics with mobile applications

Augmented analytics provides a personalized assistant that understands individual users—such as using their location to determine what charts to present to a client at an offsite sales meeting.

Augmented analytics will also be personalized and proactive to the extent that it will present insights based on patterns it detects in users’ questions. Through self-learning, it will even anticipate future questions that perhaps a user hasn’t yet thought of.

Oracle’s Data Analytics Maturity Model

Oracle has defined the analytics maturity model as consisting of three waves: centralized, self-service, and augmented, as illustrated in Figure 1-4.

Figure 1-4. The Oracle analytics model (source: Oracle, May 2019)

If you centralize your analytics efforts, you get centralized data and semantic information for consistent metric definitions. This results in stronger governance than if your data is scattered throughout multiple repositories or datacenters.

If you build a self-service model for analytics so that users don’t need to involve a data “gatekeeper” to get access to the data they need, you will boost user productivity dramatically, speeding up business decisions. You will also be able to use nonstandard datasets from external or personal sources, such as suppliers, customers, and external data feeds such as commodity prices or weather data.

Finally, if you apply automation, machine learning, and AI within your analytics process, you will realize faster time to insights from your data, which means faster time to decisions and the ability to become a true data-driven business.

It’s important to understand that this is not a linear model. You do not need to centralize your augmented analytics initiative before you implement self-service, or achieve self-service before going augmented.

Leading modern analytics platforms will offer all three of these options at one time.

There are four ways to use analytics:

Descriptive

This type of analytics simply looks backward at historical information and describes what happened. You can query the data to discover, for example, the retail sales volume last quarter or how high employee turnover was last year. Much descriptive analytics work is done by humans using Excel spreadsheets.

Diagnostic

Now that you know what happened, you want to know why. You use analytics to find out that the reason overall revenues declined was that sales of women’s shoes dropped precipitously, or that the reason for heightened employee churn was that a new manager was hired in the finance department. In this stage, much of the work is still human centered and not yet automated.

Predictive

Analytics can also be applied to data to make predictions about what will happen next. Based on historical trends—and, importantly, assumptions about the future—what will overall revenues look like next quarter? The machine does more, and the human less, of the work in this stage.

Prescriptive

Finally, some recommendations. What should we do to ensure that sales continue on an upward trajectory?

Eliminating Bias from the Equation

The human-versus-machine control aspect of analytics maturity is important. Earlier in the analytics maturity model, analytics tools are controlled by humans—and tend to have human biases. For example, users who seek answers from data will make assumptions. They will choose what data to query, and they will structure queries based on their understanding and preconceived notions about the topic. Because of this, traditional analytics arguably introduces bias into the results.

This is where augmented analytics can shine. The data determines everything—not users’ assumptions. For example, traditionally if you want to forecast sales for the next quarter, you would make various assumptions, project it out, and build a model based on your so-called expert judgment. You might estimate what the sales growth rate would be based on economic indicators from Wall Street, for instance. Alternatively, economists could be predicting a downturn, and you might project less ambitious sales numbers.

On the other hand, with augmented analytics, the data itself determines all of these things. The machine learning model parses data to identify which datasets to access in response to a query about future revenues. The model also rephrases the natural language query from the human into machine language that is impartial. The predictions then typically are more accurate and, in many cases, much faster. Also, augmented analytics can actually inform users about other datasets that might be useful in completing a particular analysis.

Augmented Analytics in the Cloud

Although businesses can certainly deploy augmented analytics on an on-premises infrastructure, many, if not most, organizations choose a cloud-based infrastructure. The prime reasons are elastic scalability and cost effectiveness. AI technologies such as machine learning and NLP are very compute-intensive—they require lots of CPUs, or even very costly graphics processing units (GPUs)—but they are not necessarily predictable in when they demand those resources. Many businesses will thus experience tremendous peaks and troughs in processing, and the differences between the highs and lows can be massive. Businesses that stick with an on-premises infrastructure must plan for those peaks by provisioning sufficient compute, storage, and network resources, and plan for future growth by purchasing all of the resources upfront. This incurs high capital as well as operational costs because of course after those resources have been provisioned and deployed, they must also be managed and maintained. This is not only costly, it is slow given that provisioning new servers can take months in some organizations. And then many of those resources might be left unused much of the time.

Also, because the cloud can handle much larger amounts of data, you never need to aggregate or truncate data so that it fits on your on-premises infrastructure. Optimizing machine learning requires that all data be used to produce the most accurate predictions. Data should also be used in its lowest granularity to prevent the introduction of false insights based on preaggregations. Indeed, aggregating or truncating data can defeat the very business agility that analytics is supposed to achieve.

It’s not surprising then, that according to a recent Deloitte study, only 15% of companies say they prefer on-premises platforms when deploying AI. (Keep in mind that this 15% of companies includes firms in industries, like finance and gaming, which are required by law to remain on-premises.) The popularity of cloud-based AI platforms is further confirmed by their annual global growth rate, which Deloitte estimates to be a “remarkable” 48.2%.

In the cloud, businesses can dynamically provision whatever resources they need and scale up and down as required. This pay-only-for-what-you-need model is not only cost-effective, but it also makes businesses extremely agile in that new compute and storage can be provisioned in minutes, not weeks or months.

Indeed, the cloud is essential for delivering many of the benefits of augmented analytics, as we explain in the next section.

Benefits of Augmented Analytics

Here are four of the chief benefits that businesses are deriving from augmented analytics.

Roadblocks to Using Augmented Analytics

To take advantage of the many benefits we just outlined, businesses must overcome some obstacles to take advantage of augmented analytics throughout their operations. These barriers fall into two categories: technical and cultural.

Mandate Data-Driven Decision Making from the Top Down

Senior management must get behind it. No longer can business departments or groups make decisions based on what the HIPPO demands. No one should come to a meeting unless prepared to present data that supports their position. Incentives should be structured to reward those who make data-driven decisions.

Empower and Trust Users Throughout Functional Areas and Organizational Levels

Moving to augmented analytics requires moving to a self-service data model, which in turn requires trust. This requires building an “enlightened” organizational culture in which leaders—business and technical—release control to users so that they can access and use data to make decisions as appropriate to their roles and responsibilities.

Balance Self-Service with Centralized Governance

Most organizations with low analytics maturity do not have formal data governance programs in place. They might have thought about it, and they might understand the importance of it, but they simply don’t know where to start. Small companies, especially, struggle because of the cost barrier of entering into the realm of data centralization and imposing proper governance on analytics initiatives.

Data governance is simply the rules by which data is managed. This includes acquiring, storing, moving, transforming, analyzing, reporting, and deleting it. It is the framework by which data is ensured to follow both internal and external guidelines for security and privacy. This means that organizations must understand how data is collected, how it is used, how it moves through the organization, how it’s changed, and how it’s stored. They also need to ensure that access to data is given on the principle of permissions: there’s no reason why a finance professional should have access to HR data, and vice versa. So, you build user profiles to ensure that people have access to only the data they need to do their jobs. In short, self-service needs to be carefully balanced with a robust governance framework that ensures secure data access.

Ensure a “Single Source of Truth”

In companies that are still stuck in Excel-based analyses, it’s common for different users to download different versions of data onto their desktops or laptops, do independent transformations on that data, and end up with results that vary widely from one another. A best practice for organizations is to know where their true data resides, who owns it, and who manages it so that everyone in the organization is working off the same version of it, and it isn’t inadvertently changed or corrupted. If your organization currently lacks a single source of truth, you might have a problem trusting the results of analyses. Conversely, if everyone is working from the same dataset, the resulting predictions and insights will be more credible.

Also critical is to ensure consistency of metrics and calculations. You do this through the semantic layer of your information model—a common business language that is used to describe data so that users throughout the enterprise have the same definitions of such things as product, customer, revenue, and more.

Visualize Big, But Think Small

It’s additionally important to have a long-term strategy that clearly lays out the business value you expect to get from augmented analytics. Then, think small within that vision: after you have your overall strategy in place, you should adopt augmented analytics on a case-by-case basis based on business needs and where you foresee getting the maximum payoff. Identify those cases that are directly tied to immediate business objectives and that promise a high ROI. Avoid leaping head first into a massive five-year, rip-and-replace project. Instead, show the value you can get from augmented analytics early and often, always staying within the parameters of your long-term vision. Leading augmented analytics platforms allow you to look for quick wins and to use the hybrid cloud—part on-premises, part cloud—as your environment. That way, you can get something up and running quickly, with minimal disruption and training required for your staff, while retaining overall flexibility.

Consider Moving to the Cloud

If they haven’t already done so, businesses interested in augmented analytics should consider the cloud. The cloud gives you the flexibility to move your workloads to where they run best and more economically; the elasticity to scale up and down as your business demands; robust security; the agility to move on new opportunities immediately; and the cost effectiveness of paying only for the resources (compute and storage) that you need. All of these will contribute to the success of any augmented analytics initiative.

Empower Everyone with Access

With the current dearth of sufficient data scientists, it’s essential to put people and processes in place so that employees, customers, partners, and suppliers will use augmented analytics to make business decisions. Anticipate upcoming requirements based on your overall vision and strategy, and allow yourself sufficient time to secure the resources you need. Keep IT, data, and business teams aligned so that each is aware of the other’s needs—and challenges.

Accenture

Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology, and operations. Combining deep experience and specialized skills across more than 40 industries and all business functions, Accenture works at the intersection of business and technology to help clients improve performance and create sustainable value for stakeholders.

Making Augmented Analytics Accessible to All

Through the Accenture myConcerto platform—and, within that platform, the Accenture Digital Boardroom—Accenture is harnessing augmented analytics using the Oracle Analytics Cloud to help its clients transform virtually every aspect of their businesses: finance, supply chain, procurement, human capital management, and others.

In effect, myConcerto is an end-to-end integrated digital platform consisting of thought leadership, approaches, methodologies, assets, and accelerators that Accenture customers can apply to their most complex business scenarios. An insight-driven, integrated platform, myConcerto helps organizations boost their ability to innovate, amplify business results, and accelerate their journeys to becoming intelligent enterprises.

“Think of myConcerto as our one-stop shop for everything you need in Oracle Cloud,” says Patrick Sullivan, head of Accenture’s Oracle Business Group in North America. “Everything we do, from SaaS [software as a service], to PaaS [platform as a service], to IaaS [infrastructure as a service], and to all the different subclouds, are in it.”

So, whether a client is seeking augmented analytics, enterprise resource planning (ERP), or human capital management (HCM) solutions, they’ll find all of Accenture’s intellectual property, innovation accelerators, assets, and solutions within myConcerto, says Sullivan—and it’s purpose-built for Oracle Cloud.

The Digital Boardroom is one of the offerings within myConcerto. “With the Digital Boardroom, we’ve embedded our proprietary predictive analytics, proven models, and applied intelligence assets along with best practices into Oracle Analytics Cloud to create a single intelligence platform,” says Brad Genson, head of Accenture’s Oracle Analytics Group for North America. “We’re showing executives that they can not only see how their businesses are doing now, but they can also predict, and look into the future, as well.”

Accenture believes that the Digital Boardroom is unique in the market because of the Accenture thought leadership that it uses to augment Oracle Analytics Cloud. “We harvest our thought leadership on what metrics are critical in managing finance, in managing HCM, and in managing the supply chain. We add in our predictive analytics assets and experience to complement what Oracle Analytics already delivers,” says Genson.

Real-World Deployments of the Digital Boardroom

Already in use around the globe, the Digital Boardroom has been proven successful across a number of corporate functions. Here are two use cases: ERP and HCM.

Constantly Enhancing the Digital Boardroom

According to the Accenture TechVision 2018 report, 79% of executives are basing their most critical strategic decisions on data. Yet most of them, 94%, have not invested in capabilities to verify the veracity of the data.

“This is a big problem, and that’s what the Digital Boardroom solves. You now have a single source of truth,” says Sullivan. “Regardless of the use of machine learning, and AI, and all the buzzwords, we focus on helping companies make the right decisions with the right data to achieve a competitive advantage. That’s the real business problem that we’re trying to solve.”

“Everyone wants to try AI machine learning because they’re reading about it everywhere today,” agrees Genson. “We believe it combines Accenture experience to show those executives what’s possible.”

The Digital Boardroom depends on Oracle Analytics as its foundation. On top of that, Accenture has layered its own extract, transform, and load (ETL) processes to load the data. It has embedded its own data visualization assets into Oracle Analytics. And it uses the open source coding language Python as well as the R capabilities of the Oracle database.

Accenture is constantly innovating its Digital Boardroom. “Every month, we gather input from our clients’ projects: what they’re seeing; what innovation is happening with them. We’re also constantly gathering input from Oracle. We embed all that we learn into the Digital Boardroom,” says Genson.

The Way Things Were

Prior to its analytics transformation initiative, Riverbed did only traditional reporting of operational enterprise data. “This was important to us but did nothing special that edged our business up a notch or two,” says Bhishma.

Technically, the data was isolated into silos. This data was too fragmented and distributed to too many places to be reliable across the enterprise. Because of this, none of it could be combined with other data and put into AI models to make predictions of enterprise-wide behaviors or trends. All of the databases were on-premise, and there was a lot of other “technical debt” that prevented Riverbed from deriving full value from its data.

A Three-Pronged Transformation

This transformation brought about by Oracle Analytics Cloud and augmented analytics had three components: an organizational component, a technical component, and a cultural component.

The Future

Riverbed now plans to go deeper into augmented analytics to the point of embedding it into its products.

For example, Riverbed sells a product for end-user experience monitoring, Riverbed SteelCentral Aternity. In late March 2019, Aternity reported that Oracle Analytics Cloud performance had gone below its threshold. The tool automatically triggered an alert and Riverbed was able to take the necessary steps. “This worked the way it was designed. But we want to get into the business of not only alerting but remediation,” says Bhishma. “So, we’re examining how we can utilize our end-user experience monitoring tool with the server experience monitoring tool (App Response) along with SD-WAN optimization capabilities for rapid remediation. Orchestrating between these products is where we’ll be leveraging AI and Oracle Analytics Cloud.”

Riverbed is thus moving from internal use of augmented analytics to actually putting it into products for customers.

“We’re heading in that direction now to make our products more exciting and interesting,” says Bhishma. “We see augmented AI-driven analytics as a way to improve our bottom line by increasing revenues and giving us opportunities to reduce expenses.”

In summary, augmented analytics is proving to be a much higher-level solution than traditional analytics for Riverbed. “It’s very strategic for us,” Bhishma says.

Takeaways from User Stories

As we’ve seen from both Accenture and Riverbed, augmented analytics is real, and delivering real results in the real world. Both companies are using Oracle Analytics Cloud’s advanced analytics capabilities to gain insights into their internal operations and to embed as features into products for external customers. Whichever type of deployment your organization chooses, it is clear that competitive advantage follows. Accenture’s advice to companies that have not yet embraced augmented analytics? Don’t be afraid. Get started. Don’t wait for the right opportunity. You can use the technology today to add significant value.

“Figure out what is right for your organization, what’s going to have the biggest impact, and get started,” says Genson.

Data is powerful, says Sullivan. If you can unlock the value of the years and years and years of enterprise data that you possess, and use that to make informed decisions, it becomes a weapon for your business.

“We’ve got clients that are using what we’ve built for them using Oracle Analytics and the Digital Boardroom, and they’re going to seize the leadership positions in their industries,” says Sullivan. “They’re able to make decisions real-time, versus waiting for data scientists, data engineers, and analysts to sort it all out.”

Because of this, the Digital Boardroom, based on Oracle Analytics, “offers a massive competitive advantage,” says Sullivan.