Will Businesses Like Facebook’s New Reaction Buttons?

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New Data Will Reveal How Customers Really Feel

If you recently updated your Facebook status, you may see a wide array of responses that goes beyond the fabled “like” riposte the social media platform has become known for. Last month Facebook unveiled “Reactions,” which offers its users five additional ways to express their opinions on everything from pictures of your cats to your brash political musings. While it will certainly give users more choices in how they interact with friends, it will also give businesses deeper insights into customer sentiment.

Reactions are essentially an extension of the like button, with six different buttons, or “animated emojis” (ask a millennial): “Like,” “Love,” “Haha,” “Wow,” “Sad” or “Angry.” Despite the public outcry for a “dislike” button, Facebook did not include one because the company felt it could be construed as negative. But that won’t stop people from using the “angry” button when they do not like something.

While this may upset a friend, it can actually help companies react and respond to complaints. What’s more, it may even help us predict trends and threats we may have not previously seen.

 

“I think it’s definitely possible to draw true insight from this, but you’ll need to do some very careful analytics before forming any meaningful conclusions.”

-Nipa Basu, Chief Analytics Officer, Dun & Bradstreet

 

Dun & Bradstreet’s Chief Analytics Officer, Nipa Basu, believes the new “Reactions” will be an opportunity for businesses to better understand their customers, but notes it will take a deep level of understanding to make perfect sense of it.

“I think it’s definitely possible to draw true insight from this, but you’ll need to do some very careful analytics before forming any meaningful conclusions,” explains Basu. “These ‘Reactions’ are typically based on real-time human emotion. Sometimes it will be fair. Sometimes it will be unfair. But if you have a large sample of a lot of people’s emotions reflected then you can begin to ask if that says something about the customer experience or the product or service from that business, and go from there.

“Then comes the second part. Does it matter? Looking deeper at what the comments suggest and how they correlate with the different types of ‘Reactions’ being received, a good analyst will be able to draw more accurate insights. Looking at both types of responses together will help understand what customers really felt.”

This is what Basu believes will make social sentiment analysis that much more effective. Not only will it open the door for brands to assess the success or relevance of their content, as well as measure customer satisfaction, it may paint a deeper picture about total risk and opportunity across industries that could benefit others.

“When you utilize a company like ours, where we already have our pulse on the health of global businesses and industries, and combine it with these social ‘Reactions,’ we can start to understand the correlation it has between business growth or degeneration,” said Basu. “Can looking at the amount of ‘angry’ comments predict the future health of a particular business or industry? Quite possibly. This is paving the way for even richer data that can drive even smarter analytics.”

The Chief Analytics Officer Takes Center Stage

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From coast to coast, the business world is lauding the emergence of the Chief Analytics Officer (CAO). That’s right, we said Chief ANALYTICS Officer. Perhaps you were thinking about that other C-level role that recently dominated the headlines – the Chief Data Officer? Nope, the CDO is so 2015. Despite it being called the hottest job of the 21st century, it seems a new contender has entered the fray, that of the CAO.

All joking aside, the role of CDO has certainly not lost any of its luster; it remains an important position within the enterprise, it’s just that the CAO has now become just as significant. While both roles will need to coexist side-by-side, they face similar challenges, many of which were common themes during two recent industry events. Just looking at the massive turnout and the passionate discussions coming out of the Chief Analytics Officer Forum in New York and the Chief Data & Analytics Officer Exchange in California, it is apparent that the CAO will play a pivotal role in utilizing the modern organization’s greatest asset – data.

IDC predicts that the market for big data technology and services will grow at a 27% clip annually through 2017 – about six times faster than the overall IT market. But that windfall is not just going towards ways to obtain more data, it’s about investing in the right strategies to help make sense of data – an increasingly common perspective. Therefore, everyone is trying to figure out how to scale their analytical approach and drive more value across the organization.

Data alone is no longer the focal point for businesses, not without analytics to accompany it. We’re seeing the term ‘data analytics’ popping up more frequently. In fact, it’s the number one investment priority for Chief Analytics Officers over the next 12-24 months according to a Chief Analytics Officer Forum survey. That means an increased investment in data analytics and predictive analytics software tools. Not surprisingly, with the increased investment planned around these initiatives, the ability for data analytics to meet expectations across the company is the number one thing keeping CAO’s up at night, according to the same study.

The lively discussions during the course of both events featured some of the industry’s smartest minds discussing common challenges and objectives. Here are some of the most prevalent topics that were discussed.

  • The Need for C-Level Support: Very similar to the challenge facing the CDO, the CAO will need to secure buy-in from the C-level to make a real impact. Many speakers at both events expressed similar frustrations with getting the C-level to provide them the budget and resources needed to do their jobs. A good approach to take shared during one session was to build a business case which clearly quantifies the business value analytics will drive against specific goals. If you can tie everything back to ROI, you will have the ears of the CEO.
  • Breaking Down Silos: Even if you have attained support from the C-level, it is critical to partner with cross-functional departments. Whether it’s sales, marketing, finance, etc., tying the business value that analytics can drive to their specific goals will help the work relationship. These teams need to feel they are being included in your work. This theme was evident in many sessions, with speakers giving examples how they partnered with their colleagues to influence their business strategy and turn insights into action. At the end of the day, analytics is only as good as the data you have, and you need to ensure you are leveraging all of it across the enterprise.
  • Becoming a Storyteller: It was widely acknowledged that 80-85 percent of business executives who claim to understand analytics actually don’t. Hence the need to be able to simplify the message is critical to your success. There was a lot of discussion around what encompasses being a better storyteller. Being able to stay on point, avoiding technical jargon, relying on words versus numbers, and clearly quantifying and measuring business value were agreed upon paths to help the analytics group clearly communicate with the C-level.
  • Building the Right Team: Of all the discussions during both events, this was one of the most prominent themes and one of the biggest challenges shared by attendees. Where do you find the right talent? Views ranged from outsourcing and offshoring strategies to partnering with universities to develop a combined curriculum for undergrads and graduate students.

Everyone agreed the right candidate should have 4 distinct skills:

  • Quant skills, i.e. math/stats qualification
  • Business acumen
  • Coding skills/visualization
  • Communication and consulting skills

Since it is very difficult to find all four skills in a single person, the consensus was the perfect analytics team should consist of 3 tiers of analytics resources that should be thought through when building a team:

  • Statisticians, modelers and PhDs
  • Computer scientists
  • Measurement and reporting

From talent to strategy, the past two analytics-focused events underline the importance of employing a CAO in the enterprise. As data and analytics continue to be the core drivers of business growth, the CAO will not only need a prominent seat at the table, they will need the freedom and resources to help turn analytics into actionable insights for the entire enterprise.

Is Anticipatory Analytics the Path Toward Future Truth?

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New Whitepaper Explores the Arrival of Anticipatory Analytics

Most everyone is familiar with the image of the eccentric fortune-teller gazing into her crystal ball to boldly predict the future. In the business world, teams of analytic experts are doing this everyday; they’re just using data instead of a crystal ball to get a glimpse into the future.

Thanks to advanced analytics, organizations are able to understand potential outcomes and evaluate how issues can be addressed. By generating predictive models based on all the data being captured, a new level of transparency and foresight has been created that helps shape future business strategy based on historical trends. This is called predicative analytics, and it is “the fastest growing segment of the business intelligence (BI) and analytics software market,” according to Information Management.

But for all of the promise around predictive analytics, there is some criticism.  For instance, since environments and people are always changing, relying on historical trends is said to be too simplistic and sterile to say something will or will not happen with a great degree of certainty. But a new analytic approach has emerged that may be better at grasping future outcomes.

As technology has evolved, so has our ability to process data at an incredible rate, making it possible to perform what has become known as anticipatory analytics. While still a relatively new concept, anticipatory analytics is gaining prevalence as a methodology.  It leapfrogs predictive analytics in that it enables companies to forecast future behaviors quicker than traditional predictive analytics by identifying change, acceleration and deceleration of market dynamics.

In order to make this possible, the right mixture of data, processing tools, technology and expertise plays a central role. The following developments play key roles in being able to address the future, today.

Key Enablers of Anticipatory Analytics

4 trends are making anticipatory analytics a reality.

To gain a deeper understanding of the emergence of anticipatory analytics, and how it should be utilized in your organization, check out this detailed guide that outlines the differences between anticipatory and predictive.

4 Wishes Data-Inspired Leaders Want This Holiday

4 Wishes Data-Inspired Leaders Want This Holiday | D&B

4 Wishes Data-Inspired Leaders Want This Holiday | D&B

 

What Every Data-Inspired Leader Wants This Holiday

With the holidays in full swing, everyone is busy making their lists and checking them twice. But while electronics and toys routinely top the wish lists for most, the data-inspired leaders of the world have some unique desires that can’t easily be purchased from your favorite store.

Whether you’ve been naughty (online hookup site for married couples was breached by hacking outfit, The Impact Team, and the personal details of 37M users were made public, leaving many men sleeping on the couch) or nice (Data Science for Social Good, a program at the University of Chicago that connects data scientists with governments, is working to predict when officers are at risk of misconduct, with the goal of preventing incidents before they happen), chief data officers, data scientists and all data stewards want better and safer ways to do their jobs.

Instead of playing Santa and asking them to sit on my lap and tell me what they want for the holidays, I figured I’d simply share some of the top things we’ve heard on data leaders’ wish lists this year.

1. A Better Way to Find Truth in Data

Mark Twain famously said, “There are three kinds of lies: lies, damned lies, and statistics.” One of the biggest problems we’re faced with every day is trying to make sense of the data we have. In a perfect world the answer to all of our questions would lie smack dab in the data itself, but that’s not the case. The premise that data can get us closer to that single version of the truth is harder to achieve than first thought. But it hasn’t stopped us from trying to form conclusions from the data that is presented. Sometimes we rush to conclusions in the face of mounting pressure from others who demand answers.

What we really need is a source of truth to compare it to, otherwise it is very hard to know what the truth actually is. Unfortunately, that is often an impossible goal – finding truth in a world of ambiguity is not as simple as looking up a word in the dictionary. If you think about Malaysia Airlines Flight 370, which tragically disappeared in 2014, there were several conflicting reports claiming to show where the downed airline would be found. Those reports were based on various data sets which essentially led to multiple versions of proposed “truth.” Until they finally found pieces of the wreckage, searchers were looking in multiple disconnected spots because that was what the “data” said. But without anything to compare it to, there was no way to know what was true or not. This is just one example how data can be used to get an answer we wall want. This same thing happens in business everyday, so the takeaway here is that we need to stop rushing to form conclusions and try to first understand the character, quality and shortcomings of data and what can be done with it. Good data scientists are data skeptics and want better ways to measure the truthfulness of data. They want a “veracity-meter” if you will, a better method to help overcome the uncertainty and doubt often found in data.

2. A Method for Applying Structure to Unstructured Data

Unstructured data – information that is not organized in a pre-defined manner, is growing significantly, outpacing structured data. Experts generally agree that 80-85% of data is unstructured. As the amount of unstructured data continues to grow, so does complexity and cost of attempting to discover, curate and make sense out of this data. However, there are benefits when it is managed right.

This explosion of data is providing organizations with insights they were previously not privy to, nor that they can fully understand. When faced with looking at data signals from numerous sources, the first inclination is to break out the parts that are understood. This is often referred to as entity extraction. Understanding those entities is a first step to drawing meaning, but the unstructured data can sometimes inform new insights that were not previously seen through the structured data, so additional skills are needed.

For example, social media yields untapped opportunities to derive new insights. Social media channels that offer user ratings and narrative offer a treasure trove of intelligence, if you can figure out how to make sense of it all. At Dun & Bradstreet, we are building capabilities that give us some insight into the hidden meaning in unstructured text. Customer reviews provide new details on the satisfactory of a business that may not previously be seen in structured data. By understanding how to correlate negative and positive comments as well as ratings, we hope to inform future decisions about total risk and total opportunity.

With unstructured data steadily becoming part of the equation, data leaders need to find a better way to organize the unorganized without relying on the traditional methods we have used in the past, because they won’t work on all of the data. A better process or system that could manage much or all of our unstructured data is certainly at the top of the data wish list.

3. A Global Way to Share Insights

Many countries around the world are considering legislation to ensure certain types of data stay within their borders. They do this out of security concerns, which are certainly understandable. They’re worried about cyber-terrorism and spying and simply want to maintain their sovereignty. Not surprisingly, it’s getting harder and harder to know what you may permissibly do in the global arena. We must be careful not to create “silos” of information that undermine the advancement of our ability to use information while carefully controlling the behaviors that are undesirable.

There’s a method in the scientific community that when you make a discovery, you publish your results in a peer-reviewed journal for the world to see. It’s a way to share knowledge to benefit the greater good. Of course not all knowledge is shared that way. Some of it is proprietary. Data falls into that area of knowledge that is commonly not shared. But data can be very valuable to others and should be shared appropriately.

That concept of publishing data is still confusing and often debated. Open data is one example, but there are many more nuanced approaches. Sharing data globally requires a tremendous amount of advise-and-consent to do this in a permissible way. The countries of the world have to mature in allowing the permissible use of data across borders in ways that do not undermine our concerns around malfeasance, but also don’t undermine the human race’s ability to move forward in using this tremendous asset that it’s creating.

4. Breeding a Generation of Analytical Thinkers

If we are going to create a better world through the power of data, we have to ensure our successors can pick up where we leave off and do things we never thought possible. As data continues to grow at an incredible rate, we’ll be faced with complex problems we can’t even conceive right now, and we’ll need the best and brightest to tackle these new challenges. For that to happen, we must first teach the next generation of data leaders how to be analytically savvy with data, especially new types of data that have never been seen before. Research firm McKinsey has predicted that by 2018, the U.S. alone may face a 50% to 60% gap between supply and demand of deep analytic talent.

Today we teach our future leaders the basics of understanding statistics. For example, we teach them regression, which is based on longitudinal data sets. Those are certainly valuable skills, but it’s not teaching them how to be analytically savvy with new types of data. Being able to look at data and tell a story takes years of training; training that is just not happening at the scale we need.

High on the wish list for all data stewards – and really organizations across the globe, whether they realize it or not – is for our educational institutions to teach students to be analytical thinkers, which means becoming proficient with methods of discovering, comparing, contrasting, evaluating and synthesizing information. This type of thinking helps budding data users see information in many different dimensions, from multiple angles. These skills are instrumental in breeding the next generation of data stewards.

Does this reflect your own data wish list? I hope many of these will come true for us in 2016 and beyond. Until then, wishing you the very best for the holiday season…

‘Neuralytics’ Aims to Turn Big Data into Bigger Sales

Mike SeyfangLast month, Utah-based InsideSales.com raised a whopping $60 million round of venture capital from some marquee names in the tech world.

These investors were intrigued by the concept that lies at the heart of InsideSales’ technology: Neuralytics. The term is relatively new – and it’s registered. Neuralytics combines elements of artificial neural networks – the idea that computer systems can learn, as biological systems do, from a variety of data inputs – and predictive analytics. InsideSales is using Big Data, machine learning and artificial intelligence to discover meaningful patterns in data, with a goal to reveal what customers are likely to want or need next.

And really, that kind of predictive information is where this is all ultimately headed. Yes, it’s great to understand what has happened, and what has worked, and what hasn’t. But what everyone craves is insight into the future. Big Data may never exactly become a crystal ball, but it needs to deliver a way for companies to understand what’s next if it’s truly going to fulfill its promise.

InsideSales’ executive team says the company is already delivering value to customers, with more to come.

“The power of data science is becoming a mission-critical part of every business,” said Dave Elkington, chief executive officer and founder of InsideSales.com, in a press release. “Neuralytics, our predictive analytics platform, is at the heart of this movement, providing measurable and immediate revenue impact for our customers. These deepened partnerships will accelerate our technology innovation and deliver significant benefits to our customers.”

The funding was led by Salesforce Ventures, and it included money from Microsoft. InsideSales had already raised more than $139 million and is now valued at $1.5 billion. Those kinds of numbers tell us one thing for sure: There are great expectations for machine learning and analytics technologies to deliver tremendous value to companies around the globe in the years to come. Got an opinion? Let us know about it.

Image credit: Mike Seyfang

Your Customer’s World: Using Predictive Analytics to Assess Business Risk

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[This post originally appeared on Hoover’s Bizmology blog on May 8, 2014.]

Congratulations! Your lumber business just scored a large contract to supply materials to an expanding hardware store chain in California. You’re looking at ramping up production and hiring a score of new employees. But how can you be sure that your new star customer will remain on its current growth trajectory?

Businesses are faced with similar decision-making challenges on a daily basis. Without the proper data tools, it is difficult to see beyond the immediate outlook and predict when a material-change event will occur. Fortunately, new analytical tools are arriving on the market to help companies more accurately assess the future risk or potential of a business partner.

For instance, D&B’s patent-pending new analytic capability, Material Change, enables customers to better identify whether a business is poised for expansion or headed for decline, reaching out 12 months, 18 months, and on into the future. This new tool is the most recent in D&B’s family of predictive analytic tools. The company has traditionally provided market-leading business scores, credit scores, and ratings. The predictive analytic tools build on the traditional scores and can help transform data into intelligence that also helps manage risk and identifies new opportunities. Predictive tools already available from D&B include Viability Rating, Total Loss Predictor, and Delinquency Predictor.

Material Change builds on D&B’s existing predictive analytic capabilities by adding anticipatory signals, such as a company’s payment behavior and financial obligations, to provide a long-range view of the firm’s risk profile. Where D&B’s existing tools allow businesses to move forward on deals with prospects, suppliers, and customers, Material Change gives customers the confidence to make future plans based on the predicted stability of those commercial relationships.

Advanced analytics like Material Change are designed to help customers anticipate a partner’s behavior and insulate against surprise developments.

For the lumber business looking at expansion, the insecurity of relying on a large contract relationship is mitigated and confident business decisions can be made. Taking on 20 new employees and adding a new production line won’t cause unnecessary worry that those operations may have to be shuttered a year or two down the line.

Predictive analytic tools on the market are also helping companies target key prospects, identify most valuable customers, and leverage successful products and marketing campaigns, according to EMC Corp.’s Bill Schmarzo. Modeling and forecasting tools allow businesses to answer futuristic questions such as: “Who will be my top customer next year?” and “What new product will be the top seller?”

Predictive analytics allow businesses to recognize patterns and correlations between customer behavior, store traffic, promotional activities, geography, and other elements that drive risk or profitability. Examples of successful data crunching initiatives range from Netflix’s predicted success of blockbuster TV show House of Cards to the Carilion Clinic’s identification of critical-risk heart patients.

D&B is currently testing the Material Change predictors to determine how the capability can best be ingested and used by customers.

Quality Data Drives Quality Business Decisions

Bizmology_BiesadaThis post originally appeared on Hoover’s Bizmology.com on April 16, 2014.

Garbage In, Garbage Out (or GIGO) is a phrase coined by computer scientists that means something produced from materials of low quality will also be of low quality. Conversely, quality input will result in quality output. Indeed, quality — specifically data quality — is a subject that occupies the mind of Paul Ballew, D&B’s Chief Economist, and Chief Data and Analytics Officer. Ballew and his team are charged with arming D&B customers with the quality data and predictive analytics they need to make smart business decisions.

Ballew emphasizes data quality and why it’s so important to businesses in this interview, especially in The Age of Big Data. “Data quality is a multidimensional issue for today’s organizations. It equates to having and leveraging insights that can move your business forward,” says Ballew, adding, “The pace is defined by the quality of the insight.”

Good decisions rest on a firm foundation. And quality data is that foundation. The four dimensions of “quality data” are: completeness, accuracy, depth of insight, and data latency. “Decision makers today need that level of completeness, accuracy, depth of insight, and timeliness to make good decisions,” asserts Ballew.

If that sounds like a tall order, it is for most organizations. The business world is littered with the missed opportunities and casualties that result from poor data quality and lack of data management. (Case in point: Apparently, J.C. Penney’s ex-CEO Ron Johnson either failed to gather or consult data that revealed how wedded customers were to periodic sales events before changing the store’s pricing strategy and alienating shoppers in droves.) Successful companies rationalize data to make fact-based decisions and increasingly are using new sources of data, such as social media, sentiment data, and transactional data, all of which are valuable sources of insight that can be leveraged for competitive advantage. “The bar has really gone up,” Ballew says. “Twenty years ago, having any data was sufficient. Now we’re looking for high-quality data across all these dimensions.”

Adding to the complexity is the fact that data degenerates over time: Phone numbers change or are disconnected, CEOs are hired and fired, and bankruptcies happen. As data decays, businesses can find themselves struggling with data governance and stewardship. Proactive companies look to tools, including CRM systems, supply chain management, and business intelligence partners, to get the most from their data. Ballew notes that while these are all necessary tools, “What’s perhaps more important is having a good data governance structure.”

Creating a first-rate data governance structure is itself a multidimensional process. First, businesses must identify what they’re trying to accomplish. Then they need to bring all their data and analytic assets to bear in an environment that’s responsible for those activities. “Finally,” says Ballew, “it’s important to go beyond your data and analytics team. You should have your users be involved in the process, and that includes folks from marketing, finance, and IT.”

Creating and managing an integrated data program is a team effort, and a complex one at that. But the result can be summed up very simply: Base your decisions on quality data and they will be good decisions.