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Have you ever been involved in executing an exploratory analysis based on an integrated clinical trial database? If so, you've probably experienced firsthand how elaborate the initial phase of data access and data processing can be.
Market analysts estimate the ratio for preparing the data, compared to actually analyzing the information, is often 80:20. So under normal circumstances, the vast majority of your time is spent with data preparation activities instead of gaining meaningful insights.
Historically, data preparation has been a classic IT task, requiring time consuming interactions between the business departments and IT. The good news is that agile BI has ushered in a new way of gaining access to your information. Modern analytical tools give statistical programmers and project statisticians the ability to prepare data on the fly, providing data insight quickly and without the complex process of engaging external departments, such as IT.
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Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the omni-channel. With machine learning, supercomputers learn from mining masses of big data without human intervention to provide unprecedented consumer demand insights.
Predictive analytics and advanced algorithms, such as neural networks, have emerged as the hottest (and sometimes controversial) topic among senior management teams. Neural network algorithms are self-correcting and powerful, but are difficult to replicate and explain using traditional multiple regression models.
For years, neural network models have been discarded due to the lack of storage and processing capabilities required to implement them. Now with cloud computing using supercomputers' neural network algorithms, along with ARIMAX, dynamic regression and unobserved components, models are becoming the catalyst for "machine learning-based forecasting." Read More
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Are you happy with the ROI on your analytics investments? Recently we’ve seen an upswing in organisations investing in analytical platform capabilities. One can assume the goal of these investments is to turn transactional data into a strategic asset. However, analytics alone will not do it. Unless your organisation is ready and able to invest significant resources in both acquiring and exploiting insights, you’ve potentially bought a very expensive analytical toolkit. It’s not until you use those insights for determining the next best step, identifying the next analysis question to ask, or making better decisions faster, that your analytics data can be considered a strategic asset.
Does this sound familiar?
“We’ve got the money to build the model, but not to … actually get it into production”
- Quote from a University of Wollongong research study.
A research team at the University of Wollongong in Australia, recently conducted an external study to determine whether or not those who have enhanced their analytical capabilities are executing on the insights discovered.
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Real world data collected in a functioning health care setting instead of a controlled clinical environment can provide opportunities for new and deeper insights across life science and health care organizations. However, managing, analyzing and extracting actionable information from the varied available sources can present unique challenges.
The sheer size of these observational and administrative sets of data alone can cause issues. Other factors include the variety of sources, data quality issues and the need to consider temporal relationships between the longitudinal data elements. Both human and computing platforms limit the available resources in many companies. Clearly, a lack of internal resources limits the number of studies that can be performed. To alleviate this issue, organizations can use an outsourcing model for research and analysis. However, this approach limits the ability to explore and analyze data freely. Beyond resource constraints, an appropriate computing environment can improve efficiency improvements for process standardization.
A number of regulatory issues also affect the collection and use of real world data. Of utmost importance is patient privacy. However, unique patient identifiers are critical for the most accurate analysis of treatments in the real world. Over time, the regulations on the collection, use, sharing and sale of real world data will continue to evolve. With these changes, life will become easier for researchers while others will introduce additional hurdles.
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Energy suppliers are fighting for prime position in the domestic energy supply market. Disillusioned customers, increased competition due to a flood of new entrants and tighter regulations are forcing suppliers to reassess their business models. According to UK regulator Ofgem, there were 3.8 million account switches in the first six months of the year. Today, the battleground is price. Tomorrow, it will be how energy suppliers can engage with younger consumers expecting a more personalized service. It’s not just a case of knowing their likes and dislikes, but predicting their future needs and preferences.
Engaging the data generation
What’s certain is that it’s going to be difficult to “wow” the next generation of energy users. These digital natives are highly selective about who they share their personal information with. Recent research we carried out with the Future Foundation into Gen Y (those aged 16-34 in the UK) shows that nearly half don’t feel comfortable sharing their data with energy suppliers. Only 18 percent trust their provider to find them the best deal available. Despite this weariness, the findings show that this emerging generation recognize that insights from the data they share can be used to enhance their lives and wider society.
And the energy sector is sitting on a data goldmine. Experts say the sector is set to experience substantial growth in the adoption of big data and internet of things (IoT) as the industry opts for intelligent assets, smart meters and appliances that drive business efficiency. Indeed, big data and the IoT will deliver a £15.6 billion boost to the UK energy sector over the next five years, according to research carried out by the Centre for Economics and Business Research. At present, 67 percent of energy companies surveyed have adopted big data analytics. By 2020, this number is expected to rise to 80 percent. This data explosion provides opportunities for electric, gas and water suppliers. Already, companies are embracing technologies such as data analytics, which will unearth customer insights and help suppliers drive a more personalized service. Read More
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While men still outnumber women in the analytics field, there are plenty of opportunities available for women.
At a recent Chief Data and Analytics forum, I was encouraged to see a well-balanced number of senior executives presenting about the business of analytics. Speakers included 12 women and 14 men, which indicates a growing diversity in analytics leadership. Even 20 years ago when I was studying statistics and computer science, I recall an obviously male dominated field. When I entered to the workforce, it was no different. Initially, I didn't consider the gender imbalance an issue, but over time I did notice a decline in young women entering the STEM disciplines and entering the workforce.
Women in analytics then and now
In more recent times, companies are striving for gender diversity. We're seeing more and more programs designed for women in data, such as workgroups, conferences and online communities to increase women into the field of data and analytics. Why wouldn’t you want to get on board this movement as is it considered the sexiest job of the 21st century? And since there is a shortage of skills in this space, the right skillset and approach can almost guarantee employment.
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With today’s customers able to access rich sources of product and service information – especially pricing data – when they are mobile, retailers are being forced to innovate in the way they capture consumers’ attention. Unfortunately, attention spans are rapidly decreasing to just the few seconds that a user spends on any one page before swiping or switching.
In the past, for many retailers it has been a race to the bottom on price or discounting in order to win and get the increasingly savvy shopper to convert and purchase with them.
Some retailers are finding ways to lock in customers without the product price being the only issue – recently illustrated by Amazon Prime’s extension of benefits and expansion into the grocery sector.
I was shocked at the number of Internet shopping-savvy friends that have told me a trick they discovered to get a discount on their likely purchases. They simply place items in the basket, give the retailer some basic details like email, and sit back and wait for the email or message with a discount code before completing their purchases. They even follow the process with multiple retailers to see who gives the best discount!
Meanwhile there is growing hype about how retailers need to develop more personalised shopping experiences, and deliver that direct one-to-one interaction with customers, no matter how they shop or browse.
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At SAS, we use terms like “machine learning,” “predictive modeling” and, of course, “analytics” quite a bit in our day-to-day business. Not surprising, given that we're the largest analytical software vendor out there. But have you noticed that these terms are popping up more frequently in news articles and blogs?
I have. Maybe because that’s the kind of stuff I end up reading, but I’m not so sure. I feel like these terms are popping up more and more; not only that, but when these terms are mentioned, it’s quite often in passing, or in a matter-of-fact way.
I like this. I like the fact that people talk about analytics now, and not only that, they're talking about sophisticated analytics. That’s great. Really great. It’s talked about in a way that makes it sounds easy and achievable. Which it is. At SAS, we've done a lot to advance the use of analytics and to make the development of analytical assets easier and easier. We've even made the deployment of those assets as easy as possible.
But, and you knew this was coming, there's a problem. It’s still not easy to get the kind of analytics that the trade magazines and we at SAS talk about into a production process. Yes, we as a software company have done pretty much everything we can do to make it easy, but there's still a problem.
It’s you. Read More
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Sometimes, a data swamp is exactly what you need.
Start with the end in mind -- wise words that apply to everything, and in the world of big data it means we have to change the way we look at managing the data we have.
There was a time when we managed data quality, and the main goal was to meet a metric that said data should be x% accurate. I’d argue that this is no longer relevant. Now, before I’m hunted down by all the data analysts out there, I’d like to clarify that I’m referring to managing data for data’s sake. Often we manage the value out of the data right when we need it most.
Does this mean I’m advocating that we cease performing any data quality work in the data stores holding that information? The answer is yes and no. That’s helpful isn’t it?
The answer became nebulous about the time new capabilities were created with the new big data architectures. Let me explain what I mean. Read More
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With the first debate between the two candidates behind us and the culmination of the US presidential election drawing near, who wouldn’t love to predict the winner? I don't have a crystal ball, but I do have the power of unstructured text analytics at my fingertips.
With the help of some good public data in the form of primary debate transcripts from the American Presidency Project and your input, I can tell you whether you’re likely to vote democratic or republican. Put my analytical powers to the test -- select your "hot button" issues from the two columns below:
|Focus on fighting ISIS
||Focus on ISIS in the context of wars in Iraq, Syria
|Second Amendment/Bill of Rights provisions for guns
|| State legislation for gun safety/control
|Illegal immigration, controlling borders
||Immigration reform, path to citizenship
|Negative sentiment towards trade deals
||Wall Street and big banks regulation
|Balancing the federal budget
||Affordable health care and insurance
||Taxes to pay for public colleges