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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.
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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.
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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.
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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?

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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.
Big data offers a host of benefits when utilized correctly.

Big data is no longer a new and untested concept in the business world. Now, these especially large datasets a recognized and respected tool for businesses in a wide variety of industries. It's not enough to simply collect and store important information, however. Businesses that want to realize the benefits of big data have must have the right business intelligence software in place, platforms that support the effective use of big data in a variety of contexts. Both considerations – enough data to fuel analytics efforts and the appropriate solutions for effective analysis – are vital. Without those two elements operating in tandem, any positive results will be muted.

"Business intelligence software is a vital element in a successful approach to big data."

Taking a successful approach to big data
There are a number of positions in which a business interested in big data might find itself. No matter if a business isn't yet a regular user of big data or is looking to make improvements to the way it stores, analyzes and derives benefits from it, there's one consideration that can't be avoided: accuracy. The validity of the data used to determine everything from internal finances to customer-facing product or project development is of the utmost importance. Lillian Taylor, director of predictive analytics and business intelligence consulting for Aviana, pointed out this need for accurate information – and to avoid data visualization efforts that draw on inaccurate data – in a recent blog post for LinkedIn Pulse.

Business intelligence software like IBM Cognos Analytics is a vital element in a successful approach to big data. While data visualization has a number of important applications, it's only ever as effective as the data used for its generation. When information isn't properly governed and different parts of an organization rely on exporting data and manipulating it to suit their needs, significant discrepancies arise. Those problems sabotage the ability of a business to develop consistent and accurate insight based on their collected information, one of the most significant benefits of using big data effectively.

Want to learn more about big data and the best, most productive ways to implement a big data solution in your organization? Aviana hosts an hour-long webinar titled "The What and Why of Datalakes: What You Need to Know Before You Start" on Feb. 23 at 11 a.m. PST. Sign up for this free learning experience by following this link.

Posted in Data Warehousing
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.
Pretty Data 1

I’m a strong proponent of data visualization. To truly convey the state of affairs, seeing *is* believing. If you’re familiar with Anscombe’s quartet it drives this point home; it’s an example that comprises four data sets that have nearly identical descriptive statistics, yet appear very different when graphed.

Pretty Data 2

I’ve had my lifetime’s worth of creating Excel pivot tables and corresponding graphs (at one point, many moons ago, I had the highest recorded score in Excel certification testing for AccountTemps). Data Visualization software is popular and widely used. However, while great for the individual analyst, it can wreak havoc in an organization. As one state official said to me, “We used to argue over spreadsheets and numbers, now we argue over pretty pictures” – and when you’re making strategic decision, the veracity of your data is critical.

I had another fortune 100 company that invested in a large contract for data visualization software. (Sadly, if they’d simply upgraded their BI software they would have had that functionality already – but not my fault, it was before I was assigned). Of the thousands of licenses they committed to, they only were able to release around five of them. They had recently changed accounting procedures, and the users kept reverting to old reports with the freedom of self-service. This is further complicated by the ability to export visualizations, manipulate the data, and re-visualize it. In larger organizations, this quickly becomes a grown-up version of “Telephone” – you know, that slumber party game where you whisper in the person’s ear to the right of you and at the end you see how much the original message had changed. With each export and reload, the data becomes more and more skewed. So when looking at data visualization, you need to think about how your organization will rely on that data. You’re making decisions, you need to be able to rely on it. Pretty pictures sells the story, but is the story the truth?

This where the battle begins to educate folks on analytics from a true Business Intelligence system versus “analytics” from a data visualization product. I’ve seen some vendors begin the journey to backward steps into both governance and predictive capabilities. However, the results seem to be clunky and slow. A true Business Intelligence system is key to solid analytics, reliable enough for executives to make strategic decisions. Data governance – Master Data Management, working from the same data sources – one version of the truth – will save your organization exponentially in the long run.

Pretty Data 4Cognos Analytics version 11 is a true business intelligence system that includes self-service and visualization. Leading the industry for years. (The 2016 Gartner Magic Quadrant didn’t evaluate the true stack due to release dates, I have the official long version if you need it). Quick video here: https://youtu.be/iCjUOd6s4r8

Couple that capability – solid enterprise business intelligence with the exploratory natural language querying and predictive capabilities of Watson Analytics, and WOW – now you’re cooking with gas. This tool is fantastic for exploring data for new discoveries and opportunities. The tool will also bring anomalies to your attention. Descriptive, Predictive, Prescriptive Analytics to drive your business. Here’s a quick video overview: https://youtu.be/u2It7b5j3uw

Pretty Data 11

There’s an option to start with a free trial, in the past I’ve seen some difficulty transferring that work to business account, so ping me before you throw too much work in there- plus I can get your better pricing than web list. Also it’s not as intuitive, or wizard driven as I would like – because, well, I’m lazy, so here’s a quick tutorial I recommend: https://bigdatauniversity.com/courses/introduction-watson-analytics/

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Here’s one thing some users won’t be happy about, but you can not export from Watson Analytics – and that’s a good thing I know. But think of it like seat belt and drunk driving laws. It reduces your individual freedom but protects the organization. Sorry, I know, I would have hated it too when I was an analyst.

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So plan ahead. The journey starts with descriptive – what’s happened. Originally these were queries on your system of record, or operational data bases. When those queries slowed the system, data warehouses were created, then the crazy proliferation of data marts, and then life got good when we could leverage unstructured data:

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Here’s where I got excited… when we could leverage existing systems, operate from the data sources, and reduce the data movement and points of failure:

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And, if you’re just now investing, IBM has offerings in each space, and leveraging and investing in the Open Source standards that keep your organization from being locked into proprietary platforms and corresponding talent resources. I’ve mentioned before there were rumors this past fall that a smaller Hadoop tech company ran into funding issues and customers were presented with renewal rates of up to 300%. Which means either biting the bullet or yet another rip and replace migration project getting in the way of the organizational goals.

Pretty Data 9

If you’re in highly secure industry not ready for cloud or hybrid, we also offer a data lake software stack, governed and secure, with a scripted installation that takes less than a day. If your interested in a Proof of Concept on that, I have a couple slots for a no-cost, no obligation, 2 week test drive. We do all the work.

Why Aviana?

Pretty Data 10Aviana 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
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.
Effective communication with customers is critical to retail success.

Traditionally, relationships between retail businesses and customers were influenced by a number of set factors. While shopper preferences played a significant role, especially in populous urban areas that had more shopping choices, there were practical limits for in-person shopping. In many cases, people simply couldn't order products from many states away unless the company in question had a major catalog business.

Now, that's no longer the case. The internet has broken down plenty of barriers in terms of shopping at stores near and far and consumers are no longer as limited to local retailers. To maintain relevance and connect with shoppers, retailers need to take a new approach. That's where predictive analytics and business intelligence software can help.

"With improved visibility into customer behaviors and preferences, retailers can act more confidently and effectively."

Increasing relevance and crafting desirable offers
One major obstacle to consider when it comes to building customer relationships is the relevance of communications. While it's better to make contact with customers than not, irrelevant communications can do almost as much damage. In an article for the IBM Big Data & Analytics Hub, writer Vivian Braun discussed how business-to-consumer communications that lack relevance can burn out customers and generally fail to create positive engagement. Communications and marketing efforts that don't connect with customers are, ultimately, a waste of time and effort

Retailers need to consider their available resources to boost effectiveness. Braun wrote that the vast majority of retailers have access to large amounts of customer data, ranging from demographic information to past purchases, payment details and social media interactions. The key is using that information effectively. That's where the power of predictive analytics comes in, automating many laborious processes and providing consistent, valuable insight into customers on a variety of levels. With improved visibility into customer behaviors and preferences, retailers can act more confidently and effectively.

Aviana's work with large retailers across the world means we have unique insight into how businesses can best utilize predictive analytics. Whether your company wants to improve ordering and stocking concerns, marketing and communications, the supply chain or a variety of other dimensions of operations, Aviana can help. To learn more, check out our dedicated retail industry page.

Posted in Business Intelligence
Mark Ishikawa

About Mark Ishikawa

Mark Ishikawa is a managing partner with Aviana managing all sales and marketing activities. Mark is a successful and seasoned sales and marketing executive with over 16 years of strategic solution selling, team building and executive positioning in the enterprise solution and consulting industry. Mark’s most recent position since joining Aviana was a Partner with Ineum Consulting where he was responsible for growing the business with aggressive revenue targets as well as customer acquisition of new business. He also worked to improve the firm’s people development and retention while being responsible for recruiting and development of all staff. Mark enjoys coaching youth sports, playing golf & traveling. Mark and his wife live in Orange County, California with their three boys.
Retailers need effective omnichannel strategies to remain competitive, and predictive analytics can help.

Omnichannel is a familiar term in the world of retailers, whether they're primarily based online or in brick-and-mortar stores. The appeal of an omnichannel strategy – one that includes bridges between different sales channels, offers a smooth experience when using any or all of them and promotes easy contact with customers – is clear.This approach fits the wants and needs of many modern shoppers and creates connections across what used to be disparate and sometimes disconnected elements of an organization. By using business intelligence software to gather valuable consumer data and develop effective marketing and communication strategies, businesses can move toward omnichannel success.

"It's critical that retailers develop connections with shoppers based on hard data and effective analytics."

Tapping into modern consumer paths
In an article for the IBM Big Data & Analytics Hub, author Valerie Moloney highlighted how quickly common customer behaviors have changed. While a frequent path to purchase in the 1980s – and up through part of the 1990s – was to choose items out of a catalog, that's no longer the case. The straight-line path of using just one method of interaction provided by a single retailer isn't extinct, but it's now rare. Modern shoppers frequently compare costs and product quality across multiple businesses, interacting with them on websites, through social media, inside physical stores and more. This more systematic approach to finding the right item at the right price means retailers have to adjust their strategies – not just to match current preferences, but to prepare for the future as well.

Gathering consumer data and leveraging it to sharpen, target and generally improve omnichannel shopping efforts is a necessary part of success in this endeavor. With so many channels in play for potential interactions – from emails and social media to retail websites and stores, not to mention the many devices used to interact with digital elements – it's critical that retailers develop connections with shoppers based on hard data and effective analytics.

Aviana's collaborative efforts with clients in the retail sphere have yielded success for a wide range of businesses. We have experience working with global organizations and taking the many elements of international operations into account. We help retailers with considerations ranging from demographic analysis to inventory efficiency and product bundling and promotion. To learn more about our work in this industry, including case studies with major retailers, visit our dedicated industry page.

Posted in Business Intelligence
Derek Hansen

About Derek Hansen

Derek Hansen is the director of the predictive analytics and financial performance management practices at Aviana. Mr. Hansen’s experience is complemented by his deep knowledge and expertise in IBM/Cognos TM1 and Enterprise Planning.
Manufacturers have to do more than just collect big data.

Manufacturing is an especially complicated process, with a multitude of data streams flowing out of nearly every step in the workflow. From designing new products and refining old ones to the supply chain information that comes along with distribution, manufacturers are frequently data rich. However, an abundance of information doesn't mean a company can automatically act on it or benefit from it – data can easily be generated and then remain invisible or hard to access.

This lack of visibility into operational information is a major argument for the need to put a data warehouse solution in place. With the right platform implemented, manufacturers don't just generate data, they can analyze it and receive the many associated benefits.

"Companies need to look beyond capturing data and pay more attention to utilization."

Getting the most out of big data
Broadly speaking, companies with greater insight into operations have the power to develop more effective processes and streamline workflows. The issues addressed by increasing access to internally generated information can span from specific concerns with minute but important differences, like manufacturing tolerances, to systemic change that alters the way an entire department or a majority of employees carry out their duties. 

An article from The IBM Big Data & Analytics Hub discussed the vital importance of using data, not just collecting it. A business that has the mechanisms in place to compile operational information but doesn't or can't analyze and act on it has only wasted time and resources developing the network.

Companies need to look beyond capturing operational data and pay more attention to utilization. That means selecting the best data warehousing services for an organization's needs, placing an emphasis on analysis and the improved decision-making capabilities of a company that is more fully informed about its short- and long-term operations. Everything from data management and governance needs to integration and self-service use have to be addressed. Manufacturers that can understand this requirement and work toward developing such a data ecosystem put themselves in a much better position than those that don't.

Aviana has the expertise to guide manufacturers through the process of implementing big data and predictive analytics solutions, ensuring they not only collect information but use it with purpose. Businesses that consistently have chances to change, grow and improve operations are ready for whatever comes their way. To learn more about our work with manufacturers, visit our dedicated industry page.

Posted in Data Warehousing
Francois Ross

About Francois Ross

François has been managing software sales and delivery of Business Analytics and Information Management solutions for more than 25 years. Prior to joining Aviana, his name has been predominant within the IBM Cognos analytics space. Leveraging a strong technical background, elite demonstration capabilities, and understanding of Big Data Analytics proven practices, he has brought thought leadership in the most active clients and organizations. His passion and mission statement: delivering the most impactful data to the best user experience through the art of possible.
Financial services providers can boost engagement with clients through predictive analytics.

One of the most powerful resources currently available to financial services providers is the large amount of data collected about clients. From their demographic information to financial history and past decisions made while working with your firm specifically, the information associated with individual customers is valuable. With the right streams of data and effective business intelligence software in place to analyze them, financial services providers can learn more about clients, develop projections for the future and make accurate predictions about which offerings will be most relevant and desirable.

"Positive changes to engagement can ultimately lead to more revenue from long-term clients."

Boosting client engagement through the power of predictive analytics
An e-book shared by the IBM Big Data & Analytics Hub highlighted both the potential benefits and challenges of using client data to realize a higher level of engagement. When applied effectively, positive changes to engagement ultimately lead to longer relationships and more revenue from long-term clients and create conditions where new customers can provide the same benefits as time passes.

These advantages are only possible when addressed appropriately, however. One major challenge to overcome is making sure the right solutions are used to analyze available data. Another is making a concentrated effort to seek out new streams of information to further fuel predictive analytics efforts. For example, financial services companies that can incorporate social media data into their efforts develop a more complete and nuanced picture of clients, which in turn means more accurate predictions and better returns. As the e-book said, this kind of approach is ultimately more holistic and taps into clients' goals as much as the banks'.

Understanding and anticipating major life events, both in general and specifically related to employment, income and financial decisions, means a greater comprehension of clients. With that sort of advantage – and a strong understanding of how predictive analytics provides staff with useful information and actionable insights – financial services companies can rise above their competition.

Aviana understands the intricacies of predictive analytics and big data in general and, more importantly for financial services providers, how to best apply them in that industry. Our proven track record of assisting organizations with implementing big data and predictive analytics solutions, using the power of IBM's Cognos software, means we have plenty to offer. To learn more, visit our dedicated landing page and check out our success stories for specific examples of how Aviana assists financial services providers.

Posted in Predictive Analytics
Derek Hansen

About Derek Hansen

Derek Hansen is the director of the predictive analytics and financial performance management practices at Aviana. Mr. Hansen’s experience is complemented by his deep knowledge and expertise in IBM/Cognos TM1 and Enterprise Planning.
Manufacturers can improve maintenance efforts with predictive analytics.

Manufacturing is a pillar of the worldwide economy. It's occupied such a position since the first industrial revolution, growing, developing and changing with advances in management, process and technology. Manufacturing's presence as one of the oldest components of the modern business world means it's highly competitive, too. International and global organizations are constantly looking for ways to boost productivity, streamline business activities and reduce wasted resources. Through their many applications in increasing informational visibility and generating useful analysis, predictive analytics and business intelligence software can give manufacturers such an edge.

Just one example: improving maintenance with predictive analytics
The maintenance of equipment, vehicles, facilities and other assets is central to a smooth process of both manufacturing and distributing products. There are numerous dimensions of efficiency that companies can explore in this space, from improving the application and results of preventative repair efforts to putting together more efficient maintenance schedules that maximize uptime and output.

"Predictive analytics and the Internet of Things can relay information on a scale larger than any seen in the past."

Manufacturing Business Technology magazine discussed how predictive analytics and the Internet of Things can relay information on a scale larger than any seen in the past. Organizations can become truly data rich in their decision-making processes, drawing on a wide range of information to ensure they follow the right course of action. When companies have many streams of data related to specific equipment and assets, along with the right business intelligence software to help them interpret the meaning behind all that information, they can make more purposeful, focused and productive decisions.

The key is to make sure the data collected is useful and actionable and the analysis that stems from it is given proper weight and consideration. Gathering reams of information without a clear plan for use or the right tools to understand it in context means it does little more than take up space. Manufacturers have to to consider all aspects of successful data gathering, use and analysis when developing such plans.

Aviana is uniquely equipped to help manufacturers implement effective solutions that provide valuable insight into operations. Improving maintenance efforts is one major concern, but organizations can draw on their data to improve a variety of other functions as well – from inventory and shipping to finance and productivity – with our help. To learn more about Aviana's work in the manufacturing industry, visit our dedicated industry page.

Posted in Business Intelligence
Ananta Mukerji

About Ananta Mukerji

Ananta Mukerji, a founding partner of Aviana Global Technologies, has 25 years of managerial, functional and technical experience in the IT industry, including many years as a Big-5 management consultant.