Advanced Analytics 101: Beyond Business Intelligence

Advanced Analytics 101: Beyond Business IntelligenceTrying to make sense of data is nothing new. Companies have been applying analytical methods since before the computer was invented. Once computers and storage became reasonably cheap and powerful, in the late 1980s, companies started using Business Intelligence (BI) software to try to find meaning in their data. Today’s Advanced Analytics go beyond the capabilities of Business Intelligence.

Business Intelligence is driven by an end user who knows the questions they want to ask and has deliberately collected data to support their inquiry. Advanced Analytics let users find answers when they don’t even know what they should be asking by identifying meaningful patterns. Rather than just looking backwards at the data to understand what has happened, Advanced Analytics use the data to look to the future and understand what is likely to happen. While both Business Intelligence and Advanced Analytics are used to improve decision making, the two techniques have different goals and use different methods.

Three Kinds of Analytics

There are three basic kinds of Analytics, increasing in both complexity and power.

  • Descriptive Analytics: Like Business Intelligence, it looks backward to understand what happened. The techniques used in these Analytics are the least complicated, mostly summarizing data through counts, aggregations of metrics, and simple calculations such as averages. These Analytics may also use data mining to find correlations between variables and help identify reasons for previous success or failure.
  • Predictive Analytics: Uses statistical models and other methods to predict what might happen based on previous events. These Analytics use statistics, data mining, Machine Learning methods, and business rules to make probabilistic predictions of the results of certain actions.
  • Prescriptive Analytics: Gives recommendations on what should be done, including for questions that weren’t specifically asked. These Analytics extend upon Predictive Analytics through the use of optimizations and simulations to evaluate possible actions and the potential impact of each option.

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What every CEO/MD needs to know about Big Data & Data Analytics

What every CEO/MD needs to know about Big Data & Data AnalyticsIn order to remain competitive, and viable, businesses now have to deal with a vast and rapidly growing sea of what has been termed ‘Big Data’.  They need to be able to transform this raw data, often in real-time, into more meaningful insights about their markets, customers, competitors, and to measure and manage their performance more accurately using using techniques such as ‘Data Analytics’. In many cases this represents a paradigm shift from their comfort zone of approaches based more on experience, guesswork, or painstakingly constructed models of reality.

It used to be that Big Data and Data Analytics were the preserve of large global corporations but consider this definition for Big Data: When volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big Data is a relative term. Every organization will rapidly reach a point where the volume, variety and velocity of their data will be something that they have to address.

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How to Boost Customer Loyalty Using Analytics and Actionable Insights

Returns data offers companies priceless insights that improve the customer experience.Returns data offers companies priceless insights that improve the customer experience.

As ecommerce grows, so do return rates: About one in three online purchases is returned. For online apparel purchases, the rate is closer to 40 percent. That has triggered the growth of the returns-management software marketplace, offering retailers solutions aimed at making the processing and dispensation of returned goods faster and more efficient.

That’s important. The more quickly and efficiently a return is processed, the better the outcome: The consumer gets his or her refund faster. The retailer minimizes reverse-logistics costs. And the company offering the product recoups the maximum recovery value.

Surge in real-time big data and IoT analytics is changing corporate thinking

Surge in real-time big data and IoT analytics is changing corporate thinkingOrganizations want real-time big data and analytics capability because of an emerging need for big data that can be immediately actionable in business decisions. An example is the use of big data in online advertising, which immediately personalizes ads for viewers when they visit websites based on their customer profiles that big data analytics have captured.

“Customers now expect personalization when they visit websites,” said Jeff Kelley, a big data analytics analyst from Wikibon, a big data research and analytics company. “There are also other real-time big data needs in specific industry verticals that want real-time analytics capabilities.”

The financial services industry is a prime example. “Financial institutions want to cut down on fraud, and they also want to provide excellent service to their customers,” said Kelley. “Several years ago, if a customer tried to use his debit card in another country, he was often denied because of fears of fraud in the system processing the transaction. Now these systems better understand each customer’s habits and the places that he is likely to travel to, so they do a better job at preventing fraud, but also at enabling customers to use their debit cards without these cards being locked down for use when they travel abroad.”


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Analytics in Government: Picking Up Speed or Caught in an Eddy? (Industry Perspective)

Analytics in Government: Picking Up Speed or Caught in an Eddy? (Industry Perspective)Whether U.S. voters are advocates for “smaller government,” “better government” or both, analytics has great potential to make federal, state and local government agencies more efficient, faster and able to operate with fewer staff members and make their staffs more effective.

In the private sector, applying analytics and using big data is a major trend that’s gaining momentum — 73 percent of private-sector enterprises said they are now using analytics or plan to start in the next two years, according to a leading research firm’s latest report. Likewise, applying analytics to big data is “one of the hottest trends in the public sector,” according to Tod Newcombe, a senior editor with Government Technology.

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Business Intelligence vs. Business Analytics

Business Intelligence vs. Business AnalyticsThe painful truth about the difference between business analytics and business intelligence is that if you ask two people to tell you the difference, you’re going to get two conflicting sets of differences explained to you. A lot of business analysis terms get misused, reinterpreted and misappropriated until their original meaning is a murky memory from the distant past.

It’s not so much that there is no difference, it’s just that you need to be prepared to acknowledge that people are going to fight you on it, and that you can’t assume that you’re speaking the same language as everyone else in the room until you break it down into plain English. Everyone has their own idea of what every business term means, so until we get down to talking about the actual points of data and the strategies being used, there’s going to be a lot of confusion.

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Are You Focused On The Right Analytics?

Are You Focused On The Right Analytics?The Analytics continuum: Descriptive Analytics → Predictive Analytics → Prescriptive Analytics. Are you focused on the right one?

Let’s assume I am a marketing analyst for one of the largest discount retailer in the market. My company has invested heavily into one of the largest analytics, (actually, I meant descriptive analytic tools in the market), name ends with a U. I look at trends as soon as data is loaded into the tool. My end consumers include the VP of Marketing, the VP of Category Management, and also the Finance team. The Sales team also has access to my reports. I am the front line for all of these folks.

Every quarter, we have a planning exercise which starts with all of the above participants getting together in a room and talking through plans for that quarter. I start by providing each team its most updated and refreshed dashboard. Based on sales and performance seen in these reports we determine products, sub-segments, and promotions for the new quarter. The only problem is that once we have some conclusions, we then ask the predictive analytics team to run some scenarios for us. That is complicated because they have to update their data sets and then run a few different simulations around some decisions we make. Most of the time we have to change one or more variables and re-run simulations. There are about 6-10 people involved in this exercise. Sometimes we get to the specific answer that we all have agreement on, but it requires a lot of interactions between the different teams.

Sometimes, because of the complexity of all these steps, we rely on our experience to come up with the plan more than any prescriptive analytics!

We claim to be data driven, but it’s a little complicated sometimes. So we ‘kind-of-use reports’, but really we are using our own business acumen to make the final call.

The problem with the above analytics continuum for this marketing analyst is that it is setup for failure. The core reason why people spend time on analytics is not to have intimate access to the latest reports! While knowing what just happened is always important and useful, in reality, it tells you how well things happened, or did not, it gives you an overview of financial performance in the past. However, what it often fails to comprehensively do is to focus on what you should do tomorrow. A lot of companies are spending a lot of time on descriptive analytics, not on the rest of the continuum. If the core goal of looking at analytics is to plan for the future, then this is where people should be focused! If the most important process is to come up with the plan for the future, next month, quarter or year, the prescriptive analytics part should happen first. In fact, the entire continuum should be inverted – and perhaps collapsed into one step.



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How companies are using big data and analytics

How companies are using big data and analyticsFew dispute that organizations have more data than ever at their disposal. But actually deriving meaningful insights from that data—and converting knowledge into action—is easier said than done. We spoke with six senior leaders from major organizations and asked them about the challenges and opportunities involved in adopting advanced analytics: Murli Buluswar, chief science officer at AIG; Vince Campisi, chief information officer at GE Software; Ash Gupta, chief risk officer at American Express; Zoher Karu, vice president of global customer optimization and data at eBay; Victor Nilson, senior vice president of big data at AT&T; and Ruben Sigala, chief analytics officer at Caesars Entertainment. An edited transcript of their comments follows.

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Planning Makes for a Successful Big Data Implementation

Planning Makes for a Successful Big Data Implementation CIOs have mastered the ability to capture and store data but how that data is used depends on how it is analyzed. The structures and processes that CIOs put in place to extract value from their big data efforts can determine their eventual success. We asked Rocana CEO Omer Trajman to elaborate on his experience implementing big data initiatives.

Trajman explains, “In our experience, the most successful analytics projects are ones backed by a solid business case that clearly identifies the business need for the project, how success will be measured, and the people, skills, and technology required to actually make the project a success. A business case enables the CIO to get the input and support of all key IT and LOB stakeholders in advance. It is this support that maximizes the likelihood of success of the analytics project.

“Often IT teams without a business case will attempt an analytics project on their own as an “under the radar” side project. These projects are almost always doomed to fail because they aren’t sufficiently staffed, and the technology used (typically free, unsupported open source components) takes much more effort to build into a production ready solution than anybody realizes. In contrast, an approved business case enables a CIO to get the resources he or she needs to properly staff the project. Often this includes acquiring an out-of-the box, fully supported data capture and analytics solution in order to dramatically accelerate the project.

“Therefore, what we would recommend to CIOs is that while any analytics project will require operational changes, the most important operational change they should focus on (if they aren’t doing so already) is to build and solicit support for a solid business case for their analytics projects, and have a plan to integrate and run the project as part of day to day operations.

“The primary rule with analytics is that the more data you have, the better your analytic output will be. And while we agree that CIOs have mastered the capture and store of structured, transactional data, the same cannot be said of the semi-structured and unstructured machine-generated data (e.g. metrics, logs, NetFlow). Every Fortune 500 organization is collecting only a fraction of the operational event data they really want and need, due to a combination of scalability and cost issues that prevent doing so. Our ABCs of starting and managing a data analytics effort based on our experiences with Fortune 500 customers is:

A: Acquire purpose-built analytics (marketing analytics, sales analytics, log analytics), don’t try to build a team of data scientists for each area

B: Be ready for the operational changes to people, processes, and technology required to make the analytics program a success

C: Collect as much data as you can and plan for explosive data growth– as much as 100x the amount of data ingested just over the next few years

MIT’s answer to global health issues: Democratizing big data analytics

MIT's answer to global health issues: Democratizing big data analytics Discover how medical professionals and MIT researchers are using data-enabled systems to help doctors around the world make the best healthcare decisions.

“While wonderful new medical discoveries and innovations are in the news every day, doctors struggle with using information and techniques available right now,” writes Leo Anthony Celi, assistant professor of medicine, Harvard Medical School, in the Conversation commentary Improving patient care by bridging the divide between doctors and data scientists. “As a practicing doctor, I deal with uncertainties and unanswered clinical questions all the time.”

Enter big data

Celi feels there are opportunities for big data and information analytics in the healthcare field. “A digital system would collect and store as much clinical data as possible from as many patients as possible,” writes Celi. “It could then use information from the past—such as blood pressure, blood sugar levels, heart rate, and other measurements of patients’ body functions—to guide future doctors to the best diagnosis and treatment of similar patients.”

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