Blog

Improving risk awareness and assessment is a major consideration for the financial services industry.

Risks and opportunities are integral elements of operation for financial services companies. The ability to accurately predict, understand and analyze risk factors and potential opportunities gives companies in this industry an advantage over the competition and the base on which long-lasting client relationships can be built. Predictive analytics improves the overall ability of an organization to comb through many sources of data, identifying positive and negative trends and developing a plan of action to avoid or take advantage of them. Through business intelligence software, companies increase their insight into the market and a variety of other factors, from risk exposure to hedging strategy.

"Developing the best possible strategy and ensuring a high level of execution is critical for success."

The power and speed of automation
One of the central principles of predictive analytics is using advanced algorithms to seek out and present trends apparent in the many mounds of data that financial services companies regularly gather. Instead of relying on the work of individuals or entire departments to make these same determinations – and potentially introduce human errors to the process or miss connections – companies can rely on proven software like the IBM Cognos suite of products. With a software solution handling this intensive, time-consuming process, financial services companies can more efficiently allocate other resources and develop strategies that focus on further mitigation of risk.

An article in the Credit Union Times discussed the power of predictive analytics as an early warning system. CUT used the example of identifying risks related to employees misrepresenting investment opportunities to clients. With the right predictive analytics solution in place, businesses can develop more comprehensive insight into internal risks and take action to stop them before they turn into bigger problems. Predictive analytics can also help the financial services industry find concerns across markets and in other industries, revealing opportunities to avoid certain decisions and encourage clients to make others.

Developing the best possible strategy and ensuring a high level of execution in terms of predictive analytics is critical for success. Aviana understands how the industry can utilize predictive analytics in the most effective and efficient way through our experience helping a variety of financial services companies establish successful approaches and follow through on them. To learn more about our work in the realm of financial services, including past examples of our successful partnerships, check out our dedicated industry page.

Posted in Predictive Analytics
Miten Bhatt

About Miten Bhatt

Miten Bhatt is Client Director, Healthcare & Financial Systems at Aviana Global Technologies. Prior to coming to Aviana, he was a Sr. Project Manager at Niteo Partners (An NEC Company) where he was responsible for deploying financial consolidation applications and managing project deployments. He currently manages a delivery team delivering analytics projects for one of Aviana’s Fortune 500 healthcare industry clients. He has experience deploying financial applications and other enterprise systems across healthcare, financial services, manufacturing, distribution , retail and transportation industries across 3 continents. He is an avid badminton player and enjoys travel.
Cognitive analytics are an exciting prospect for the financial services industry.

Since its introduction to the business world, the advantages of predictive analytics have been clear. Not only do such systems, like the IBM Cognos suite of products, aid immensely in developing understanding of past and current situations, they also provide a data-based look at potential future scenarios. With this sort of perspective on past performance and future trends, financial services companies gain insight that is immensely valuable in terms of streamlining operations, assessing risk, and confidently making good decisions.

With the arrival of cognitive analytics, the benefits of these processes increase. The IBM Big Data & Analytics Hub highlighted the many diverse considerations financial services providers have to contend with in the modern market, from customer expectations and fulfillment to cyber security, risk management and compliance.

"Cognitive analytics provides broader, more comprehensive insight."

Cognitive analytics incorporates advanced computer learning processes, like data mining and pattern recognition, to create a simulation of human thought processes. In practical applications, this means more effective analysis that can tie together disparate areas of operation and highlight commonalities. In turn, broader, more comprehensive insight is provided. That gives financial services providers a better chance of connecting with a customer and maintaining successful interactions going forward, as well as better assessment of risk and the discovery of opportunities in the moment – instead of when they've already passed.

Aviana works with IBM to provide businesses in the financial services industry with solutions that address their unique needs and help them increase their competitiveness. With a history of working with businesses in this market, we have a powerful understanding of what's important for financial services organizations and can help implement the best possible solutions. To learn more about our work with companies like yours, visit our dedicated industry page.

Posted in Predictive Analytics
Miten Bhatt

About Miten Bhatt

Miten Bhatt is Client Director, Healthcare & Financial Systems at Aviana Global Technologies. Prior to coming to Aviana, he was a Sr. Project Manager at Niteo Partners (An NEC Company) where he was responsible for deploying financial consolidation applications and managing project deployments. He currently manages a delivery team delivering analytics projects for one of Aviana’s Fortune 500 healthcare industry clients. He has experience deploying financial applications and other enterprise systems across healthcare, financial services, manufacturing, distribution , retail and transportation industries across 3 continents. He is an avid badminton player and enjoys travel.
Big data gives financial services companies a leg up on the competition.

The financial services world is diverse and complicated, with businesses carving out areas of expertise among a wide range of options. Companies that have a strong grasp on the data behind market conditions, trends and their own decision-making have a major advantage over their competitors. One of the most rewarding methods is through big data. With the proper implementation and use of IBM data warehousing services, companies in the financial services industry can understand more about their internal operations and the market in which they operate. They also gain a deeper and broader understanding of their customers.

"One of the most rewarding methods of developing better understanding is through big data."

Gaining insights into customer behavior is a strong advantage in all industries, as IBM pointed out, but it has some especially useful applications in the world of financial services. Risk assessment is a core element of effective operations, and understanding important pieces of data as diverse as the potential for fraud and the credit-worthiness of individual clients allows single employees and the business as a whole to make the best decisions possible. When data can inform and support large decisions that affect the entire organization as well as optimize interactions with customers and avoid risk, financial services companies come out on top.

Aviana offers the financial services market not only access to and implementation of powerful IBM solutions, but also experience in the industry to guide our work. Interested to see how we partner with businesses to offer a host of improvements? Take a look at our success story of working with Stearns Lending, which drastically improved the value and distribution of reporting, among many other advantages. To learn more and get in touch, visit our dedicated financial services industry page.

Posted in Data Warehousing
Michele Harnish

About Michele Harnish

Michele Harnish has extensive experience in marketing and consulting to various industries. Her professional resume includes both corporate and field marketing executive roles. Her former role as Senior Director of Field Marketing at Cognos Corporation allowed her to leverage her strong corporate marketing background as she managed a large team of field marketing managers and business development managers and was solely responsible for lead generation across North America. In 2009, Michele joined Aviana Global and continues to leverage her unique approach to marketing which incorporates the branding and strategy of corporate marketing with the results driven lead generation power in field marketing. Michele enjoys spending time with her husband and four boys in Orange County, California.
Better healthcare outcomes are possible through predictive analytics.

Predictive analytics mean a lot of good news for patients when healthcare facilities implement and successfully use solutions involving these forecasting and modeling techniques.[This is structured weird. Maybe it would make more sense to say "With predictive analytics, so and so happens for patients."] With the right strategy in place, healthcare providers can gain deeper, more meaningful insight into the risk factors present in their patients. Drawing on data that's already stored digitally, business intelligence software like the IBM Cognos suite of applications can perform the type of deep data analysis that's cumbersome, difficult, error-prone and time-intensive when done manually. Doctors, nurses and other medical staff can then draw on this information to provide a higher level of care.

"Medical staff can draw on predictive analytics to provide a higher level of care."

Getting the most out of patient data
With so much information about patients now digitally stored, there are plenty of opportunities for predictive analytics to advance medical performance and supplement the knowledge and experience of staff. Hospitals & Health Networks highlighted the presence of this mostly unutilized store of data and discussed efforts to unlock its value and create better outcomes for patients.

Two major issues that may interfere with the successful implementation of predictive analytics in healthcare are traditions and heavy regulation of medicine as a business. Both of these elements provide ammunition for those who are against making major changes. They can also help create doubt in the minds of people genuinely unsure about the best course of action in terms of adopting new technology and trying something significantly different than what's come before.

However, neither of those concerns address the many positives that come along with predictive analytics. Additionally, they aren't tied to any drawbacks or negative consequences. Healthcare providers that effectively utilize predictive analytics can find more connections between patients and risk factors, gain assistance in troublesome diagnoses and much more without any drastic changes to the way they do their jobs.

Aviana is ready to help healthcare facilities take the next step forward and incorporate predictive analytics into their strategies for patient care on the micro and macro levels. With plenty of past implementations under our belts and experience working in the healthcare field, we're ready to help you and your patients provide the highest level of care possible. Visit our dedicated industry page to learn more

Predictive analytics can transform the way a retail business operates.

Like many other industries, the retail world has undergone a number of major changes thanks to the rise of big data and predictive analytics. With improved access to both data and the powerful insights that follow, merchants can improve, enhance and expand operations in ways that weren't nearly as practical just a decade ago.

Part of a successful strategy with predictive analytics is taking the right approach to implementing business intelligence software. Everything from gaining the approval of company leaders to making sure staff understand the analysis provided is critical to realizing the largest possible benefit.

"Predictive analytics can transform the way a merchant does business."

Making the best decisions about predictive analytics
Software like the IBM Cognos suite of solutions goes a long way toward keeping retailers informed about everything from current inventory and stocking levels to performance forecasts for the coming weeks, months and years. With so many merchants operating in a variety of different markets and regions, both digital and physical, complete visibility of a business is a critical element of success.

Big data and analytics specialist Bernard Marr said predictive analytics is providing retailers with more diverse and powerful advantages as its use continues to grow throughout the industry. With much of the early work of figuring out where analytics fits into the retail business model already completed, companies implementing this solution are in a stronger position.

For the most part, the hypothetical guide to using predictive analytics in general terms has already been written and is widely understood. It's no longer an issue of trying to integrate analytics into a specific element of operations or workflow. The major concern for retailers is determining where predictive analytics will provide the most benefit. Implementing a new solution into areas of high need first both proves the usefulness of the software and addresses the most pressing concerns at the earliest time possible. 

Aviana offers retailers a powerful combination when it comes to predictive analytics – experience and expertise working with a wide range of businesses in the industry. We understand the many potential benefits to be had by retailers and how to steer implementation projects so they're efficient and provide the most useful results. Predictive analytics can transform the way a merchant does business, from making specific changes to individual elements of operation to larger, broader improvements that stretch across an entire business. To learn more, visit our dedicated page for the retail industry. 

Posted in Business Intelligence
Lillian Taylor

About Lillian Taylor

Helping clients leverage Big Data, Cognitive, and Advanced Predictive Analytics to reach their maximum potential in the most cost effective way possible. Passionate about leveraging technology for the greater good! "Without data, you're just another person with an opinion" - W. Edwards Deming - Data Scientist.
Manufacturers are in the midst of another industrial revolution.

Manufacturing is an enduring and critical element of the larger economy, one that has remained so effective through its ability to change and adapt. Taking advantage of business trends that catch on and prove their worth, as well as researching and developing new, better processes for production and distribution, have helped many businesses maintain relevance and succeed. As more businesses come to understand the benefits of predictive analytics and incorporate them into operations, manufacturers have a unique opportunity to continue their development and take advantage of this new technology.

Taking part in the new industrial revolution
The IBM Big Data & Analytics Hub pointed out the current situation facing manufacturers across the globe: A fourth industrial revolution that's using concepts like advanced analytics and machine learning to achieve new objectives and create more efficient ways of doing business. Companies need to understand that failing to consider the power of these new tools and processes may mean declining performance and other negative consequences as compared to peers that embrace them. However, beyond the potential for falling behind by sticking to outmoded forms of analysis, production and distribution, the fourth industrial revolution offers a lot to be happy about.

Manufacturers can achieve leaner, more effective operations with predictive analytics.
Manufacturers can achieve leaner, more effective operations with predictive analytics.

One of the strongest advantages of predictive analytics is its ability to be harnessed for many different purposes. Business intelligence software, like IBM Cognos solutions, offers organizations the ability to select the options that make the most sense for their unique situations. Companies can address the most pressing issues facing them while also applying the power of predictive analytics to many other areas of operation.

As the IBM Big Data & Analytics Hub said, this major shift in business operations isn't necessarily the easiest thing to adapt to. There will be instances where companies have to change long-held rules, policies and workflows. The important thing to remember is the results – better insight into operations, more efficient production, improvement in the supply chain – are well worth it.

Aviana knows how to work with businesses in the manufacturing industry and develop solutions that tie into their needs and offer the most effective results. Manufacturers that want to benefit from the advantages of predictive analytics need to find the right partner, one that can offer relevance and proven results in the software and support they provide. To learn more about our work with manufacturers, visit our dedicated industry page

Posted in Business Intelligence
Michele Harnish

About Michele Harnish

Michele Harnish has extensive experience in marketing and consulting to various industries. Her professional resume includes both corporate and field marketing executive roles. Her former role as Senior Director of Field Marketing at Cognos Corporation allowed her to leverage her strong corporate marketing background as she managed a large team of field marketing managers and business development managers and was solely responsible for lead generation across North America. In 2009, Michele joined Aviana Global and continues to leverage her unique approach to marketing which incorporates the branding and strategy of corporate marketing with the results driven lead generation power in field marketing. Michele enjoys spending time with her husband and four boys in Orange County, California.
Consumer packaged goods companies are adopting the many benefits of predictive analytics.

Consumer packaged goods are a staple of the overall market, filling a number of roles in terms of convenience of use, easy access for purchase, shelf stability and many other consumer desires. The ubiquity of CPGs doesn't make the companies that produce or sell them immune to the pressure of change. The preferences of shoppers constantly shift, and the industry is moving through a period full of technological advancements. Solutions like business intelligence software and data warehousing services have a major impact on the operations of CPG companies, helping them stay in sync with changing consumer habits and take advantage of the benefits new technology has to offer.

"More effective product marketing is one of the most immediately visible advantages of predictive analytics."

Kroger buys into predictive analytics
Midwestern grocery giant Kroger recently merged its internal consumer insights division with an outside predictive analytics firm, Progressive Grocer pointed out. This move allows the grocer, which produces about 40 percent of its CPGs in house in addition to its retail operations, according to the Cincinnati Enquirer, to develop a clearer, more actionable picture of supply, demand and a host of other short- and long-term metrics. A single source of data for a wide variety of operational needs, from stocking to marketing and product development, was another major benefit of the deal.

One of the most practical benefits
The IBM Big Data & Analytics Hub highlighted one of the most practical advantages of of predictive analytics for companies connected to both production and end-user sales of CPGs: more effective product marketing. The article cited Ahold USA, a Northeast-based supermarket group, for its ability to utilize recommendation engines and suggest additional relevant products to consumers, an effort made possible through predictive analytics. The company tapped into the same sort of data and automated workflows that allow e-commerce retail giants like Amazon to offer eye-catching items and can now make recommendations in the present while stockpiling related data for future efforts.

Aviana knows what it takes for businesses in the CPG industry – from the businesses that manufacture them to the companies that sell them to individual consumers – to make the most of current predictive analytics solutions. Our experience in working specifically with CPG organizations means we understand the unique facets of this market and can help businesses develop useful, reliable, far-reaching predictive analytics solutions. To learn more about how predictive analytics and big data can help your business, visit our dedicated CPG industry page.

Posted in Business Intelligence
Michele Harnish

About Michele Harnish

Michele Harnish has extensive experience in marketing and consulting to various industries. Her professional resume includes both corporate and field marketing executive roles. Her former role as Senior Director of Field Marketing at Cognos Corporation allowed her to leverage her strong corporate marketing background as she managed a large team of field marketing managers and business development managers and was solely responsible for lead generation across North America. In 2009, Michele joined Aviana Global and continues to leverage her unique approach to marketing which incorporates the branding and strategy of corporate marketing with the results driven lead generation power in field marketing. Michele enjoys spending time with her husband and four boys in Orange County, California.
title image

Re: This article:http://www.forbes.com/sites/matthewherper/2017/02/19/md-anderson-benches-ibm-watson-in-setback-for-artificial-intelligence-in-medicine/#695216f33776

So I’ve had *several* people sending me this article, I am aware, and I know a few players involved and was holding off to comment until I met with them today.

A good partner that knows how to navigate internal IBM and is immune to pressure from product management or strategic objectives and can protect you from overruns or getting pulled into a less than pragmatic solutions.

Please do not confuse Watson the technology, Watson the available APIs, Watson Branded applications and the Watson Business Divisions in IBM. I’ll know more today about specifics, but I’m not entirely shocked. With large projects you will cross divisions and interact with several executives, many with strategic objectives that may not align entirely. If a customer does not have a clear vision of their goals (though creative exploration of new possibilities can net huge rewards), it’s very easy to run over and experience scope creep or end up up in softly baked solutions. Don’t let this scare you off. This technology is moving very fast, baby step in and make decisions that allow for flexibility.

Learn some tips for your Big Data and Cognitive journey on tomorrow’s webcast – Must Know Series on Big Data”: http://www.avianaglobal.com/blog/events/bigdatawebcast/

Posted in Big Data
Lillian Taylor

About Lillian Taylor

Helping clients leverage Big Data, Cognitive, and Advanced Predictive Analytics to reach their maximum potential in the most cost effective way possible. Passionate about leveraging technology for the greater good! "Without data, you're just another person with an opinion" - W. Edwards Deming - Data Scientist.
aviana-lillian

Gartner Magic Quadrants are out. As you can see, IBM didn’t get the best showing on BI, but killed it in Data Science.
In the evolution of Analytics the journey goes from descriptive (what happened), predictive (what’s likely to happen), to prescriptive (what are the best possible actions to take). BI is old tech, lots of established, competent players- hard to go wrong (except to mistake visualization for BI). Predictive has several options and debates about which ones (read here). Where IBM is killing it is in prescriptive – Cognitive with Watson and Decision Optimization with CPLEX.
aviana-lillian
Join us on Thursday!

Posted in Big Data, Business Intelligence
Lillian Taylor

About Lillian Taylor

Helping clients leverage Big Data, Cognitive, and Advanced Predictive Analytics to reach their maximum potential in the most cost effective way possible. Passionate about leveraging technology for the greater good! "Without data, you're just another person with an opinion" - W. Edwards Deming - Data Scientist.
feature-ibm

The Business Intelligence and Analytics journey begins in descriptive analytics. This is a well established discipline with many, many, competent players at every price point. They have the ability automate and streamline disseminating information about what happened. Then the human brain power and domain knowledge is typically leveraged for the *why* of it, and then consequently what actions to take because of it.

The next step in the journey is to leverage the descriptive of the past to determine what *will* happen. As Big Data technology accelerates rapidly, allowing the ingestion, storage, and analysis of large varied sets of data easier with cloud options and new tech, reducing the barrier to entry – MANY debates ensue, including:

  • Causation vs. Correlation
  • The sterile application of statistics over domain knowledge and real world feet-on-the street experience
  • The moral and legal issues surrounding what data can be leveraged (think profiling)

And then you get into long winded debates about what tools and resources should be leveraged:

  • Open source options in pure Data Science and increasingly more scarce and expensive resources to wield them. Then splintering off within that debates column are arugments about which models, regression techniques etc. are most appropriate. Usually done by folks well versed in data science but lacking in deep domain knowledge. (The movie Sully was an excellent illustration).
  • The investment in long established predictive analytics solutions (SPSS, SAS, etc)
  • The use of automated tools that choose models for the user and how accurate those may be – dependent on industry, if you’re not saving lives or in a high precision industry – they’re probably just fine. (Perhaps you caught today’s webinar on cognitive assistance in Data Science? https://www.ibm.com/analytics/us/en/events/machine-learning/)

Where the money to be saved / made is in the *prescriptive* side of the equation. For example, what if you could leverage volume discounts, net 2/10 discounts, and surplus purchases to reduce COGS? Or alternatively, just-in-time inventory management, seemingly counter intuitive mid-shift machine changes that increase output and reduce cost per piece. or perhaps the perfect timing of promotions to move products with reduced COGS at a higher margin? Hot day coming up? Maybe that bulk buy of cheap beer would net some extra profit?

What if you could run through 30,000 different scenarios between purchasing, manufacturing, shipping, employee hours, and promotions to eek out every bit of savings and efficiency – maximizing every investment, reimbursement, and discount available? Pre-technology this was done through trial and error, studies, pilots, gut decisions, diving rods, etc. What if you could use massive amounts of data to speed through and get to the optimal solution with very little investment?

ibm-2

This is where your data, and public data sets available can increase your bottom line, take advantages of bulk purchases or just-in-time by predicting demand, adjusting manufacturing schedules, and supply chain options. Can you 100% accurately predict without an occasional misstep – meh, unlikely. Though I will say with rudimentary tools, unsophisticated models, I personally was able to forecast $1 million per month customer residuals within about $10K. So I can’t imagine with the host of tools available today you’d be too far off (barring election forecasts, sorry Nate Silver that was a rough lesson in human reported metrics).

However, the gains to be made with better decision support being able to net positively even with some missteps – that’s pretty much a guarantee.

I will forewarn, when you take this journey you may find yourself steeped in opensource language holy wars and theoretical debates, anyone claiming to have the bullet proof system needs to “hang their pants over a telephone wire” because those suckers are on fire! They’re lying to you or themselves ….. but take some guidance, weigh the options, and think *pragmatic* progress over perfection. Look for flexibility to adjust and do not forget the costs of your talent resources to execute, sometimes hiring the super star data scientist may negate a chunk of savings available in a slightly less accurate forecast.

I’m always open to chat, brainstorm, offer what I do know, or play devil’s advocate on what you’ve been told, gratis.

Why Aviana?

Our story: https://vimeo.com/106965002

Aviana has documented over $1.8 billion in ROI for their clients. The overwhelming majority of Aviana clients are repeat clients because we keep working until our client is successful. We give our best advice and service regardless of profit.

Posted in Big Data, Predictive Analytics
Lillian Taylor

About Lillian Taylor

Helping clients leverage Big Data, Cognitive, and Advanced Predictive Analytics to reach their maximum potential in the most cost effective way possible. Passionate about leveraging technology for the greater good! "Without data, you're just another person with an opinion" - W. Edwards Deming - Data Scientist.